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Quantitative finance
S.I. Valuation
[ S.0a ]
Valuation foundations
[ S.0b ]
Linear pricing theory: core
[ S.0b.1 ]
s_current_values
[ S.0b.1.1 ]
Summary
[ S.0b.1.2 ]
Parameters
[ S.0b.1.3 ]
Outcomes and figures
[ S.0b.1.4 ]
Implementation
[ S.0b.1.5 ]
See also
[ S.0b.2 ]
Stochastic discount factor comparison
[ S.0b.3 ]
s_fund_theorem_mre
[ S.0b.3.1 ]
Summary
[ S.0b.3.2 ]
Data
[ S.0b.3.3 ]
Outcomes and figures
[ S.0b.3.4 ]
Implementation
[ S.0b.3.5 ]
See also
[ S.0b.4 ]
Fundamental theorem of asset pricing with forward numeraire
[ S.0b.5 ]
s_risk_neutral_density
[ S.0b.5.1 ]
Summary
[ S.0b.5.2 ]
Data
[ S.0b.5.3 ]
Parameters
[ S.0b.5.4 ]
Outcomes and figures
[ S.0b.5.5 ]
Implementation
[ S.0b.6 ]
s_security_market_line
[ S.0b.6.1 ]
Summary
[ S.0b.6.2 ]
Parameters
[ S.0b.6.3 ]
Outcomes and figures
[ S.0b.6.4 ]
Implementation
[ S.0b.6.5 ]
See also
[ S.0c ]
Linear pricing theory: further assumptions
[ S.0c.1 ]
s_simulate_call
[ S.0c.1.1 ]
Summary
[ S.0c.1.2 ]
Parameters
[ S.0c.1.3 ]
Outcomes and figures
[ S.0c.1.4 ]
Implementation
[ S.0c.1.5 ]
See also
[ S.0c.2 ]
s_intertemporal_valuation
[ S.0c.2.1 ]
Summary
[ S.0c.2.2 ]
Input
[ S.0c.2.3 ]
Outcomes and figures
[ S.0c.2.4 ]
Implementation
[ S.0c.2.5 ]
Tips
[ S.0c.2.6 ]
See also
[ S.0c.3 ]
s_intertemporal_valuation_complete
[ S.0c.3.1 ]
Summary
[ S.0c.3.2 ]
Input
[ S.0c.3.3 ]
Outcomes and figures
[ S.0c.3.4 ]
Implementation
[ S.0c.3.5 ]
See also
[ S.0c.4 ]
Minimum variance factor-replicating portfolio
[ S.0d ]
Non-linear pricing theory
[ S.0e ]
Valuation implementation
[ S.0e.1 ]
Fitting implied volatility from the Black-Scholes-Merton valuation
S.II. The “Checklist”
[ S.II ]
s_checklist_executive_summary
[ S.II ]
Summary
[ S.II ]
Data
[ S.II ]
Parameters
[ S.II ]
Outcomes and figures
[ S.II ]
Implementation
[ S.II ]
See also
[ S.1 ]
Risk drivers identification
[ S.1.1 ]
s_stock_short_horizon
[ S.1.1.1 ]
Summary
[ S.1.1.2 ]
Data
[ S.1.1.3 ]
Parameters
[ S.1.1.4 ]
Outcomes and figures
[ S.1.1.5 ]
Implementation
[ S.1.2 ]
s_stock_long_horizon
[ S.1.2.1 ]
Summary
[ S.1.2.2 ]
Data
[ S.1.2.3 ]
Parameters
[ S.1.2.4 ]
Outcomes and figures
[ S.1.2.5 ]
Implementation
[ S.1.2.6 ]
See also
[ S.1.3 ]
Zero-coupon bond rolling price
[ S.1.4 ]
s_yield_curve_evolution
[ S.1.4.1 ]
Summary
[ S.1.4.2 ]
Data
[ S.1.4.3 ]
Parameters
[ S.1.4.4 ]
Outcomes and figures
[ S.1.4.5 ]
Implementation
[ S.1.5 ]
s_analyze_rates_jgb
[ S.1.5.1 ]
Summary
[ S.1.5.2 ]
Data
[ S.1.5.3 ]
Parameters
[ S.1.5.4 ]
Outcomes and figures
[ S.1.5.5 ]
Implementation
[ S.1.5.6 ]
See also
[ S.1.6 ]
s_fit_yield_ns
[ S.1.6.1 ]
Summary
[ S.1.6.2 ]
Data
[ S.1.6.3 ]
Parameters
[ S.1.6.4 ]
Outcomes and figures
[ S.1.6.5 ]
Implementation
[ S.1.6.6 ]
See also
[ S.1.7 ]
Nelson-Siegel parametrization of the spread curve
[ S.1.8 ]
s_implied_volatility_surface
[ S.1.8.1 ]
Summary
[ S.1.8.2 ]
Data
[ S.1.8.3 ]
Parameters
[ S.1.8.4 ]
Outcomes and figures
[ S.1.8.5 ]
Implementation
[ S.1.8.6 ]
See also
[ S.1.9 ]
Inverse-call implied volatility
[ S.1.10 ]
Market quotes in delta-moneyness coordinates
[ S.1.11 ]
SVI parametrization of the implied volatility surface
[ S.1.12 ]
s_fx_rates_vs_logrates
[ S.1.12.1 ]
Summary
[ S.1.12.2 ]
Data
[ S.1.12.3 ]
Outcomes and figures
[ S.1.12.4 ]
Implementation
[ S.1.13 ]
s_rating_migrations
[ S.1.13.1 ]
Summary
[ S.1.13.2 ]
Data
[ S.1.13.3 ]
Parameters
[ S.1.13.4 ]
Outcomes and figures
[ S.1.13.5 ]
Implementation
[ S.1.13.6 ]
See also
[ S.1.14 ]
s_high_freq_stock_var
[ S.1.14.1 ]
Summary
[ S.1.14.2 ]
Data
[ S.1.14.3 ]
Parameters
[ S.1.14.4 ]
Outcomes and figures
[ S.1.14.5 ]
Implementation
[ S.1.14.6 ]
See also
[ S.1.15 ]
High frequency flow variables
[ S.1.16 ]
s_high_freq_tick_time
[ S.1.16.1 ]
Summary
[ S.1.16.2 ]
Data
[ S.1.16.3 ]
Parameters
[ S.1.16.4 ]
Outcomes and figures
[ S.1.16.5 ]
Implementation
[ S.1.16.6 ]
See also
[ S.1.17 ]
Volume time activity evolution of market microstructure
[ S.1.18 ]
Vasicek fit of the yield curve
[ S.1.19 ]
Heston parametrization of the implied volatility surface
[ S.2 ]
Quest for invariance
[ S.2.1 ]
s_elltest_compret
[ S.2.1.1 ]
Summary
[ S.2.1.2 ]
Data
[ S.2.1.3 ]
Parameters
[ S.2.1.4 ]
Outcomes and figures
[ S.2.1.5 ]
Implementation
[ S.2.1.6 ]
See also
[ S.2.2 ]
s_kolmsmirn_compret
[ S.2.2.1 ]
Summary
[ S.2.2.2 ]
Data
[ S.2.2.3 ]
Parameters
[ S.2.2.4 ]
Outcomes and figures
[ S.2.2.5 ]
Implementation
[ S.2.2.6 ]
See also
[ S.2.3 ]
Ellipsoid test for invariance on equity market variables
[ S.2.4 ]
Kolmogorov-Smirnov test for invariance on equity market variables
[ S.2.5 ]
Ellipsoid test for invariance on microprice increments
[ S.2.6 ]
Kolmogorov-Smirnov test for invariance on microprice increments
[ S.2.7 ]
s_elltest_ytm_dailychanges
[ S.2.7.1 ]
Summary
[ S.2.7.2 ]
Data
[ S.2.7.3 ]
Parameters
[ S.2.7.4 ]
Outcomes and figures
[ S.2.7.5 ]
Implementation
[ S.2.7.6 ]
See also
[ S.2.8 ]
s_kolmsmirn_ytm
[ S.2.8.1 ]
Summary
[ S.2.8.2 ]
Data
[ S.2.8.3 ]
Parameters
[ S.2.8.4 ]
Outcomes and figures
[ S.2.8.5 ]
Implementation
[ S.2.8.6 ]
See also
[ S.2.9 ]
s_elltest_ytm_ns
[ S.2.9.1 ]
Summary
[ S.2.9.2 ]
Data
[ S.2.9.3 ]
Parameters
[ S.2.9.4 ]
Outcomes and figures
[ S.2.9.5 ]
Implementation
[ S.2.9.6 ]
See also
[ S.2.10 ]
s_kolmsmirn_ytm_ns
[ S.2.10.1 ]
Summary
[ S.2.10.2 ]
Data
[ S.2.10.3 ]
Parameters
[ S.2.10.4 ]
Outcomes and figures
[ S.2.10.5 ]
Implementation
[ S.2.10.6 ]
See also
[ S.2.11 ]
Ellipsoid test for invariance on the increments of the SVI parameters
[ S.2.12 ]
Kolmogorov-Smirnov test for invariance on the increments of the SVI parameters
[ S.2.13 ]
Ellipsoid test for invariance in the derivatives market
[ S.2.14 ]
Kolmogorov-Smirnov test for invariance in the derivatives market
[ S.2.15 ]
Ellipsoid test for invariance on the waiting times in high frequency trading
[ S.2.16 ]
Kolmogorov-Smirnov test for invariance on the waiting times in high frequency trading
[ S.2.17 ]
Ellipsoid test for invariance on the increments of tick time in high frequency trading
[ S.2.18 ]
Kolmogorov-Smirnov test for invariance on the increments of tick time in high frequency trading
[ S.2.19 ]
Estimation of a quantile of a mixture I
[ S.2.20 ]
Flexible combinations models
[ S.2.21 ]
s_elltest_ytm_monthly
[ S.2.21.1 ]
Summary
[ S.2.21.2 ]
Data
[ S.2.21.3 ]
Parameters
[ S.2.21.4 ]
Outcomes and figures
[ S.2.21.5 ]
Implementation
[ S.2.21.6 ]
See also
[ S.2.22 ]
s_default_merton_model
[ S.2.22.1 ]
Summary
[ S.2.22.2 ]
Parameters
[ S.2.22.3 ]
Outcomes and figures
[ S.2.22.4 ]
Implementation
[ S.2.23 ]
s_fit_discrete_markov_chain
[ S.2.23.1 ]
Summary
[ S.2.23.2 ]
Data
[ S.2.23.3 ]
Parameters
[ S.2.23.4 ]
Outcomes and figures
[ S.2.23.5 ]
Implementation
[ S.2.23.6 ]
See also
[ S.2.24 ]
Markov chain model for bid-ask spread
[ S.2.25 ]
Long Memory in high frequency trading order sign
[ S.2.26 ]
Long Memory in high frequency trading order sign: fractional integration processes
[ S.2.27 ]
s_volatility_clustering_stock
[ S.2.27.1 ]
Summary
[ S.2.27.2 ]
Data
[ S.2.27.3 ]
Parameters
[ S.2.27.4 ]
Outcomes and figures
[ S.2.27.5 ]
Implementation
[ S.2.27.6 ]
See also
[ S.2.28 ]
s_elltest_garchres_stock
[ S.2.28.1 ]
Summary
[ S.2.28.2 ]
Data
[ S.2.28.3 ]
Parameters
[ S.2.28.4 ]
Outcomes and figures
[ S.2.28.5 ]
Implementation
[ S.2.28.6 ]
See also
[ S.2.29 ]
P&L realizations modeled by a GARCH(1,1): ellipsoid test for invariance on the residuals
[ S.2.30 ]
Ellipsoid test for invariance on the residuals of an ACD model fitted on arrival times
[ S.2.31 ]
Stochastic volatility and leverage effect
[ S.2.32 ]
Implied leverage effect
[ S.2.33 ]
s_fit_yields_var1
[ S.2.33.1 ]
Summary
[ S.2.33.2 ]
Data
[ S.2.33.3 ]
Parameters
[ S.2.33.4 ]
Outcomes and figures
[ S.2.33.5 ]
Implementation
[ S.2.33.6 ]
See also
[ S.2.34 ]
Multivariate quest for invariance
[ S.2.35 ]
s_fit_var1_implvol
[ S.2.35.1 ]
Summary
[ S.2.35.2 ]
Data
[ S.2.35.3 ]
Parameters
[ S.2.35.4 ]
Outcomes and figures
[ S.2.35.5 ]
Implementation
[ S.2.35.6 ]
See also
[ S.2.36 ]
s_fit_garch_stocks
[ S.3 ]
Estimation
[ S.3.1 ]
Standard deviation estimation with backward/forward smoothing
[ S.3.2 ]
s_state_crisp_fp
[ S.3.2.1 ]
Summary
[ S.3.2.2 ]
Data
[ S.3.2.3 ]
Parameters
[ S.3.2.4 ]
Outcomes and figures
[ S.3.2.5 ]
Implementation
[ S.3.2.6 ]
See also
[ S.3.3 ]
s_exp_decay_fp
[ S.3.3.1 ]
Summary
[ S.3.3.2 ]
Data
[ S.3.3.3 ]
Parameters
[ S.3.3.4 ]
Outcomes and figures
[ S.3.3.5 ]
Implementation
[ S.3.3.6 ]
See also
[ S.3.4 ]
s_smooth_kernel_fp
[ S.3.4.1 ]
Summary
[ S.3.4.2 ]
Data
[ S.3.4.3 ]
Parameters
[ S.3.4.4 ]
Outcomes and figures
[ S.3.4.5 ]
Implementation
[ S.3.4.6 ]
See also
[ S.3.5 ]
s_min_entropy_fp
[ S.3.5.1 ]
Summary
[ S.3.5.2 ]
Data
[ S.3.5.3 ]
Parameters
[ S.3.5.4 ]
Outcomes and figures
[ S.3.5.5 ]
Implementation
[ S.3.5.6 ]
See also
[ S.3.6 ]
s_ens_two_scenarios
[ S.3.6.1 ]
Summary
[ S.3.6.2 ]
Parameters
[ S.3.6.3 ]
Outcomes and figures
[ S.3.6.4 ]
Implementation
[ S.3.6.5 ]
See also
[ S.3.7 ]
s_ens_exp_decay
[ S.3.7.1 ]
Summary
[ S.3.7.2 ]
Parameters
[ S.3.7.3 ]
Outcomes and figures
[ S.3.7.4 ]
Implementation
[ S.3.7.5 ]
See also
[ S.3.8 ]
s_generalized_expon_entropy
[ S.3.8.1 ]
Summary
[ S.3.8.2 ]
Parameters
[ S.3.8.3 ]
Outcomes and figures
[ S.3.8.4 ]
Implementation
[ S.3.8.5 ]
See also
[ S.3.9 ]
s_glivenko_cantelli
[ S.3.9.1 ]
Summary
[ S.3.9.2 ]
Parameters
[ S.3.9.3 ]
Outcomes and figures
[ S.3.9.4 ]
Implementation
[ S.3.9.5 ]
See also
[ S.3.10 ]
s_glivenko_cantelli_hfp
[ S.3.10.1 ]
Summary
[ S.3.10.2 ]
Parameters
[ S.3.10.3 ]
Outcomes and figures
[ S.3.10.4 ]
Implementation
[ S.3.10.5 ]
See also
[ S.3.11 ]
HFP ellipsoid dependence on flexible probabilities
[ S.3.12 ]
HFP quantile dependence on flexible probabilities
[ S.3.13 ]
Quantile estimation: maximum likelihood vs non-parametric
[ S.3.14 ]
s_max_likelihood_consistency
[ S.3.14.1 ]
Script summary
[ S.3.14.2 ]
Parameters
[ S.3.14.3 ]
Outcomes and figures
[ S.3.14.4 ]
Implementation
[ S.3.14.5 ]
See also
[ S.3.15 ]
MLFP estimators for elliptical variables: numerical study on the Hessian matrix of Student t distribution
[ S.3.16 ]
MLFP estimators for the Student t distribution. Application
[ S.3.17 ]
s_mlfp_ellipsoid_convergence
[ S.3.17.1 ]
Summary
[ S.3.17.2 ]
Data
[ S.3.17.3 ]
Parameters
[ S.3.17.4 ]
Outcomes and figures
[ S.3.17.5 ]
Implementation
[ S.3.17.6 ]
See also
[ S.3.18 ]
MLFP quantile dependence on flexible probabilities in extreme value theory
[ S.3.19 ]
Quantile function tail approximation
[ S.3.20 ]
Non-robustness of sample mean and sample covariance
[ S.3.21 ]
Jackknife test on sample mean and covariance
[ S.3.22 ]
Sample mean and sample median breakdown point
[ S.3.23 ]
Minimum volume ellipsoid enclosing data
[ S.3.24 ]
Farthest outlier detection
[ S.3.25 ]
High breakdown point with flexible probabilities estimators
[ S.3.26 ]
Robustness comparison between HFP and HBFP-estimators
[ S.3.27 ]
Method of moments with flexible probabilities: reflected shifted lognormal case study
[ S.3.28 ]
Generalized method of moments with flexible probabilities: Poisson distribution
[ S.3.29 ]
P&L unconditional distribution
[ S.3.30 ]
MLFP estimation of unconditional distribution: properties
[ S.3.31 ]
MLFP estimation of unconditional distribution: skew-t fit
[ S.3.32 ]
Non-synchronous data
[ S.3.33 ]
Outlier detection with flexible probabilities
[ S.3.34 ]
Expectation-maximization with flexible probabilities for missing values. Application
[ S.3.35 ]
s_different_length_series
[ S.3.35.1 ]
Summary
[ S.3.35.2 ]
Data
[ S.3.35.3 ]
Parameters
[ S.3.35.4 ]
Outcomes and figures
[ S.3.35.5 ]
Implementation
[ S.3.35.6 ]
See also
[ S.3.36 ]
Proxies
[ S.3.37 ]
Fix non-synchroneity in HFP
[ S.3.38 ]
Flexible probabilities ensemble posterior and classical equivalent
[ S.3.39 ]
Hellinger distance and diversity indicator
[ S.3.40 ]
Ensemble FP: curse of dimensionality?
[ S.3.41 ]
Likelihood with flexible probabilities of GARCH(1,1)
[ S.3.42 ]
Alternative FP specifications: bootstrap
[ S.3.43 ]
Alternative FP specifications: Dirichlet distribution
[ S.3.44 ]
s_estimation_copmarg_calloption
[ S.3.44.1 ]
Script summary
[ S.3.44.2 ]
Data
[ S.3.44.3 ]
Parameters
[ S.3.44.4 ]
Outcomes and figures
[ S.3.44.5 ]
Implementation
[ S.3.44.6 ]
See also
[ S.3.45 ]
s_estimation_copmarg_ratings
[ S.3.45.1 ]
Script summary
[ S.3.45.2 ]
Data
[ S.3.45.3 ]
Parameters
[ S.3.45.4 ]
Outcomes and figures
[ S.3.45.5 ]
Implementation
[ S.3.45.6 ]
See also
[ S.3.46 ]
Copula-marginal estimation for invariants of different asset classes
[ S.3.47 ]
Standardization of invariants’ marginals into uniform realizations: case study
[ S.3.48 ]
Copula-marginal distribution
[ S.3.49 ]
s_dcc_fit
[ S.3.49.1 ]
Summary
[ S.3.49.2 ]
Data
[ S.3.49.3 ]
Parameters
[ S.3.49.4 ]
Outcomes and figures
[ S.3.49.5 ]
Implementation
[ S.3.49.6 ]
See also
[ S.3.50 ]
Covariance normalization
[ S.3.51 ]
s_ewm_statistics
[ S.3.51.1 ]
Script summary
[ S.3.51.2 ]
Data
[ S.3.51.3 ]
Parameters
[ S.3.51.4 ]
Outcomes and figures
[ S.3.51.5 ]
Implementation
[ S.3.51.6 ]
See also
[ S.3.52 ]
s_ewm_bf_statistics
[ S.3.52.1 ]
Script summary
[ S.3.52.2 ]
Data
[ S.3.52.3 ]
Parameters
[ S.3.52.4 ]
Outcomes and figures
[ S.3.52.5 ]
Implementation
[ S.3.52.6 ]
See also
[ S.3.53 ]
s_shrinkage_location
[ S.3.53.1 ]
Summary
[ S.3.53.2 ]
Data
[ S.3.53.3 ]
Parameters
[ S.3.53.4 ]
Outcomes and figures
[ S.3.53.5 ]
Implementation
[ S.3.54 ]
s_shrinkage_factor
[ S.3.54.1 ]
Summary
[ S.3.54.2 ]
Data
[ S.3.54.3 ]
Parameters
[ S.3.54.4 ]
Outcomes and figures.
[ S.3.54.5 ]
Implementation
[ S.3.54.6 ]
See also
[ S.3.55 ]
s_shrink_corr_clusters
[ S.3.55.1 ]
Summary
[ S.3.55.2 ]
Datas
[ S.3.55.3 ]
Outcomes and figures
[ S.3.55.4 ]
Implementation
[ S.3.56 ]
Homogeneous cluster shrinkage
[ S.3.57 ]
Graphical lasso estimation of the correlation and inverse correlation matrix
[ S.3.58 ]
Random matrix theory: Marchenko-Pastur limit
[ S.3.59 ]
Numerical integration of Marchenko-Pastur distribution
[ S.3.60 ]
s_spectrum_norm
[ S.3.60.1 ]
Summary
[ S.3.60.2 ]
Parameters
[ S.3.60.3 ]
Outcomes and figures
[ S.3.60.4 ]
Implementation
[ S.3.60.5 ]
See also
[ S.3.61 ]
Spectrum of the covariance matrix: exponential noise
[ S.3.62 ]
s_shrink_spectrum_filt
[ S.3.62.1 ]
Summary
[ S.3.62.2 ]
Data
[ S.3.62.3 ]
Parameters
[ S.3.62.4 ]
Outcomes and figures
[ S.3.62.5 ]
Implementation
[ S.3.62.6 ]
See also
[ S.3.63 ]
Sparse matrix transformation shrinkage
[ S.3.64 ]
Shrinkage estimator of location: implementation
[ S.3.65 ]
s_eigenvalue_dispersion
[ S.3.65.1 ]
Summary
[ S.3.65.2 ]
Parameters
[ S.3.65.3 ]
Outcomes and figures
[ S.3.65.4 ]
Implementation
[ S.3.65.5 ]
See also
[ S.3.66 ]
Shrinkage estimator of dispersion: implementation
[ S.3.67 ]
Random matrix theory: Wigner semicircle law
[ S.3.68 ]
Shrinkage estimators of location: assessment
[ S.3.69 ]
s_bayes_prior_niw
[ S.3.69.1 ]
Summary
[ S.3.69.2 ]
Parameters
[ S.3.69.3 ]
Outcomes and figures
[ S.3.69.4 ]
Implementation
[ S.3.70 ]
s_bayes_posterior_niw
[ S.3.70.1 ]
Summary
[ S.3.70.2 ]
Parameters
[ S.3.70.3 ]
Outcomes and figures
[ S.3.70.4 ]
Implementation
[ S.3.70.5 ]
See also
[ S.3.71 ]
s_bayesian_estimation
[ S.3.72 ]
Markov chain Monte Carlo: application of Metropolis-Hastings algorithm
[ S.3.73 ]
Agnostic prior on correlation
[ S.3.74 ]
s_sample_mean_covariance
[ S.3.74.1 ]
Summary
[ S.3.74.2 ]
Parameters
[ S.3.74.3 ]
Outcomes and figures
[ S.3.74.4 ]
Implementation
[ S.3.74.5 ]
See also
[ S.3.75 ]
Distribution of conditioning variable for an aggregate of clusters
[ S.4 ]
Projection
[ S.4.1 ]
Projection of historical with flexible probabilities distribution via FFT
[ S.4.2 ]
Projection of the reflected shifted lognormal distribution
[ S.4.3 ]
Sum of random variables via simulation: practice
[ S.4.4 ]
Empirical verification of the square-root rule
[ S.4.5 ]
Projection of higher-order standardized summary statistical features
[ S.4.6 ]
Projection of the GARCH process
[ S.4.7 ]
s_projection_var1_yields
[ S.4.7.1 ]
Summary
[ S.4.7.2 ]
Data
[ S.4.7.3 ]
Parameters
[ S.4.7.4 ]
Outcomes and figures
[ S.4.7.5 ]
Implementation
[ S.4.7.6 ]
See also
[ S.4.8 ]
s_projection_multiv_ratings
[ S.4.8.1 ]
Summary
[ S.4.8.2 ]
Data
[ S.4.8.3 ]
Outcomes and figures
[ S.4.8.4 ]
Implementation
[ S.4.8.5 ]
See also
[ S.4.9 ]
Projection of a MVOU process with non parametric invariants (historical with flexible probabilities distribution of the principal component)
[ S.4.10 ]
Multivariate GARCH: fit and projection. Practice
[ S.4.11 ]
Equity market: linear vs. compounded returns projection
[ S.4.12 ]
s_projection_stock_fx
[ S.4.12.1 ]
Summary
[ S.4.12.2 ]
Data
[ S.4.12.3 ]
Parameters
[ S.4.12.4 ]
Outcomes and figures
[ S.4.12.5 ]
Implementation
[ S.4.12.6 ]
See also
[ S.4.13 ]
s_projection_calloption
[ S.4.13.1 ]
Summary
[ S.4.13.2 ]
Data
[ S.4.13.3 ]
Parameters
[ S.4.13.4 ]
Outcomes and figures
[ S.4.13.5 ]
Implementation
[ S.4.13.6 ]
See also
[ S.4.14 ]
Projection of cluster aggregates: case study
[ S.4.15 ]
Projection of risk drivers whose invariants stem from marginal GARCH(1,1) fit and joint DCC fit: stocks case study
[ S.4.16 ]
s_projection_stock_hfp
[ S.4.16.1 ]
Summary
[ S.4.16.2 ]
Data
[ S.4.16.3 ]
Parameters
[ S.4.16.4 ]
Outcomes and figures
[ S.4.16.5 ]
Implementation
[ S.4.16.6 ]
See also
[ S.4.17 ]
s_projection_stock_bootstrap
[ S.4.17.1 ]
Summary
[ S.4.17.2 ]
Data
[ S.4.17.3 ]
Parameters
[ S.4.17.4 ]
Outcomes and figures
[ S.4.17.5 ]
Implementation
[ S.4.17.6 ]
See also
[ S.4.18 ]
Projection via historical approach: call option risk drivers
[ S.4.19 ]
Historical projection: bootstrap
[ S.4.20 ]
Projection via historical bootstrapping: case study
[ S.4.21 ]
Hybrid Monte Carlo-historical projection: case study
[ S.4.22 ]
Hybrid historical and Monte Carlo projection: modeling default via a one-factor normal copula
[ S.4.23 ]
Copula-marginal projection for risk drivers of different asset classes
[ S.4.24 ]
Risk propagation in the Heston process
[ S.5 ]
Pricing at the horizon
[ S.5.1 ]
s_pricing_stock_hfp
[ S.5.1.1 ]
Summary
[ S.5.1.2 ]
Data
[ S.5.1.3 ]
Outcomes and figures
[ S.5.1.4 ]
Implementation
[ S.5.1.5 ]
See also
[ S.5.2 ]
s_pricing_equity_pl
[ S.5.2.1 ]
Summary
[ S.5.2.2 ]
Data
[ S.5.2.3 ]
Outcomes and figures
[ S.5.2.4 ]
Implementation
[ S.5.3 ]
s_pricing_equity_fx
[ S.5.3.1 ]
Summary
[ S.5.3.2 ]
Data
[ S.5.3.3 ]
Outcomes and figures
[ S.5.3.4 ]
Implementation
[ S.5.4 ]
s_pricing_zero_coupon_bond
[ S.5.4.1 ]
Summary
[ S.5.4.2 ]
Data
[ S.5.4.3 ]
Parameters
[ S.5.4.4 ]
Outcomes and figures
[ S.5.4.5 ]
Implementation
[ S.5.4.6 ]
See also
[ S.5.5 ]
s_pricing_couponbond
[ S.5.5.1 ]
Summary
[ S.5.5.2 ]
Data
[ S.5.5.3 ]
Parameters
[ S.5.5.4 ]
Outcomes and figures
[ S.5.5.5 ]
Implementation
[ S.5.5.6 ]
See also
[ S.5.6 ]
Annualized carry return of a zero-coupon bond (practice)
[ S.5.7 ]
s_bond_carry
[ S.5.7.1 ]
Summary
[ S.5.7.2 ]
Data
[ S.5.7.3 ]
Parameters
[ S.5.7.4 ]
Outcomes and figures
[ S.5.7.5 ]
Implementation
[ S.5.7.6 ]
See also
[ S.5.8 ]
Vega carry of a variance swap
[ S.5.9 ]
Carry trade in currencies
[ S.5.10 ]
s_pricing_equity_taylor
[ S.5.10.1 ]
Summary
[ S.5.10.2 ]
Data
[ S.5.10.3 ]
Parameters
[ S.5.10.4 ]
Outcomes and figures
[ S.5.10.5 ]
Implementation
[ S.5.10.6 ]
See also
[ S.5.11 ]
s_pricing_calloption
[ S.5.11.1 ]
Summary
[ S.5.11.2 ]
Data
[ S.5.11.3 ]
Parameters
[ S.5.11.4 ]
Outcomes and figures
[ S.5.11.5 ]
Implementation
[ S.5.11.6 ]
See also
[ S.5.12 ]
s_pricing_couponbond_taylor
[ S.5.12.1 ]
Summary
[ S.5.12.2 ]
Data
[ S.5.12.3 ]
Parameters
[ S.5.12.4 ]
Outcomes and figures
[ S.5.12.5 ]
Implementation
[ S.5.12.6 ]
See also
[ S.5.13 ]
Call option P&L Taylor approximation
[ S.5.14 ]
s_pricing_zcb
[ S.5.14.1 ]
Summary
[ S.5.14.2 ]
Data
[ S.5.14.3 ]
Parameters
[ S.5.14.4 ]
Outcomes and figures
[ S.5.14.5 ]
Implementation
[ S.5.14.6 ]
See also
[ S.5.15 ]
Exact pricing of stocks under normality
[ S.5.16 ]
Decomposition of a coupon bond P&L
[ S.5.17 ]
Decomposition of a call option P&L
[ S.5.18 ]
Stock P&L: historical approach
[ S.5.19 ]
Coupon bond with credit risk
[ S.5.20 ]
Market and credit joint ex-ante P&L: scenario-based approach
[ S.5.21 ]
Equity P&L’s: historical approach
[ S.5.22 ]
Call option P&L: historical approach
[ S.6 ]
Aggregation
[ S.6.1 ]
s_aggregation_exante_perf
[ S.6.1.1 ]
Summary
[ S.6.1.2 ]
Data
[ S.6.1.3 ]
Parameters
[ S.6.1.4 ]
Outcomes and figures
[ S.6.1.5 ]
Implementation
[ S.6.2 ]
s_aggregation_scen_prob
[ S.6.2.1 ]
Summary
[ S.6.2.2 ]
Parameters
[ S.6.2.3 ]
Outcomes and figures
[ S.6.2.4 ]
Implementation
[ S.6.3 ]
s_aggregation_options_hfp
[ S.6.3.1 ]
Summary
[ S.6.3.2 ]
Data
[ S.6.3.3 ]
Parameters
[ S.6.3.4 ]
Outcomes and figures
[ S.6.3.5 ]
Implementation
[ S.6.3.6 ]
See also
[ S.6.4 ]
Ex-ante return distribution: scenario-probability distribution
[ S.6.5 ]
s_aggregation_quad
[ S.6.5.1 ]
Summary
[ S.6.5.2 ]
Data
[ S.6.5.3 ]
Parameters
[ S.6.5.4 ]
Outcomes and figures
[ S.6.5.5 ]
Implementation
[ S.6.5.6 ]
See also
[ S.6.6 ]
P&L of a large portfolio of stocks: historical approach
[ S.6.7 ]
s_aggregation_norm
[ S.6.7.1 ]
Summary
[ S.6.7.2 ]
Data
[ S.6.7.3 ]
Parameters
[ S.6.7.4 ]
Outcomes and figures
[ S.6.7.5 ]
Implementation
[ S.6.7.6 ]
See also
[ S.6.8 ]
s_aggregation_regcred
[ S.6.8.1 ]
Summary
[ S.6.8.2 ]
Data
[ S.6.8.3 ]
Parameters
[ S.6.8.4 ]
Outcomes and figures
[ S.6.8.5 ]
Implementation
[ S.6.8.6 ]
See also
[ S.6.9 ]
s_cop_marg_stresstest
[ S.6.9.1 ]
Script summary
[ S.6.9.2 ]
Parameters
[ S.6.9.3 ]
Outcomes and figures
[ S.6.9.4 ]
Implementation
[ S.6.9.5 ]
See also
[ S.7 ]
Ex-ante evaluation
[ S.7.1 ]
s_evaluation_certainty_equiv
[ S.7.1.1 ]
Summary
[ S.7.1.2 ]
Parameters
[ S.7.1.3 ]
Outcomes and figures
[ S.7.1.4 ]
Implementation
[ S.7.1.5 ]
See also
[ S.7.2 ]
s_evaluation_satis_scenprob
[ S.7.2.1 ]
Summary
[ S.7.2.2 ]
Data
[ S.7.2.3 ]
Parameters
[ S.7.2.4 ]
Outcomes
[ S.7.2.5 ]
Implementation
[ S.7.2.6 ]
See also
[ S.7.3 ]
s_evaluation_satis_norm
[ S.7.3.1 ]
Summary
[ S.7.3.2 ]
Data
[ S.7.3.3 ]
Parameters
[ S.7.3.4 ]
Outcomes
[ S.7.3.5 ]
Implementation
[ S.7.4 ]
s_evaluation_eco_cap
[ S.7.4.1 ]
Summary
[ S.7.4.2 ]
Data
[ S.7.4.3 ]
Parameters
[ S.7.4.4 ]
Outcomes
[ S.7.4.5 ]
Implementation
[ S.7.4.6 ]
See also
[ S.7.5 ]
s_evaluation_cornishfisher_stocks
[ S.7.5.1 ]
Summary
[ S.7.5.2 ]
Data
[ S.7.5.3 ]
Parameters
[ S.7.5.4 ]
Outcomes and figures
[ S.7.5.5 ]
Implementation
[ S.7.5.6 ]
See also
[ S.7.6 ]
The mean-lower partial moment trade-off is not co-monotonic additive: practice
[ S.8a ]
Ex-ante attribution: performance
[ S.8a.1 ]
s_attribution_scen_prob
[ S.8a.1.1 ]
Summary
[ S.8a.1.2 ]
Parameters
[ S.8a.1.3 ]
Outcomes and figures
[ S.8a.1.4 ]
Implementation
[ S.8a.1.5 ]
See also
[ S.8a.2 ]
s_attribution_norm
[ S.8a.2.1 ]
Summary
[ S.8a.2.2 ]
Data
[ S.8a.2.3 ]
Parameters
[ S.8a.2.4 ]
Outcomes and figures
[ S.8a.2.5 ]
Implementation
[ S.8a.2.6 ]
See also
[ S.8a.3 ]
s_attribution_hedging
[ S.8a.3.1 ]
Summary
[ S.8a.3.2 ]
Data
[ S.8a.3.3 ]
Parameters
[ S.8a.3.4 ]
Outcomes and figures
[ S.8a.3.5 ]
Implementation
[ S.8a.3.6 ]
See also
[ S.8b ]
Ex-ante attribution: risk
[ S.8b.1 ]
s_risk_attribution_scen_prob
[ S.8b.1.1 ]
Summary
[ S.8b.1.2 ]
Data
[ S.8b.1.3 ]
Parameters
[ S.8b.1.4 ]
Outcomes and figures
[ S.8b.1.5 ]
Implementation
[ S.8b.1.6 ]
See also
[ S.8b.2 ]
s_risk_attribution_norm
[ S.8b.2.1 ]
Summary
[ S.8b.2.2 ]
Data
[ S.8b.2.3 ]
Parameters
[ S.8b.2.4 ]
Outcomes and figures
[ S.8b.2.5 ]
Implementation
[ S.8b.3 ]
s_risk_attrib_torsion
[ S.8b.3.1 ]
Summary
[ S.8b.3.2 ]
Data
[ S.8b.3.3 ]
Parameters
[ S.8b.3.4 ]
Outcomes and figures
[ S.8b.3.5 ]
Implementation
[ S.8b.3.6 ]
See also
[ S.8b.4 ]
s_risk_attrib_variance
[ S.8b.4.1 ]
Summary
[ S.8b.4.2 ]
Data
[ S.8b.4.3 ]
Outcomes and figures
[ S.8b.4.4 ]
Implementation
[ S.8b.4.5 ]
See also
[ S.9a ]
Construction: portfolio optimization
[ S.9a.1 ]
Mean-variance efficient frontier: numerical implementation
[ S.9a.2 ]
Mean-variance robust efficient frontier: numerical implementation
[ S.9a.3 ]
s_stock_selection
[ S.9a.3.1 ]
Summary
[ S.9a.3.2 ]
Data
[ S.9a.3.3 ]
Parameters
[ S.9a.3.4 ]
Outcomes and figures
[ S.9a.3.5 ]
Implementation
[ S.9a.3.6 ]
See also
[ S.9a.4 ]
Portfolio optimization: S&P 500 stocks
[ S.9b ]
Construction: estimation and model risk
[ S.9c ]
Construction: cross-sectional strategies
[ S.9c.1 ]
Characteristic portfolio construction (size)
[ S.9c.2 ]
s_characteristic_port_rev
[ S.9c.2.1 ]
Summary
[ S.9c.2.2 ]
Data
[ S.9c.2.3 ]
Parameters
[ S.9c.2.4 ]
Outcomes and figures
[ S.9c.2.5 ]
Implementation
[ S.9c.2.6 ]
See also
[ S.9c.3 ]
s_flexible_characteristic_port_rev
[ S.9c.3.1 ]
Summary
[ S.9c.3.2 ]
Data
[ S.9c.3.3 ]
Parameters
[ S.9c.3.4 ]
Outcomes and figures
[ S.9c.3.5 ]
Implementation
[ S.9c.3.6 ]
See also
[ S.9c.4 ]
s_generalized_flam_toy
[ S.9c.4.1 ]
Summary
[ S.9c.4.2 ]
Input
[ S.9c.4.3 ]
Outcomes
[ S.9c.4.4 ]
Implementation
[ S.9c.4.5 ]
See also
[ S.9d ]
Construction: time series strategies
[ S.9d.1 ]
s_dynamic_port_strats
[ S.9d.1.1 ]
Summary
[ S.9d.1.2 ]
Parameters
[ S.9d.1.3 ]
Outcomes and figures
[ S.9d.1.4 ]
Implementation
[ S.9d.1.5 ]
See also
[ S.10 ]
Execution
[ S.10.1 ]
s_execution_exog_impact
[ S.10.1.1 ]
Summary
[ S.10.1.2 ]
Data
[ S.10.1.3 ]
Parameters
[ S.10.1.4 ]
Outcomes and figures
[ S.10.1.5 ]
Implementation
[ S.10.1.6 ]
See also
[ S.10.2 ]
Monotonicity of trading trajectories in the Almgren-Chriss model (full execution requirement)
[ S.10.3 ]
Non-monotone trading trajectories in the Almgren-Chriss model with null drift
[ S.10.4 ]
s_execution_trading_traject
[ S.10.4.1 ]
Summary
[ S.10.4.2 ]
Parameters
[ S.10.4.3 ]
Outcomes and figures
[ S.10.4.4 ]
Implementation
[ S.10.4.5 ]
See also
[ S.10.5 ]
s_execution_opt_satisfaction_quantile
[ S.10.5.1 ]
Summary
[ S.10.5.2 ]
Parameters
[ S.10.5.3 ]
Outcomes and figures
[ S.10.5.4 ]
Implementation
[ S.10.6 ]
Trading trajectories in the multidimensional Almgren-Chriss model
[ S.10.7 ]
Liquidation trajectory and trading rate in the transient impact model with power law decay kernel
[ S.10.8 ]
Liquidation trajectory and trading rate in the transient impact model with logarithmic decay kernel
[ S.10.9 ]
s_execution_child_order
[ S.10.9.1 ]
Summary
[ S.10.9.2 ]
Data
[ S.10.9.3 ]
Parameters
[ S.10.9.4 ]
Outcomes and figures
[ S.10.9.5 ]
Implementation
[ S.10.10 ]
s_execution_sell_algorithm
[ S.10.10.1 ]
Summary
[ S.10.10.2 ]
Parameters
[ S.10.10.3 ]
Outcomes and figures
[ S.10.10.4 ]
Implementation
[ S.10.11 ]
A buying algorithm
S.III. Performance analysis
[ S.11 ]
Performance attribution
S.IV. Financial toolbox
[ S.12 ]
Performance definitions
[ S.12.1 ]
Compounded return of a portfolio
[ S.13 ]
Signals
[ S.13.1 ]
s_cointegration_detection
[ S.13.1.1 ]
Summary
[ S.13.1.2 ]
Data
[ S.13.1.3 ]
Parameters
[ S.13.1.4 ]
Outcomes and figures
[ S.13.1.5 ]
Implementation
[ S.13.1.6 ]
See also
[ S.13.2 ]
s_cointegration_signal
[ S.13.2.1 ]
Summary
[ S.13.2.2 ]
Data
[ S.13.2.3 ]
Parameters
[ S.13.2.4 ]
Outcomes and figures
[ S.13.2.5 ]
Implementation
[ S.13.2.6 ]
See also
[ S.13.3 ]
s_trade_autocorr_signal
[ S.13.3.1 ]
Summary
[ S.13.3.2 ]
Data
[ S.13.3.3 ]
Parameters
[ S.13.3.4 ]
Outcomes and figures
[ S.13.3.5 ]
Implementation
[ S.13.3.6 ]
See also
[ S.13.4 ]
s_order_imbal_signal
[ S.13.4.1 ]
Summary
[ S.13.4.2 ]
Data
[ S.13.4.3 ]
Parameters
[ S.13.4.4 ]
Outcomes and figures
[ S.13.4.5 ]
Implementation
[ S.13.4.6 ]
See also
[ S.13.5 ]
s_price_pred_signal
[ S.13.5.1 ]
Summary
[ S.13.5.2 ]
Data
[ S.13.5.3 ]
Parameters
[ S.13.5.4 ]
Outcomes and figures
[ S.13.5.5 ]
Implementation
[ S.13.5.6 ]
See also
[ S.13.6 ]
s_volume_cluster_signal
[ S.13.6.1 ]
Summary
[ S.13.6.2 ]
Data
[ S.13.6.3 ]
Parameters
[ S.13.6.4 ]
Outcomes and figures
[ S.13.6.5 ]
Implementation
[ S.13.6.6 ]
See also
[ S.13.7 ]
s_momentum_signals
[ S.13.7.1 ]
Summary
[ S.13.7.2 ]
Data
[ S.13.7.3 ]
Parameters
[ S.13.7.4 ]
Outcomes and figures
[ S.13.7.5 ]
Implementation
[ S.13.7.6 ]
See also
[ S.13.8 ]
Signal filtering for systematic strategy (size)
[ S.14 ]
Black-Litterman
[ S.14.1 ]
s_bl_equilibrium_ret
[ S.14.1.1 ]
Summary
[ S.14.1.2 ]
Data
[ S.14.1.3 ]
Parameters
[ S.14.1.4 ]
Outcomes
[ S.14.1.5 ]
Implementation
[ S.14.1.6 ]
See also
[ S.14.2 ]
s_info_processing_comparison
[ S.14.2.1 ]
Summary
[ S.14.2.2 ]
Data
[ S.14.2.3 ]
Parameters
[ S.14.2.4 ]
Outcomes and figures
[ S.14.2.5 ]
Implementation
[ S.14.2.6 ]
See also
Data science
S.V. Mathematics
[ S.15 ]
Linear algebra primer
[ S.15.1 ]
s_vector_operations
[ S.15.1.1 ]
Summary
[ S.15.1.2 ]
Parameters
[ S.15.1.3 ]
Outcomes and figures
[ S.15.1.4 ]
Implementation
[ S.15.2 ]
s_spectral_theorem
[ S.15.2.1 ]
Summary
[ S.15.2.2 ]
Parameters
[ S.15.2.3 ]
Outcomes and figures
[ S.15.2.4 ]
Implementation
[ S.15.2.5 ]
See also
[ S.15.3 ]
s_spectral_decomposition_squarem
[ S.15.3.1 ]
Summary
[ S.15.3.2 ]
Parameters
[ S.15.3.3 ]
Outcomes and figures
[ S.15.3.4 ]
Implementation
[ S.16 ]
Calculus primer
[ S.16.1 ]
s_univariate_derivatives
[ S.16.1.1 ]
Summary
[ S.16.1.2 ]
Parameters
[ S.16.1.3 ]
Outcomes and figures
[ S.16.1.4 ]
Implementation
[ S.16.2 ]
s_multivariate_derivatives
[ S.16.2.1 ]
Summary
[ S.16.2.2 ]
Parameters
[ S.16.2.3 ]
Outcomes and figures
[ S.16.2.4 ]
Implementation
[ S.16.2.5 ]
See also
[ S.16.3 ]
s_riemann_integration_univariate
[ S.16.3.1 ]
Summary
[ S.16.3.2 ]
Parameters
[ S.16.3.3 ]
Outcomes and figures
[ S.16.3.4 ]
Implementation
[ S.16.4 ]
s_riemann_integration_multivariate
[ S.16.4.1 ]
Summary
[ S.16.4.2 ]
Parameters
[ S.16.4.3 ]
Outcomes and figures
[ S.16.4.4 ]
Implementation
[ S.16.5 ]
s_saddlepoint
[ S.16.5.1 ]
Summary
[ S.16.5.2 ]
Parameters
[ S.16.5.3 ]
Outcomes and figures
[ S.16.5.4 ]
Implementation
[ S.17 ]
Functional analysis primer
[ S.17.1 ]
s_display_dirac_delta
[ S.17.1.1 ]
Summary
[ S.17.1.2 ]
Input
[ S.17.1.3 ]
Outcomes and figures
[ S.17.1.4 ]
Implementation
[ S.17.1.5 ]
See also
[ S.18 ]
Optimization primer
[ S.18.1 ]
s_convex_programming
[ S.18.1.1 ]
Summary
[ S.18.1.2 ]
Implementation
[ S.18.1.3 ]
See also
[ S.18.2 ]
s_selection_toy
[ S.18.2.1 ]
Summary
[ S.18.2.2 ]
Parameters
[ S.18.2.3 ]
Outcomes and figures
[ S.18.2.4 ]
Implementation
[ S.18.2.5 ]
See also
[ S.18.3 ]
s_factors_selection
[ S.18.3.1 ]
Summary
[ S.18.3.2 ]
Parameters
[ S.18.3.3 ]
Outcomes and figures
[ S.18.3.4 ]
Implementation
[ S.18.3.5 ]
See also
S.VI. Statistics
[ S.19 ]
Distributions
[ S.19.1 ]
Conditional expectation between normal random variables
[ S.19.2 ]
Innovation: the normal case, implementation
[ S.19.3 ]
Innovation: the lognormal case, implementation
[ S.19.4 ]
s_bivariate_normal
[ S.19.4.1 ]
Summary
[ S.19.4.2 ]
Parameters
[ S.19.4.3 ]
Outcomes and figures
[ S.19.4.4 ]
Implementation
[ S.19.5 ]
s_uniform_inside_circle
[ S.19.5.1 ]
Summary
[ S.19.5.2 ]
Parameters
[ S.19.5.3 ]
Outcomes and figures
[ S.19.5.4 ]
Implementation
[ S.19.6 ]
s_elliptical_uniform_radial_rep
[ S.19.6.1 ]
Summary
[ S.19.6.2 ]
Parameters
[ S.19.6.3 ]
Outcomes and figures
[ S.19.6.4 ]
Implementation
[ S.19.7 ]
s_simulate_unif_in_ellipse
[ S.19.7.1 ]
Summary
[ S.19.7.2 ]
Parameters
[ S.19.7.3 ]
Outcomes and figures
[ S.19.7.4 ]
Implementation
[ S.19.7.5 ]
See also
[ S.19.8 ]
s_q_chi_grid
[ S.19.8.1 ]
Summary
[ S.19.8.2 ]
Parameters
[ S.19.8.3 ]
Outcomes and figures
[ S.19.8.4 ]
Implementation
[ S.19.8.5 ]
See also
[ S.19.9 ]
s_scen_prob_pdf
[ S.19.9.1 ]
Summary
[ S.19.9.2 ]
Parameters
[ S.19.9.3 ]
Outcomes and figures
[ S.19.9.4 ]
Implementation
[ S.19.9.5 ]
See also
[ S.19.10 ]
s_scen_prob_cdf_quantile
[ S.19.10.1 ]
Summary
[ S.19.10.2 ]
Input
[ S.19.10.3 ]
Outcomes
[ S.19.10.4 ]
Implementation
[ S.19.10.5 ]
See also
[ S.19.11 ]
s_normal_exponential_family
[ S.19.11.1 ]
Summary
[ S.19.11.2 ]
Parameters
[ S.19.11.3 ]
Outcomes
[ S.19.11.4 ]
Implementation
[ S.19.11.5 ]
See also
[ S.19.12 ]
s_saddle_point_vs_mcfp_quadn
[ S.19.12.1 ]
Summary
[ S.19.12.2 ]
Parameters
[ S.19.12.3 ]
Outcomes and figures
[ S.19.12.4 ]
Implementation
[ S.19.12.5 ]
See also
[ S.19.13 ]
s_bivariate_wishart
[ S.19.13.1 ]
Summary
[ S.19.13.2 ]
Input
[ S.19.13.3 ]
Outcomes and figures
[ S.19.13.4 ]
Implementation
[ S.19.13.5 ]
See also
[ S.19.14 ]
s_gaussian_mixture
[ S.19.14.1 ]
Summary
[ S.19.14.2 ]
Parameters
[ S.19.14.3 ]
Outcomes and figures
[ S.19.14.4 ]
Implementation
[ S.19.14.5 ]
See also
[ S.19.15 ]
s_discrete_partitioned_variable
[ S.20 ]
Copulas
[ S.20.1 ]
s_nlg_to_uniform
[ S.20.1.1 ]
Summary
[ S.20.1.2 ]
Parameters
[ S.20.1.3 ]
Outcomes and figures
[ S.20.1.4 ]
Implementation
[ S.20.1.5 ]
See also
[ S.20.2 ]
s_uniform_to_normal
[ S.20.2.1 ]
Summary
[ S.20.2.2 ]
Parameters
[ S.20.2.3 ]
Outcomes and figures
[ S.20.2.4 ]
Implementation
[ S.20.2.5 ]
See also
[ S.20.3 ]
s_uniform_to_lognorm
[ S.20.3.1 ]
Summary
[ S.20.3.2 ]
Parameters
[ S.20.3.3 ]
Outcomes and figures
[ S.20.3.4 ]
Implementation
[ S.20.3.5 ]
See also
[ S.20.4 ]
s_uniform_to_gamma
[ S.20.4.1 ]
Summary
[ S.20.4.2 ]
Parameters
[ S.20.4.3 ]
Outcomes and figures
[ S.20.4.4 ]
Implementation
[ S.20.4.5 ]
See also
[ S.20.5 ]
Combination: from FP-copula/historical marginal to joint
[ S.20.6 ]
s_ncop_nmarg
[ S.20.6.1 ]
Script summary
[ S.20.6.2 ]
Parameters
[ S.20.6.3 ]
Outcomes and figures
[ S.20.6.4 ]
Implementation
[ S.20.6.5 ]
See also
[ S.20.7 ]
s_tcop_tmarg
[ S.20.7.1 ]
Script summary
[ S.20.7.2 ]
Parameters
[ S.20.7.3 ]
Outcomes and figures
[ S.20.7.4 ]
Implementation
[ S.20.7.5 ]
See also
[ S.20.8 ]
s_display_norm_copula
[ S.20.8.1 ]
Summary
[ S.20.8.2 ]
Parameters
[ S.20.8.3 ]
Outcomes and figures
[ S.20.8.4 ]
Implementation
[ S.20.8.5 ]
See also
[ S.20.9 ]
s_display_t_copula
[ S.20.9.1 ]
Summary
[ S.20.9.2 ]
Parameters
[ S.20.9.3 ]
Outcomes and figures
[ S.20.9.4 ]
Implementation
[ S.20.9.5 ]
See also
[ S.20.10 ]
s_copula_returns
[ S.20.10.1 ]
Summary
[ S.20.10.2 ]
Parameters
[ S.20.10.3 ]
Outcomes and figures
[ S.20.10.4 ]
Implementation
[ S.20.10.5 ]
See also
[ S.20.11 ]
s_cop_marg_separation
[ S.20.11.1 ]
Script summary
[ S.20.11.2 ]
Parameters
[ S.20.11.3 ]
Outcomes and figures
[ S.20.11.4 ]
Implementation
[ S.20.11.5 ]
See also
[ S.20.12 ]
s_cop_marg_combination
[ S.20.12.1 ]
Script summary
[ S.20.12.2 ]
Data
[ S.20.12.3 ]
Parameters
[ S.20.12.4 ]
Outcomes and figures
[ S.20.12.5 ]
Implementation
[ S.20.12.6 ]
See also
[ S.20.13 ]
s_t_copula_norm_marginals
[ S.20.13.1 ]
Summary
[ S.20.13.2 ]
Parameters
[ S.20.13.3 ]
Outcomes and figures
[ S.20.13.4 ]
Implementation
[ S.20.13.5 ]
See also
[ S.20.14 ]
Separation: from historical joint to copula/marginal
[ S.20.15 ]
Display the pdf of a bivariate t-copula
[ S.21 ]
Geometry of distributions
[ S.22 ]
Location and dispersion
[ S.22.1 ]
s_logn_uncertainty_bands
[ S.22.1.1 ]
Summary
[ S.22.1.2 ]
Parameters
[ S.22.1.3 ]
Outcomes and figures
[ S.22.1.4 ]
Implementation
[ S.22.2 ]
Different visualizations of the multivariate uncertainty band
[ S.22.3 ]
s_affine_equiv_exp_cov
[ S.22.3.1 ]
Summary
[ S.22.3.2 ]
Parameters
[ S.22.3.3 ]
Outcomes and figures
[ S.22.3.4 ]
Implementation
[ S.22.3.5 ]
See also
[ S.22.4 ]
Ellipsoid and uncertainty band of a bivariate normal
[ S.22.5 ]
s_ellipsoid_multiv_exp_cov
[ S.22.5.1 ]
Summary
[ S.22.5.2 ]
Parameters
[ S.22.5.3 ]
Outcomes and figures
[ S.22.5.4 ]
Implementation
[ S.22.5.5 ]
See also
[ S.22.6 ]
s_display_corr_norm_ellips
[ S.22.6.1 ]
Summary
[ S.22.6.2 ]
Parameters
[ S.22.6.3 ]
Outcomes and figures
[ S.22.6.4 ]
Implementation
[ S.22.6.5 ]
See also
[ S.22.7 ]
Ellipsoid and uncertainty band of a bivariate lognormal
[ S.22.8 ]
Ellipsoid and uncertainty band of normalized Wishart marginals
[ S.22.9 ]
s_affine_equiv_mode_mod_disp
[ S.22.9.1 ]
Summary
[ S.22.9.2 ]
Parameters
[ S.22.9.3 ]
Outcomes and figures
[ S.22.9.4 ]
Implementation
[ S.22.9.5 ]
See also
[ S.22.10 ]
Iso-contour of a lognormal bivariate distribution
[ S.22.11 ]
s_markov_ineq
[ S.22.11.1 ]
Summary
[ S.22.11.2 ]
Parameters
[ S.22.11.3 ]
Outcomes and figures
[ S.22.11.4 ]
Implementation
[ S.22.11.5 ]
See also
[ S.22.12 ]
s_chebyshev_ineq
[ S.22.12.1 ]
Summary
[ S.22.12.2 ]
Parameters
[ S.22.12.3 ]
Outcomes and figures
[ S.22.12.4 ]
Implementation
[ S.22.12.5 ]
See also
[ S.22.13 ]
Expectation-covariance as minimum volume ellipsoid
[ S.22.14 ]
R-squared: numerical example
[ S.22.15 ]
Euclidean vectors of joint normal variables
[ S.22.16 ]
s_lognormal_l2_geometry
[ S.22.16.1 ]
Summary
[ S.22.16.2 ]
Parameters
[ S.22.16.3 ]
Outcomes and figures
[ S.22.16.4 ]
Implementation
[ S.22.16.5 ]
See also
[ S.22.17 ]
Orthogonality of principal directions
[ S.22.18 ]
Euclidean vectors via linear transformation and Riccati root
[ S.23 ]
Correlation and generalizations
[ S.23.1 ]
s_norm_const_sw
[ S.23.1.1 ]
Summary
[ S.23.1.2 ]
Parameters
[ S.23.1.3 ]
Outcomes and figures
[ S.23.1.4 ]
Implementation
[ S.23.2 ]
s_dependence_structure_call_put
[ S.23.2.1 ]
Summary
[ S.23.2.2 ]
Parameters
[ S.23.2.3 ]
Outcomes and figures
[ S.23.2.4 ]
Implementation
[ S.23.2.5 ]
See also
[ S.23.3 ]
Correlation between normal variables (numerical proof)
[ S.23.4 ]
Correlation between lognormal variables (numerical proof)
[ S.23.5 ]
Correlation between normalized Wishart marginals(numerical proof)
[ S.23.6 ]
s_uncorr_no_indep
[ S.23.6.1 ]
Summary
[ S.23.6.2 ]
Parameters
[ S.23.6.3 ]
Outcomes and figures
[ S.23.6.4 ]
Implementation
[ S.23.6.5 ]
See also
[ S.23.7 ]
s_clt_student_t
[ S.23.7.1 ]
Summary
[ S.23.7.2 ]
Parameters
[ S.23.7.3 ]
Outcomes and figures
[ S.23.7.4 ]
Implementation
[ S.23.7.5 ]
See also
[ S.23.8 ]
s_full_dependence
[ S.23.8.1 ]
Summary
[ S.23.8.2 ]
Parameters
[ S.23.8.3 ]
Outcomes and figures
[ S.23.8.4 ]
Implementation
[ S.23.8.5 ]
See also
[ S.24 ]
Statistical decision theory
[ S.24.1 ]
s_strong_dominance
[ S.24.1.1 ]
Summary
[ S.24.1.2 ]
Parameters
[ S.24.1.3 ]
Outcomes and figures
[ S.24.1.4 ]
Implementation
[ S.24.1.5 ]
See also
[ S.24.2 ]
s_weak_dominance
[ S.24.2.1 ]
Summary
[ S.24.2.2 ]
Parameters
[ S.24.2.3 ]
Outcomes and figures
[ S.24.2.4 ]
Implementation
[ S.24.2.5 ]
See also
[ S.24.3 ]
s_second_order_dominance
[ S.24.3.1 ]
Summary
[ S.24.3.2 ]
Parameters
[ S.24.3.3 ]
Outcomes and figures
[ S.24.3.4 ]
Implementation
[ S.25 ]
Useful algorithms
S.VII. Factor models and learning
[ S.VII ]
s_lfm_executive_summary
[ S.VII ]
Summary
[ S.VII ]
Data
[ S.VII ]
Parameters
[ S.VII ]
Outcomes and figures
[ S.VII ]
Implementation
[ S.VII ]
See also
[ S.26 ]
Linear factor models
[ S.26.1 ]
s_normal_mean_regression_lfm
[ S.26.1.1 ]
Summary
[ S.26.1.2 ]
Parameters
[ S.26.1.3 ]
Outcomes and figures
[ S.26.1.4 ]
Implementation
[ S.26.1.5 ]
See also
[ S.26.2 ]
s_regression_lfm
[ S.26.2.1 ]
Summary
[ S.26.2.2 ]
Parameters
[ S.26.2.3 ]
Outcomes and figures
[ S.26.2.4 ]
Implementation
[ S.26.2.5 ]
See also
[ S.26.3 ]
Comparison of LFM’s
[ S.26.4 ]
Symmetric regression: numerical example
[ S.26.5 ]
s_principal_component_lfm
[ S.26.5.1 ]
Summary
[ S.26.5.2 ]
Parameters
[ S.26.5.3 ]
Outcomes and figures
[ S.26.5.4 ]
Implementation
[ S.26.5.5 ]
See also
[ S.26.6 ]
Principal-component LFM’s: joint distribution of residuals and factors
[ S.26.7 ]
s_factor_analysis_lfm
[ S.26.7.1 ]
Summary
[ S.26.7.2 ]
Parameters
[ S.26.7.3 ]
Outcomes and figures
[ S.26.7.4 ]
Implementation
[ S.26.7.5 ]
See also
[ S.26.8 ]
s_cross_section_lfm
[ S.26.8.1 ]
Summary
[ S.26.8.2 ]
Parameters
[ S.26.8.3 ]
Outcomes and figures
[ S.26.8.4 ]
Implementation
[ S.26.9 ]
s_factor_replication_logn
[ S.26.9.1 ]
Summary
[ S.26.9.2 ]
Parameters
[ S.26.9.3 ]
Outcomes and figures
[ S.26.9.4 ]
Implementation
[ S.26.9.5 ]
See also
[ S.26.10 ]
Factor-replicating portfolio convergence under normal factors and residuals
[ S.26.11 ]
Cross-sectional LFM’s loadings versus regression LFM’s loadings
[ S.26.12 ]
Cross-sectional factor extraction matrix is variance-minimizing: numerical example
[ S.26.13 ]
Cross-sectional LFM’s optimal r-squared: numerical example
[ S.26.14 ]
Correlation of residuals in generalized cross-sectional LFM
[ S.26.15 ]
Factor extraction: numerical test
[ S.26.16 ]
Hidden factors: puzzle
[ S.26.17 ]
Hidden regression versus Principal-component LFM’s: numerical example
[ S.26.18 ]
Correlation of residuals in generalized regression LFM’s
[ S.26.19 ]
Fully observable factor model
[ S.26.20 ]
Fully observable models: study of factor-residual distribution
[ S.26.21 ]
Cross-sectional LFM’s: generalized cross-sectional industry factors
[ S.26.22 ]
Regression LFM’s: shifted-lognormal regression II
[ S.26.23 ]
s_reg_truncated_lfm
[ S.26.23.1 ]
Summary
[ S.26.23.2 ]
Data
[ S.26.23.3 ]
Parameters
[ S.26.23.4 ]
Outcomes and figures
[ S.26.23.5 ]
Implementation
[ S.26.23.6 ]
See also
[ S.26.24 ]
s_pca_truncated_lfm
[ S.26.24.1 ]
Summary
[ S.26.24.2 ]
Data
[ S.26.24.3 ]
Parameters
[ S.26.24.4 ]
Outcomes and figures
[ S.26.24.5 ]
Implementation
[ S.26.24.6 ]
See also
[ S.26.25 ]
s_cross_section_truncated_lfm
[ S.26.25.1 ]
Summary
[ S.26.25.2 ]
Data
[ S.26.25.3 ]
Parameters
[ S.26.25.4 ]
Outcomes and figures
[ S.26.25.5 ]
Implementation
[ S.26.25.6 ]
See also
[ S.26.26 ]
Simulation of the distribution of statistics of regression parameters
[ S.26.27 ]
Regression LFM’s: truncated estimations
[ S.26.28 ]
Regression LFM’s: generalized regression industry factors
[ S.26.29 ]
s_cpca_vs_pca
[ S.26.29.1 ]
Script summary
[ S.26.29.2 ]
Parameters
[ S.26.29.3 ]
Outcomes and figures
[ S.26.29.4 ]
Implementation
[ S.26.29.5 ]
See also
[ S.27 ]
Machine learning foundations
[ S.28 ]
Supervised learning: regression
[ S.28.1 ]
s_logn_mean_regression
[ S.28.1.1 ]
Summary
[ S.28.1.2 ]
Parameters
[ S.28.1.3 ]
Outcomes and figures
[ S.28.1.4 ]
Implementation
[ S.28.1.5 ]
See also
[ S.28.2 ]
s_logn_mean_lin_regression
[ S.28.2.1 ]
Summary
[ S.28.2.2 ]
Parameters
[ S.28.2.3 ]
Outcomes and figures
[ S.28.2.4 ]
Implementation
[ S.28.2.5 ]
See also
[ S.28.3 ]
s_continuum_discrete_point_pred
[ S.28.3.1 ]
Summary
[ S.28.3.2 ]
Parameters
[ S.28.3.3 ]
Outcomes and figures
[ S.28.3.4 ]
Implementation
[ S.28.3.5 ]
See also
[ S.28.4 ]
s_sp_anova
[ S.28.4.1 ]
Summary
[ S.28.4.2 ]
Data
[ S.28.4.3 ]
Parameters
[ S.28.4.4 ]
Outcomes and figures
[ S.28.4.5 ]
Implementation
[ S.28.4.6 ]
See also
[ S.28.5 ]
s_interactions
[ S.28.5.1 ]
Summary
[ S.28.5.2 ]
Input parameters
[ S.28.5.3 ]
Outcomes and figures
[ S.28.5.4 ]
Implementation
[ S.28.5.5 ]
See also
[ S.28.6 ]
s_kernel_trick
[ S.28.7 ]
s_cart
[ S.28.7.1 ]
Summary
[ S.28.7.2 ]
Data
[ S.28.7.3 ]
Parameters
[ S.28.7.4 ]
Outcomes and figures
[ S.28.7.5 ]
Implementation
[ S.28.8 ]
s_ann_regression
[ S.28.8.1 ]
Summary
[ S.28.8.2 ]
Data
[ S.28.8.3 ]
Input parameters
[ S.28.8.4 ]
Outcomes and figures
[ S.28.8.5 ]
Implementation
[ S.28.9 ]
s_gradient_boosting
[ S.28.9.1 ]
Summary
[ S.28.9.2 ]
Data
[ S.28.9.3 ]
Parameters
[ S.28.9.4 ]
Outcomes and figures
[ S.28.9.5 ]
Implementation
[ S.28.10 ]
s_logn_quant_regression
[ S.28.10.1 ]
Summary
[ S.28.10.2 ]
Parameters
[ S.28.10.3 ]
Outcomes and figures
[ S.28.10.4 ]
Implementation
[ S.28.10.5 ]
See also
[ S.28.11 ]
s_logn_quant_lin_regression
[ S.28.11.1 ]
Summary
[ S.28.11.2 ]
Parameters
[ S.28.11.3 ]
Outcomes and figures
[ S.28.11.4 ]
Implementation
[ S.28.11.5 ]
See also
[ S.28.12 ]
s_continuum_discrete_generative_pred
[ S.29 ]
Supervised learning: classification
[ S.29.1 ]
s_binary_margin_losses
[ S.29.1.1 ]
Summary
[ S.29.1.2 ]
Parameters
[ S.29.1.3 ]
Outcomes and figures
[ S.29.1.4 ]
Implementation
[ S.29.2 ]
s_point_classification_normal_mixtures
[ S.29.2.1 ]
Summary
[ S.29.2.2 ]
Parameters
[ S.29.2.3 ]
Outcomes and figures
[ S.29.2.4 ]
Implementation
[ S.29.2.5 ]
See also
[ S.29.3 ]
s_linclass_regression
[ S.29.4 ]
s_linclass_fda
[ S.29.4.1 ]
Summary
[ S.29.4.2 ]
Data
[ S.29.4.3 ]
Outcomes and figures
[ S.29.4.4 ]
Implementation
[ S.29.4.5 ]
See also
[ S.29.5 ]
s_linclass_perceptron
[ S.29.5.1 ]
Summary
[ S.29.5.2 ]
Data
[ S.29.5.3 ]
Outcomes and figures
[ S.29.5.4 ]
Implementation
[ S.29.6 ]
s_linclass_svm
[ S.29.6.1 ]
Summary
[ S.29.6.2 ]
Data
[ S.29.6.3 ]
Parameters
[ S.29.6.4 ]
Outcomes and figures
[ S.29.6.5 ]
Implementation
[ S.29.7 ]
s_linclass_ann_svm
[ S.30 ]
Unsupervised learning
[ S.30.1 ]
s_autoencoders_kmeans
[ S.30.1.1 ]
Summary
[ S.30.1.2 ]
Parameters
[ S.30.1.3 ]
Outcomes and figures
[ S.30.1.4 ]
Implementation
[ S.30.1.5 ]
See also
[ S.30.2 ]
s_factor_analysis_algos
[ S.30.2.1 ]
Summary
[ S.30.2.2 ]
Parameters
[ S.30.2.3 ]
Outcomes and figures
[ S.30.2.4 ]
Implementation
[ S.30.2.5 ]
See also
[ S.31 ]
Generalized probabilistic inference
[ S.31.1 ]
s_min_rel_ent_point_view
[ S.31.1.1 ]
Summary
[ S.31.1.2 ]
Parameters
[ S.31.1.3 ]
Outcomes
[ S.31.1.4 ]
Implementation
[ S.31.1.5 ]
See also
[ S.31.2 ]
s_min_rel_ent_distr_view
[ S.31.2.1 ]
Summary
[ S.31.2.2 ]
Parameters
[ S.31.2.3 ]
Outcomes
[ S.31.2.4 ]
Implementation
[ S.31.2.5 ]
See also
[ S.31.3 ]
s_min_rel_ent_partial_view
[ S.31.3.1 ]
Summary
[ S.31.3.2 ]
Parameters
[ S.31.3.3 ]
Outcomes
[ S.31.3.4 ]
Implementation
[ S.31.3.5 ]
See also
[ S.31.4 ]
s_entropy_view
[ S.31.4.1 ]
Summary
[ S.31.4.2 ]
Parameters
[ S.31.4.3 ]
Outcomes and figures
[ S.31.4.4 ]
Implementation
[ S.31.4.5 ]
See also
[ S.31.5 ]
s_views_linear_exp
[ S.31.5.1 ]
Summary
[ S.31.5.2 ]
Parameters
[ S.31.5.3 ]
Outcomes
[ S.31.5.4 ]
Implementation
[ S.31.5.5 ]
See also
[ S.31.6 ]
s_views_gen_expectations
[ S.31.6.1 ]
Summary
[ S.31.6.2 ]
Parameters
[ S.31.6.3 ]
Outcomes
[ S.31.6.4 ]
Implementation
[ S.31.6.5 ]
See also
[ S.31.7 ]
s_views_sorted_exp
[ S.31.7.1 ]
Summary
[ S.31.7.2 ]
Parameters
[ S.31.7.3 ]
Outcomes
[ S.31.7.4 ]
Implementation
[ S.31.7.5 ]
See also
[ S.31.8 ]
s_views_st_deviations
[ S.31.8.1 ]
Summary
[ S.31.8.2 ]
Parameters
[ S.31.8.3 ]
Outcomes
[ S.31.8.4 ]
Implementation
[ S.31.8.5 ]
See also
[ S.31.9 ]
s_views_correlations
[ S.31.9.1 ]
Summary
[ S.31.9.2 ]
Parameters
[ S.31.9.3 ]
Outcomes
[ S.31.9.4 ]
Implementation
[ S.31.9.5 ]
See also
[ S.31.10 ]
s_views_cond_prob
[ S.31.10.1 ]
Summary
[ S.31.10.2 ]
Parameters
[ S.31.10.3 ]
Outcomes
[ S.31.10.4 ]
Implementation
[ S.31.10.5 ]
See also
[ S.31.11 ]
s_views_cdf
[ S.31.11.1 ]
Summary
[ S.31.11.2 ]
Parameters
[ S.31.11.3 ]
Outcomes
[ S.31.11.4 ]
Implementation
[ S.31.11.5 ]
See also
[ S.31.12 ]
s_views_cond_exp
[ S.31.12.1 ]
Summary
[ S.31.12.2 ]
Parameters
[ S.31.12.3 ]
Outcomes
[ S.31.12.4 ]
Implementation
[ S.31.12.5 ]
See also
[ S.31.13 ]
Convexity test for relative entropy
[ S.31.14 ]
Numerical verification of the gradient of entropy
[ S.31.15 ]
Numerical verification of gradient of constraint function on signal
[ S.31.16 ]
Numerical verification of the Hessian of entropy
[ S.31.17 ]
Numerical and analytical Hessian of constraints on signal
[ S.31.18 ]
Copula opinion pooling: uninformative views
[ S.32 ]
Dynamic and spatial models
[ S.32.1 ]
s_dyn_principal_component_var
[ S.32.1.1 ]
Summary
[ S.32.1.2 ]
Parameters
[ S.32.1.3 ]
Outcomes and figures
[ S.32.1.4 ]
Implementation
[ S.32.1.5 ]
See also
[ S.32.2 ]
s_kalman_filter_yield_curve
[ S.32.2.1 ]
Summary
[ S.32.2.2 ]
Data
[ S.32.2.3 ]
Parameters
[ S.32.2.4 ]
Outcomes and figures
[ S.32.2.5 ]
Implementation
[ S.32.2.6 ]
See also
[ S.32.3 ]
s_hidden_markov_model_stocks
[ S.32.3.1 ]
Summary
[ S.32.3.2 ]
Data
[ S.32.3.3 ]
Parameters
[ S.32.3.4 ]
Outcomes and figures
[ S.32.3.5 ]
Implementation
S.VIII. Stochastic processes
[ S.33 ]
Stochastic processes primer
[ S.33.1 ]
s_discrete_partitioned_process
[ S.33.1.1 ]
Summary
[ S.33.1.2 ]
Input
[ S.33.1.3 ]
Outcomes and figures
[ S.33.1.4 ]
Implementation
[ S.34 ]
Covariance stationary processes
[ S.34.1 ]
s_autocov_spec_dens_ar1
[ S.34.1.1 ]
Summary
[ S.34.1.2 ]
Parameters
[ S.34.1.3 ]
Outcomes and figures
[ S.34.1.4 ]
Implementation
[ S.34.2 ]
s_spectral_representation
[ S.34.2.1 ]
Summary
[ S.34.2.2 ]
Parameters
[ S.34.2.3 ]
Outcomes and figures
[ S.34.2.4 ]
Implementation
[ S.34.2.5 ]
Tips
[ S.34.2.6 ]
See also
[ S.34.3 ]
s_toeplitz_spectral
[ S.34.3.1 ]
Summary
[ S.34.3.2 ]
Parameters
[ S.34.3.3 ]
Outcomes and figures
[ S.34.3.4 ]
Implementation
[ S.34.3.5 ]
See also
[ S.34.4 ]
s_bandpass_filter_ar1
[ S.34.4.1 ]
Summary
[ S.34.4.2 ]
Parameters
[ S.34.4.3 ]
Outcomes and figures
[ S.34.4.4 ]
Implementation
[ S.34.4.5 ]
See also
[ S.35 ]
Invariance tests
[ S.35.1 ]
s_elltest_normal
[ S.35.1.1 ]
Summary
[ S.35.1.2 ]
Parameters
[ S.35.1.3 ]
Outcomes and figures
[ S.35.1.4 ]
Implementation
[ S.35.1.5 ]
See also
[ S.35.2 ]
Kolmogorov-Smirnov test for invariance on simulations
[ S.35.3 ]
Copula based tests for invariance on simulations
[ S.35.4 ]
Invariance tests on realized time series
[ S.36 ]
Continuous time processes
[ S.36.1 ]
s_projection_brownian_motion
[ S.36.1.1 ]
Summary
[ S.36.1.2 ]
Data
[ S.36.1.3 ]
Parameters
[ S.36.1.4 ]
Outcomes and figures
[ S.36.1.5 ]
Implementation
[ S.36.1.6 ]
See also
[ S.36.2 ]
s_projection_univ_rating
[ S.36.2.1 ]
Summary
[ S.36.2.2 ]
Data
[ S.36.2.3 ]
Outcomes and figures
[ S.36.2.4 ]
Implementation
[ S.36.2.5 ]
See also
[ S.36.3 ]
Projection of Poisson process
[ S.36.4 ]
Simulate compound Poisson processes
[ S.36.5 ]
Projection of a Compound Poisson Process
[ S.36.6 ]
Projection of Cauchy distribution
[ S.36.7 ]
Projection of the variance gamma distribution
[ S.36.8 ]
Simulate the NIG and VG processes
[ S.36.9 ]
Simulation of the variance gamma process by subordination
[ S.36.10 ]
Projection of the Student t distribution
[ S.36.11 ]
Projection of the uniform distribution
[ S.36.12 ]
Projection of fractional Brownian motion
[ S.36.13 ]
Projection of the Heston process
[ S.36.14 ]
Simulation of the Heston process by Time Change
[ S.36.15 ]
Simulation of a Ornstein-Uhlenbeck process
[ S.36.16 ]
Projection of conditional mean and covariance of the MVOU process
S.IX. Estimation theory
[ S.37 ]
Probabilistic estimation and inference techniques
[ S.37.1 ]
Maximum likelihood estimation
[ S.38 ]
Estimation and assessment
[ S.38.1 ]
s_location_estimators
[ S.38.1.1 ]
Summary
[ S.38.1.2 ]
Parameters
[ S.38.1.3 ]
Outcomes and figures
[ S.38.1.4 ]
Implementation
[ S.38.1.5 ]
See also
[ S.39.1 ]
Estimation assessment of a moment-based functional of a mixture I
[ S.39.2 ]
Estimation assessment of a moment-based functional of a mixture II
[ S.39.3 ]
Estimation assessment of the quantile of a mixture I
[ S.39.4 ]
Estimation assessment of the quantile of a mixture II
[ S.39.5 ]
s_location_stress_error
[ S.39.5.1 ]
Summary
[ S.39.5.2 ]
Parameters
[ S.39.5.3 ]
Outcomes and figures
[ S.39.5.4 ]
Implementation
[ S.39.5.5 ]
See also
[ S.39.6 ]
Estimation assessment
[ S.39.7 ]
Sample mean and sample covariance estimation error
[ S.39.8 ]
Sample quantiles distribution
[ S.39.9 ]
s_bias_vs_variance_lognormal
[ S.39.10 ]
Predictive distribution assessment
[ S.39 ]
Bias reduction
[ S.39.1 ]
s_bias_reduction_toy
[ S.40 ]
Estimation and regularization
[ S.40.1 ]
s_encoding
[ S.41 ]
Hypothesis testing
Featured case studies
S.X. Quantitative finance: the "Checklist"
[ S.42 ]
Historical Checklist
[ S.42.1 ]
s_checklist_historical_step01
[ S.42.1.1 ]
Summary
[ S.42.1.2 ]
Data
[ S.42.1.3 ]
Parameters
[ S.42.1.4 ]
Outcomes and figures
[ S.42.1.5 ]
Implementation
[ S.42.1.6 ]
See also
[ S.42.2 ]
s_checklist_historical_step02
[ S.42.2.1 ]
Summary
[ S.42.2.2 ]
Data
[ S.42.2.3 ]
Parameters
[ S.42.2.4 ]
Outcomes and figures
[ S.42.2.5 ]
Implementation
[ S.42.2.6 ]
See also
[ S.42.3 ]
s_checklist_historical_step03
[ S.42.3.1 ]
Summary
[ S.42.3.2 ]
Data
[ S.42.3.3 ]
Parameters
[ S.42.3.4 ]
Outcomes and figures
[ S.42.3.5 ]
Implementation
[ S.42.3.6 ]
See also
[ S.42.4 ]
s_checklist_historical_step04
[ S.42.4.1 ]
Summary
[ S.42.4.2 ]
Data
[ S.42.4.3 ]
Parameters
[ S.42.4.4 ]
Outcomes and figures
[ S.42.4.5 ]
Implementation
[ S.42.4.6 ]
See also
[ S.42.5 ]
s_checklist_historical_step05
[ S.42.5.1 ]
Summary
[ S.42.5.2 ]
Data
[ S.42.5.3 ]
Parameters
[ S.42.5.4 ]
Outcomes and figures
[ S.42.5.5 ]
Implementation
[ S.42.5.6 ]
See also
[ S.42.6 ]
s_checklist_historical_step06
[ S.42.6.1 ]
Script summary
[ S.42.6.2 ]
Data
[ S.42.6.3 ]
Parameters
[ S.42.6.4 ]
Outcomes and figures
[ S.42.6.5 ]
Implementation
[ S.42.6.6 ]
See also
[ S.42.7 ]
s_checklist_historical_step07
[ S.42.7.1 ]
Script summary
[ S.42.7.2 ]
Data
[ S.42.7.3 ]
Parameters
[ S.42.7.4 ]
Outcomes and figures
[ S.42.7.5 ]
Implementation
[ S.42.7.6 ]
See also
[ S.42.8 ]
s_checklist_historical_step08
[ S.42.8.1 ]
Script summary
[ S.42.8.2 ]
Data
[ S.42.8.3 ]
Parameters
[ S.42.8.4 ]
Outcomes and figures
[ S.42.8.5 ]
Implementation
[ S.42.8.6 ]
See also
[ S.42.9 ]
s_checklist_historical_step09
[ S.42.9.1 ]
Script summary
[ S.42.9.2 ]
Data
[ S.42.9.3 ]
Parameters
[ S.42.9.4 ]
Outcomes and figures
[ S.42.9.5 ]
Implementation
[ S.42.9.6 ]
See also
[ S.42.10 ]
s_checklist_historical_step10
[ S.42.10.1 ]
Script summary
[ S.42.10.2 ]
Data
[ S.42.10.3 ]
Parameters
[ S.42.10.4 ]
Outcomes and figures
[ S.42.10.5 ]
Implementation
[ S.42.10.6 ]
See also
[ S.43 ]
Monte Carlo Checklist
[ S.43.1 ]
s_checklist_montecarlo_step01
[ S.43.1.1 ]
Summary
[ S.43.1.2 ]
Data
[ S.43.1.3 ]
Parameters
[ S.43.1.4 ]
Outcomes and figures
[ S.43.1.5 ]
Implementation
[ S.43.1.6 ]
See also
[ S.43.2 ]
s_checklist_montecarlo_step02
[ S.43.2.1 ]
Summary
[ S.43.2.2 ]
Data
[ S.43.2.3 ]
Parameters
[ S.43.2.4 ]
Outcomes and figures
[ S.43.2.5 ]
Implementation
[ S.43.2.6 ]
See also
[ S.43.3 ]
s_checklist_montecarlo_step03
[ S.43.3.1 ]
Summary
[ S.43.3.2 ]
Data
[ S.43.3.3 ]
Parameters
[ S.43.3.4 ]
Outcomes and figures
[ S.43.3.5 ]
Implementation
[ S.43.3.6 ]
See also
[ S.43.4 ]
s_checklist_montecarlo_step04
[ S.43.4.1 ]
Summary
[ S.43.4.2 ]
Data
[ S.43.4.3 ]
Parameters
[ S.43.4.4 ]
Outcomes and figures
[ S.43.4.5 ]
Implementation
[ S.43.4.6 ]
See also
[ S.43.5 ]
s_checklist_montecarlo_step05
[ S.43.5.1 ]
Summary
[ S.43.5.2 ]
Data
[ S.43.5.3 ]
Parameters
[ S.43.5.4 ]
Outcomes and figures
[ S.43.5.5 ]
Implementation
[ S.43.5.6 ]
See also
[ S.43.6 ]
s_checklist_montecarlo_step06
[ S.43.6.1 ]
Script summary
[ S.43.6.2 ]
Data
[ S.43.6.3 ]
Parameters
[ S.43.6.4 ]
Outcomes and figures
[ S.43.6.5 ]
Implementation
[ S.43.6.6 ]
See also
[ S.43.7 ]
s_checklist_montecarlo_step07
[ S.43.7.1 ]
Script summary
[ S.43.7.2 ]
Data
[ S.43.7.3 ]
Parameters
[ S.43.7.4 ]
Outcomes and figures
[ S.43.7.5 ]
Implementation
[ S.43.7.6 ]
See also
[ S.43.8 ]
s_checklist_montecarlo_step08
[ S.43.8.1 ]
Script summary
[ S.43.8.2 ]
Data
[ S.43.8.3 ]
Parameters
[ S.43.8.4 ]
Outcomes and figures
[ S.43.8.5 ]
Implementation
[ S.43.8.6 ]
See also
[ S.43.9 ]
s_checklist_montecarlo_step09
[ S.43.9.1 ]
Script summary
[ S.43.9.2 ]
Data
[ S.43.9.3 ]
Parameters
[ S.43.9.4 ]
Outcomes and figures
[ S.43.9.5 ]
Implementation
[ S.43.9.6 ]
See also
[ S.43.10 ]
s_checklist_montecarlo_step10
[ S.43.10.1 ]
Script summary
[ S.43.10.2 ]
Data
[ S.43.10.3 ]
Parameters
[ S.43.10.4 ]
Outcomes and figures
[ S.43.10.5 ]
Implementation
[ S.43.10.6 ]
See also
S.XI. Data science: factor models and learning
[ S.44 ]
Principal component analysis of the yield curve
[ S.44.1 ]
Eigenvectors for Toeplitz structure
[ S.44.2 ]
s_yield_change_correlation
[ S.44.2.1 ]
Summary
[ S.44.2.2 ]
Data
[ S.44.2.3 ]
Parameters
[ S.44.2.4 ]
Outcomes and figures
[ S.44.2.5 ]
Implementation
[ S.44.2.6 ]
See also
[ S.44.3 ]
s_pca_empirical
[ S.44.3.1 ]
Summary
[ S.44.3.2 ]
Data
[ S.44.3.3 ]
Parameters
[ S.44.3.4 ]
Outcomes and figures
[ S.44.3.5 ]
Implementation
[ S.44.3.6 ]
See also
[ S.44.4 ]
s_pca_yield
[ S.44.4.1 ]
Summary
[ S.44.4.2 ]
Data
[ S.44.4.3 ]
Outcomes and figures
[ S.44.4.4 ]
Implementation
[ S.45 ]
Machine learning for hedging
[ S.45.1 ]
s_call_bms_hedging
[ S.45.1.1 ]
Summary
[ S.45.1.2 ]
Data
[ S.45.1.3 ]
Outcomes and figures
[ S.45.1.4 ]
Implementation
[ S.45.1.5 ]
See also
[ S.45.2 ]
s_call_least_squares_regression
[ S.45.2.1 ]
Summary
[ S.45.2.2 ]
Data
[ S.45.2.3 ]
Parameters
[ S.45.2.4 ]
Outcomes and figures
[ S.45.2.5 ]
Implementation
[ S.45.2.6 ]
See also
[ S.45.3 ]
s_call_least_abs_distance_regression
[ S.45.3.1 ]
Summary
[ S.45.3.2 ]
Data
[ S.45.3.3 ]
Parameters
[ S.45.3.4 ]
Outcomes and figures
[ S.45.3.5 ]
Implementation
[ S.45.3.6 ]
See also
[ S.46 ]
Regression in the stock market
[ S.46.1 ]
s_market_prediction_regression
[ S.46.2 ]
s_lasso_vs_ridge
[ S.46.2.1 ]
Summary
[ S.46.2.2 ]
Data
[ S.46.2.3 ]
Parameters
[ S.46.2.4 ]
Outcomes and figures
[ S.46.2.5 ]
Implementation
[ S.46.2.6 ]
See also
[ S.46.3 ]
Regression LFM’s: randomized least square estimates
[ S.46.4 ]
s_reg_lfm_bayes_prior_niw
[ S.46.4.1 ]
Summary
[ S.46.4.2 ]
Parameters
[ S.46.4.3 ]
Outcomes and figures
[ S.46.4.4 ]
Implementation
[ S.46.5 ]
s_reg_lfm_bayes_posterior_niw
[ S.46.5.1 ]
Summary
[ S.46.5.2 ]
Parameters
[ S.46.5.3 ]
Outcomes and figures
[ S.46.5.4 ]
Implementation
[ S.46.5.5 ]
See also
[ S.47 ]
Credit default classification
[ S.47.1 ]
s_default_probabilities
[ S.48 ]
Clustering for the stock market
Functions documentation
[ F.1 ]
Estimation
[ F.1.1 ]
cointegration_fp
[ F.1.1.1 ]
Summary
[ F.1.1.2 ]
Input
[ F.1.1.3 ]
Output
[ F.1.1.4 ]
Implementation
[ F.1.1.5 ]
Example
[ F.1.1.6 ]
Case studies
[ F.1.1.7 ]
See also
[ F.1.2 ]
conditional_fp
[ F.1.2.1 ]
Summary
[ F.1.2.2 ]
Input
[ F.1.2.3 ]
Output
[ F.1.2.4 ]
Implementation
[ F.1.2.5 ]
Example
[ F.1.2.6 ]
Case studies
[ F.1.2.7 ]
References
[ F.1.2.8 ]
See also
[ F.1.3 ]
cov_2_corr
[ F.1.3.1 ]
Summary
[ F.1.3.2 ]
Input
[ F.1.3.3 ]
Output
[ F.1.3.4 ]
Implementation
[ F.1.3.5 ]
Example
[ F.1.3.6 ]
Case studies
[ F.1.3.7 ]
References
[ F.1.4 ]
crisp_fp
[ F.1.4.1 ]
Summary
[ F.1.4.2 ]
Input
[ F.1.4.3 ]
Output
[ F.1.4.4 ]
Implementation
[ F.1.4.5 ]
Example
[ F.1.4.6 ]
Case studies
[ F.1.4.7 ]
References
[ F.1.4.8 ]
See also
[ F.1.5 ]
effective_num_scenarios
[ F.1.5.1 ]
Summary
[ F.1.5.2 ]
Input
[ F.1.5.3 ]
Output
[ F.1.5.4 ]
Implementation
[ F.1.5.5 ]
Example
[ F.1.5.6 ]
Case studies
[ F.1.5.7 ]
References
[ F.1.5.8 ]
See also
[ F.1.6 ]
enet
[ F.1.6.1 ]
Input
[ F.1.6.2 ]
Output
[ F.1.6.3 ]
Implementation
[ F.1.6.4 ]
Example
[ F.1.6.5 ]
Case studies
[ F.1.7 ]
exp_decay_fp
[ F.1.7.1 ]
Summary
[ F.1.7.2 ]
Input
[ F.1.7.3 ]
Output
[ F.1.7.4 ]
Implementation
[ F.1.7.5 ]
Example
[ F.1.7.6 ]
Case studies
[ F.1.8 ]
factor_analysis_mlf
[ F.1.8.1 ]
Summary
[ F.1.8.2 ]
Input
[ F.1.8.3 ]
Output
[ F.1.8.4 ]
Implementation
[ F.1.8.5 ]
Example
[ F.1.8.6 ]
Case studies
[ F.1.8.7 ]
References
[ F.1.8.8 ]
See also
[ F.1.9 ]
factor_analysis_paf
[ F.1.9.1 ]
Summary
[ F.1.9.2 ]
Input
[ F.1.9.3 ]
Output
[ F.1.9.4 ]
Implementation
[ F.1.9.5 ]
Example
[ F.1.9.6 ]
Case studies
[ F.1.9.7 ]
References
[ F.1.9.8 ]
See also
[ F.1.10 ]
fit_dcc_t
[ F.1.10.1 ]
Summary
[ F.1.10.2 ]
Input
[ F.1.10.3 ]
Output
[ F.1.10.4 ]
Implementation
[ F.1.10.5 ]
Example
[ F.1.10.6 ]
Case studies
[ F.1.10.7 ]
See also
[ F.1.11 ]
fit_garch_fp
[ F.1.11.1 ]
Summary
[ F.1.11.2 ]
Input
[ F.1.11.3 ]
Output
[ F.1.11.4 ]
Implementation
[ F.1.11.5 ]
Tips
[ F.1.11.6 ]
Example
[ F.1.11.7 ]
Case studies
[ F.1.11.8 ]
See also
[ F.1.12 ]
fit_lfm_lasso
[ F.1.12.1 ]
Summary
[ F.1.12.2 ]
Input
[ F.1.12.3 ]
Output
[ F.1.12.4 ]
Implementation
[ F.1.12.5 ]
Example
[ F.1.12.6 ]
See also
[ F.1.13 ]
fit_lfm_lasso_path
[ F.1.14 ]
fit_lfm_mlfp
[ F.1.14.1 ]
Summary
[ F.1.14.2 ]
Input
[ F.1.14.3 ]
Output
[ F.1.14.4 ]
Implementation
[ F.1.14.5 ]
Example
[ F.1.14.6 ]
Case studies
[ F.1.14.7 ]
See also
[ F.1.15 ]
fit_lfm_ols
[ F.1.15.1 ]
Summary
[ F.1.15.2 ]
Input
[ F.1.15.3 ]
Output
[ F.1.15.4 ]
Implementation
[ F.1.15.5 ]
Example
[ F.1.15.6 ]
Case studies
[ F.1.15.7 ]
References
[ F.1.15.8 ]
See also
[ F.1.16 ]
fit_lfm_pcfp
[ F.1.17 ]
fit_lfm_ridge
[ F.1.18 ]
fit_lfm_ridge_path
[ F.1.19 ]
fit_lfm_roblasso
[ F.1.19.1 ]
Example
[ F.1.20 ]
fit_locdisp_mlfp
[ F.1.20.1 ]
Summary
[ F.1.20.2 ]
Input
[ F.1.20.3 ]
Output
[ F.1.20.4 ]
Implementation
[ F.1.20.5 ]
Example
[ F.1.20.6 ]
Case studies
[ F.1.20.7 ]
See also
[ F.1.21 ]
fit_locdisp_mlfp_difflength
[ F.1.21.1 ]
Summary
[ F.1.21.2 ]
Input
[ F.1.21.3 ]
Output
[ F.1.21.4 ]
Implementation
[ F.1.21.5 ]
Tips
[ F.1.21.6 ]
Example
[ F.1.21.7 ]
Case studies
[ F.1.21.8 ]
References
[ F.1.21.9 ]
See also
[ F.1.22 ]
fit_trans_matrix_credit
[ F.1.22.1 ]
Summary
[ F.1.22.2 ]
Input
[ F.1.22.3 ]
Output
[ F.1.22.4 ]
Implementation
[ F.1.22.5 ]
Example
[ F.1.22.6 ]
Case studies
[ F.1.22.7 ]
References
[ F.1.22.8 ]
See also
[ F.1.23 ]
fit_state_space
[ F.1.23.1 ]
Summary
[ F.1.23.2 ]
Input
[ F.1.23.3 ]
Output
[ F.1.23.4 ]
Implementation
[ F.1.23.5 ]
Example
[ F.1.23.6 ]
Case studies
[ F.1.23.7 ]
References
[ F.1.23.8 ]
See also
[ F.1.24 ]
fit_t_dof
[ F.1.25 ]
fit_t_fp
[ F.1.26 ]
fit_var1
[ F.1.26.1 ]
Summary
[ F.1.26.2 ]
Input
[ F.1.26.3 ]
Output
[ F.1.26.4 ]
Implementation
[ F.1.26.5 ]
Example
[ F.1.26.6 ]
Case studies
[ F.1.26.7 ]
See also
[ F.1.27 ]
markov_network
[ F.1.28 ]
min_corr_toeplitz
[ F.1.28.1 ]
Summary
[ F.1.28.2 ]
Input
[ F.1.28.3 ]
Output
[ F.1.28.4 ]
Implementation
[ F.1.28.5 ]
Example
[ F.1.28.6 ]
Case studies
[ F.1.28.7 ]
References
[ F.1.29 ]
smooth_kernel_fp
[ F.1.29.1 ]
Summary
[ F.1.29.2 ]
Input
[ F.1.29.3 ]
Output
[ F.1.29.4 ]
Implementation
[ F.1.29.5 ]
Example
[ F.1.29.6 ]
Case studies
[ F.1.29.7 ]
References
[ F.1.29.8 ]
See also
[ F.1.30 ]
spectrum_shrink
[ F.1.31 ]
var2mvou
[ F.1.31.1 ]
Summary
[ F.1.31.2 ]
Input
[ F.1.31.3 ]
Output
[ F.1.31.4 ]
Implementation
[ F.1.31.5 ]
Tips
[ F.1.31.6 ]
Example
[ F.1.31.7 ]
Case studies
[ F.1.31.8 ]
References
[ F.1.32 ]
Generate FP profiles via multivariate Gaussian kernel: function implementation
[ F.1.33 ]
Expectation Maximization algorithm with flexible probabilities for Missing Values. Routine
[ F.1.34 ]
Homogeneous correlation cluster shrinkage
[ F.1.35 ]
Iterated generalized method of moments with flexible probabilities for the Poisson distribution
[ F.1.36 ]
Minimum volume ellipsoid enclosing data. Routine
[ F.1.37 ]
Farthest Outlier Detection. Routine
[ F.1.38 ]
High breakdown point with flexible probabilities. Routine
[ F.1.39 ]
Outlier detection with flexible probabilities. Routine
[ F.1.40 ]
SMTCovariance
[ F.1.41 ]
FitFractionalIntegration
[ F.1.42 ]
FitGenParetoMLFP
[ F.1.43 ]
NormalMixtureFit
[ F.1.44 ]
FitSkewtMLFP
[ F.1.45 ]
FitVar_ATMSVI
[ F.1.46 ]
GarchResiduals
[ F.1.47 ]
MMFP
[ F.1.48 ]
kMeansClustering
[ F.1.49 ]
QuantileGenParetoMLFP
[ F.1.50 ]
ObjectiveToeplitz
[ F.2 ]
Portfolio
[ F.2.1 ]
almgren_chriss
[ F.2.2 ]
char_portfolio
[ F.2.3 ]
effective_num_bets
[ F.2.4 ]
minimum_torsion
[ F.2.5 ]
obj_tracking_err
[ F.2.5.1 ]
Summary
[ F.2.5.2 ]
Input
[ F.2.5.3 ]
Output
[ F.2.5.4 ]
Implementation
[ F.2.5.5 ]
Example
[ F.2.5.6 ]
Case studies
[ F.2.5.7 ]
References
[ F.2.6 ]
opt_trade_meanvar
[ F.2.7 ]
spectral_index
[ F.2.7.1 ]
Summary
[ F.2.7.2 ]
Input
[ F.2.7.3 ]
Output
[ F.2.7.4 ]
Implementation
[ F.2.7.5 ]
Example
[ F.2.7.6 ]
Case studies
[ F.2.7.7 ]
References
[ F.2.8 ]
Profit-and-loss statistical features: function implementation
[ F.2.9 ]
EwmaIncludingStartingDays
[ F.2.10 ]
SolveGarleanuPedersen
[ F.2.11 ]
Leverage
[ F.2.12 ]
MomentumStrategy
[ F.3 ]
Pricing
[ F.3.1 ]
bond_value
[ F.3.1.1 ]
Summary
[ F.3.1.2 ]
Input
[ F.3.1.3 ]
Output
[ F.3.1.4 ]
Implementation
[ F.3.1.5 ]
Example
[ F.3.1.6 ]
Case studies
[ F.3.1.7 ]
References
[ F.3.1.8 ]
See also
[ F.3.2 ]
bootstrap_nelson_siegel
[ F.3.2.1 ]
Summary
[ F.3.2.2 ]
Input
[ F.3.2.3 ]
Output
[ F.3.2.4 ]
Implementation
[ F.3.2.5 ]
Example
[ F.3.2.6 ]
Case studies
[ F.3.2.7 ]
References
[ F.3.2.8 ]
See also
[ F.3.3 ]
bsm_function
[ F.3.3.1 ]
Summary
[ F.3.3.2 ]
Input
[ F.3.3.3 ]
Output
[ F.3.3.4 ]
Implementation
[ F.3.3.5 ]
Tips
[ F.3.3.6 ]
Example
[ F.3.3.7 ]
Case studies
[ F.3.3.8 ]
References
[ F.3.4 ]
call_option_value
[ F.3.4.1 ]
Summary
[ F.3.4.2 ]
Input
[ F.3.4.3 ]
Output
[ F.3.4.4 ]
Implementation
[ F.3.4.5 ]
Tips
[ F.3.4.6 ]
Example
[ F.3.4.7 ]
References
[ F.3.4.8 ]
See also
[ F.3.5 ]
cash_flow_reinv
[ F.3.5.1 ]
Summary
[ F.3.5.2 ]
Input
[ F.3.5.3 ]
Output
[ F.3.5.4 ]
Implementation
[ F.3.5.5 ]
Tips
[ F.3.5.6 ]
Example
[ F.3.5.7 ]
Case studies
[ F.3.5.8 ]
References
[ F.3.6 ]
fit_nelson_siegel_bonds
[ F.3.6.1 ]
Summary
[ F.3.6.2 ]
Input
[ F.3.6.3 ]
Output
[ F.3.6.4 ]
Implementation
[ F.3.6.5 ]
Tips
[ F.3.6.6 ]
Example
[ F.3.6.7 ]
References
[ F.3.6.8 ]
See also
[ F.3.7 ]
fit_nelson_siegel_yield
[ F.3.7.1 ]
Summary
[ F.3.7.2 ]
Input
[ F.3.7.3 ]
Output
[ F.3.7.4 ]
Implementation
[ F.3.7.5 ]
Tips
[ F.3.7.6 ]
Example
[ F.3.7.7 ]
Case studies
[ F.3.7.8 ]
References
[ F.3.7.9 ]
See also
[ F.3.8 ]
implvol_delta2m_moneyness
[ F.3.8.1 ]
Summary
[ F.3.8.2 ]
Input
[ F.3.8.3 ]
Output
[ F.3.8.4 ]
Implementation
[ F.3.8.5 ]
Example
[ F.3.8.6 ]
Case studies
[ F.3.8.7 ]
References
[ F.3.9 ]
ytm_shadowrates
[ F.3.9.1 ]
Summary
[ F.3.9.2 ]
Input
[ F.3.9.3 ]
Output
[ F.3.9.4 ]
Implementation
[ F.3.9.5 ]
Example
[ F.3.9.6 ]
Case studies
[ F.3.9.7 ]
References
[ F.3.10 ]
nelson_siegel_yield
[ F.3.10.1 ]
Summary
[ F.3.10.2 ]
Input
[ F.3.10.3 ]
Output
[ F.3.10.4 ]
Implementation
[ F.3.10.5 ]
Example
[ F.3.10.6 ]
Case studies
[ F.3.10.7 ]
References
[ F.3.11 ]
shadowrates_ytm
[ F.3.11.1 ]
Summary
[ F.3.11.2 ]
Input
[ F.3.11.3 ]
Output
[ F.3.11.4 ]
Implementation
[ F.3.11.5 ]
Example
[ F.3.11.6 ]
Case studies
[ F.3.11.7 ]
References
[ F.3.12 ]
numeraire_mre
[ F.3.12.1 ]
Summary
[ F.3.12.2 ]
Input
[ F.3.12.3 ]
Output
[ F.3.12.4 ]
Implementation
[ F.3.12.5 ]
Tips
[ F.3.12.6 ]
Example
[ F.3.12.7 ]
Case studies
[ F.3.12.8 ]
References
[ F.3.12.9 ]
See also
[ F.3.13 ]
zcb_value
[ F.3.13.1 ]
Summary
[ F.3.13.2 ]
Input
[ F.3.13.3 ]
Output
[ F.3.13.4 ]
Implementation
[ F.3.13.5 ]
Example
[ F.3.13.6 ]
Case studies
[ F.3.13.7 ]
References
[ F.3.13.8 ]
See also
[ F.3.14 ]
Vasicek yield curve
[ F.3.15 ]
regularized_payoff
[ F.3.15.1 ]
Summary
[ F.3.15.2 ]
Input
[ F.3.15.3 ]
Output
[ F.3.15.4 ]
Example
[ F.3.15.5 ]
Case studies
[ F.3.16 ]
Stochastic discount factor and kernel space: implementation
[ F.3.17 ]
BachelierCallPrice
[ F.3.18 ]
BondPriceNelSieg
[ F.3.19 ]
blsimpv
[ F.3.20 ]
FilterStochasticVolatility
[ F.3.21 ]
FitCIR_FP
[ F.3.22 ]
FitHeston
[ F.3.23 ]
FitStochasticVolatilityModel
[ F.3.24 ]
FitSigmaSVI
[ F.3.25 ]
FitVasicek
[ F.3.26 ]
HestonChFun
[ F.3.27 ]
CallPriceHestonFFT
[ F.3.28 ]
MapSVIparams
[ F.3.29 ]
SigmaSVI
[ F.3.30 ]
StochTime
[ F.3.31 ]
RollPrices2Prices
[ F.3.32 ]
ZCBondPriceVasicek
[ F.4 ]
Statistics
[ F.4.1 ]
bootstrap_hfp
[ F.4.1.1 ]
Summary
[ F.4.1.2 ]
Input
[ F.4.1.3 ]
Output
[ F.4.1.4 ]
Implementation
[ F.4.1.5 ]
Example
[ F.4.1.6 ]
Case studies
[ F.4.1.7 ]
References
[ F.4.2 ]
cdf_sp
[ F.4.2.1 ]
Summary
[ F.4.2.2 ]
Input
[ F.4.2.3 ]
Output
[ F.4.2.4 ]
Implementation
[ F.4.2.5 ]
Example
[ F.4.2.6 ]
Case studies
[ F.4.2.7 ]
References
[ F.4.3 ]
cop_marg_comb
[ F.4.3.1 ]
Summary
[ F.4.3.2 ]
Input
[ F.4.3.3 ]
Output
[ F.4.3.4 ]
Implementation
[ F.4.3.5 ]
Example
[ F.4.3.6 ]
References
[ F.4.3.7 ]
See also
[ F.4.4 ]
cop_marg_sep
[ F.4.4.1 ]
Summary
[ F.4.4.2 ]
Input
[ F.4.4.3 ]
Output
[ F.4.4.4 ]
Implementation
[ F.4.4.5 ]
Example
[ F.4.4.6 ]
References
[ F.4.4.7 ]
See also
[ F.4.5 ]
cornish_fisher
[ F.4.5.1 ]
Summary
[ F.4.5.2 ]
Input
[ F.4.5.3 ]
Output
[ F.4.5.4 ]
Implementation
[ F.4.5.5 ]
Example
[ F.4.5.6 ]
Case studies
[ F.4.6 ]
ewm_meancov
[ F.4.6.1 ]
Summary
[ F.4.6.2 ]
Input
[ F.4.6.3 ]
Output
[ F.4.6.4 ]
Implementation
[ F.4.6.5 ]
Example
[ F.4.6.6 ]
References
[ F.4.6.7 ]
See also
[ F.4.7 ]
gaussian_kernel
[ F.4.7.1 ]
Summary
[ F.4.7.2 ]
Input
[ F.4.7.3 ]
Output
[ F.4.7.4 ]
Implementation
[ F.4.7.5 ]
Example
[ F.4.7.6 ]
Case studies
[ F.4.7.7 ]
References
[ F.4.8 ]
invariance_test_copula
[ F.4.8.1 ]
Summary
[ F.4.8.2 ]
Input
[ F.4.8.3 ]
Output
[ F.4.8.4 ]
Implementation
[ F.4.8.5 ]
Example
[ F.4.8.6 ]
Case studies
[ F.4.8.7 ]
See also
[ F.4.9 ]
invariance_test_ellipsoid
[ F.4.9.1 ]
Summary
[ F.4.9.2 ]
Input
[ F.4.9.3 ]
Output
[ F.4.9.4 ]
Implementation
[ F.4.9.5 ]
Tips
[ F.4.9.6 ]
Example
[ F.4.9.7 ]
Case studies
[ F.4.9.8 ]
References
[ F.4.9.9 ]
See also
[ F.4.10 ]
invariance_test_ks
[ F.4.10.1 ]
Summary
[ F.4.10.2 ]
Input
[ F.4.10.3 ]
Output
[ F.4.10.4 ]
Implementation
[ F.4.10.5 ]
Example
[ F.4.10.6 ]
References
[ F.4.10.7 ]
See also
[ F.4.11 ]
kalman_filter
[ F.4.11.1 ]
Example
[ F.4.11.2 ]
References
[ F.4.12 ]
marchenko_pastur
[ F.4.12.1 ]
Summary
[ F.4.12.2 ]
Input
[ F.4.12.3 ]
Output
[ F.4.12.4 ]
Implementation
[ F.4.12.5 ]
Example
[ F.4.12.6 ]
Case studies
[ F.4.12.7 ]
References
[ F.4.13 ]
meancov_sp
[ F.4.13.1 ]
Summary
[ F.4.13.2 ]
Input
[ F.4.13.3 ]
Output
[ F.4.13.4 ]
Implementation
[ F.4.13.5 ]
Tips
[ F.4.13.6 ]
Example
[ F.4.13.7 ]
Case studies
[ F.4.13.8 ]
References
[ F.4.14 ]
meancov_inverse_wishart
[ F.4.14.1 ]
Summary
[ F.4.14.2 ]
Input
[ F.4.14.3 ]
Output
[ F.4.14.4 ]
Implementation
[ F.4.14.5 ]
Example
[ F.4.14.6 ]
Case studies
[ F.4.14.7 ]
References
[ F.4.15 ]
meancov_wishart
[ F.4.15.1 ]
Summary
[ F.4.15.2 ]
Input
[ F.4.15.3 ]
Output
[ F.4.15.4 ]
Implementation
[ F.4.15.5 ]
Example
[ F.4.15.6 ]
Case studies
[ F.4.15.7 ]
References
[ F.4.16 ]
moments_logn
[ F.4.17 ]
moments_mvou
[ F.4.17.1 ]
Summary
[ F.4.17.2 ]
Input
[ F.4.17.3 ]
Output
[ F.4.17.4 ]
Implementation
[ F.4.17.5 ]
Example
[ F.4.17.6 ]
Case studies
[ F.4.17.7 ]
References
[ F.4.18 ]
multi_r2
[ F.4.18.1 ]
Summary
[ F.4.18.2 ]
Input
[ F.4.18.3 ]
Output
[ F.4.18.4 ]
Implementation
[ F.4.18.5 ]
Example
[ F.4.18.6 ]
See also
[ F.4.19 ]
mvt_cdf
[ F.4.19.1 ]
Summary
[ F.4.19.2 ]
Input
[ F.4.19.3 ]
Output
[ F.4.19.4 ]
Implementation
[ F.4.19.5 ]
Tips
[ F.4.19.6 ]
Example
[ F.4.19.7 ]
Case study
[ F.4.19.8 ]
See also
[ F.4.20 ]
q_chi
[ F.4.20.1 ]
Summary
[ F.4.20.2 ]
Data
[ F.4.20.3 ]
Input
[ F.4.20.4 ]
Output
[ F.4.20.5 ]
Implementation
[ F.4.20.6 ]
Example
[ F.4.20.7 ]
Case study
[ F.4.20.8 ]
See also
[ F.4.21 ]
mvt_pdf
[ F.4.21.1 ]
Summary
[ F.4.21.2 ]
Input
[ F.4.21.3 ]
Output
[ F.4.21.4 ]
Implementation
[ F.4.21.5 ]
Example
[ F.4.21.6 ]
Case studies
[ F.4.21.7 ]
References
[ F.4.22 ]
mvt_logpdf
[ F.4.22.1 ]
Summary
[ F.4.22.2 ]
Input
[ F.4.22.3 ]
Output
[ F.4.22.4 ]
Implementation
[ F.4.22.5 ]
Example
[ F.4.22.6 ]
References
[ F.4.23 ]
normal_canonical
[ F.4.23.1 ]
Summary
[ F.4.23.2 ]
Input
[ F.4.23.3 ]
Output
[ F.4.23.4 ]
Implementation
[ F.4.23.5 ]
Example
[ F.4.23.6 ]
Case studies
[ F.4.23.7 ]
References
[ F.4.24 ]
objective_r2
[ F.4.24.1 ]
Summary
[ F.4.24.2 ]
Input
[ F.4.24.3 ]
Output
[ F.4.24.4 ]
Implementation
[ F.4.24.5 ]
Example
[ F.4.24.6 ]
Case studies
[ F.4.24.7 ]
See also
[ F.4.25 ]
plot_kstest
[ F.4.26 ]
pdf_sp
[ F.4.26.1 ]
Summary
[ F.4.26.2 ]
Input
[ F.4.26.3 ]
Output
[ F.4.26.4 ]
Implementation
[ F.4.26.5 ]
Example
[ F.4.26.6 ]
Case studies
[ F.4.26.7 ]
References
[ F.4.27 ]
project_trans_matrix
[ F.4.27.1 ]
Summary
[ F.4.27.2 ]
Output
[ F.4.27.3 ]
Implementation
[ F.4.27.4 ]
Example
[ F.4.27.5 ]
Case studies
[ F.4.27.6 ]
References
[ F.4.27.7 ]
See also
[ F.4.28 ]
quantile_sp
[ F.4.28.1 ]
Summary
[ F.4.28.2 ]
Input
[ F.4.28.3 ]
Output
[ F.4.28.4 ]
Implementation
[ F.4.28.5 ]
Example
[ F.4.28.6 ]
Case studies
[ F.4.28.7 ]
References
[ F.4.29 ]
saddle_point_quadn
[ F.4.29.1 ]
Summary
[ F.4.29.2 ]
Input
[ F.4.29.3 ]
Output
[ F.4.29.4 ]
Implementation
[ F.4.29.5 ]
Tips
[ F.4.29.6 ]
Example
[ F.4.29.7 ]
Case studies
[ F.4.29.8 ]
References
[ F.4.29.9 ]
See also
[ F.4.30 ]
schweizer_wolff
[ F.4.30.1 ]
Summary
[ F.4.30.2 ]
Input
[ F.4.30.3 ]
Output
[ F.4.30.4 ]
Implementation
[ F.4.30.5 ]
Example
[ F.4.30.6 ]
Case studies
[ F.4.30.7 ]
See also
[ F.4.31 ]
scoring
[ F.4.31.1 ]
Summary
[ F.4.31.2 ]
Input
[ F.4.31.3 ]
Output
[ F.4.31.4 ]
Implementation
[ F.4.31.5 ]
Example
[ F.4.31.6 ]
Case studies
[ F.4.31.7 ]
References
[ F.4.31.8 ]
See also
[ F.4.32 ]
simulate_garch
[ F.4.32.1 ]
Summary
[ F.4.32.2 ]
Input
[ F.4.32.3 ]
Output
[ F.4.32.4 ]
Implementation
[ F.4.32.5 ]
Example
[ F.4.32.6 ]
See also
[ F.4.33 ]
simulate_niw
[ F.4.33.1 ]
Example
[ F.4.34 ]
simulate_bm
[ F.4.34.1 ]
Summary
[ F.4.34.2 ]
Input
[ F.4.34.3 ]
Output
[ F.4.34.4 ]
Implementation
[ F.4.34.5 ]
Example
[ F.4.34.6 ]
Case studies
[ F.4.34.7 ]
References
[ F.4.34.8 ]
See also
[ F.4.35 ]
simulate_markov_chain_univ
[ F.4.35.1 ]
Summary
[ F.4.35.2 ]
Input
[ F.4.35.3 ]
Output
[ F.4.35.4 ]
Implementation
[ F.4.35.5 ]
Example
[ F.4.35.6 ]
Case studies
[ F.4.35.7 ]
References
[ F.4.35.8 ]
See also
[ F.4.36 ]
simulate_markov_chain_multiv
[ F.4.36.1 ]
Summary
[ F.4.36.2 ]
Input
[ F.4.36.3 ]
Output
[ F.4.36.4 ]
Implementation
[ F.4.36.5 ]
Example
[ F.4.36.6 ]
Case studies
[ F.4.36.7 ]
References
[ F.4.36.8 ]
See also
[ F.4.37 ]
simulate_mvou
[ F.4.37.1 ]
Summary
[ F.4.37.2 ]
Input
[ F.4.37.3 ]
Output
[ F.4.37.4 ]
Implementation
[ F.4.37.5 ]
Tips
[ F.4.37.6 ]
Example
[ F.4.37.7 ]
Case studies
[ F.4.37.8 ]
References
[ F.4.37.9 ]
See also
[ F.4.38 ]
simulate_normal
[ F.4.38.1 ]
Summary
[ F.4.38.2 ]
Input
[ F.4.38.3 ]
Output
[ F.4.38.4 ]
Implementation
[ F.4.38.5 ]
Tips
[ F.4.38.6 ]
Example
[ F.4.38.7 ]
Case studies
[ F.4.38.8 ]
References
[ F.4.38.9 ]
See also
[ F.4.39 ]
simulate_normal_dimred
[ F.4.39.1 ]
Example
[ F.4.40 ]
simulate_quadn
[ F.4.40.1 ]
Summary
[ F.4.40.2 ]
Input
[ F.4.40.3 ]
Output
[ F.4.40.4 ]
Implementation
[ F.4.40.5 ]
Example
[ F.4.40.6 ]
Case studies
[ F.4.40.7 ]
References
[ F.4.40.8 ]
See also
[ F.4.41 ]
simulate_rw_hfp
[ F.4.41.1 ]
Summary
[ F.4.41.2 ]
Input
[ F.4.41.3 ]
Output
[ F.4.41.4 ]
Implementation
[ F.4.41.5 ]
Example
[ F.4.41.6 ]
Case studies
[ F.4.41.7 ]
References
[ F.4.41.8 ]
See also
[ F.4.42 ]
simulate_elliptical
[ F.4.42.1 ]
Summary
[ F.4.42.2 ]
Input
[ F.4.42.3 ]
Output
[ F.4.42.4 ]
Implementation
[ F.4.42.5 ]
Tips
[ F.4.42.6 ]
Example
[ F.4.42.7 ]
Case studies
[ F.4.42.8 ]
References
[ F.4.42.9 ]
See also
[ F.4.43 ]
simulate_t
[ F.4.43.1 ]
Summary
[ F.4.43.2 ]
Input
[ F.4.43.3 ]
Output
[ F.4.43.4 ]
Implementation
[ F.4.43.5 ]
Tips
[ F.4.43.6 ]
Example
[ F.4.43.7 ]
See also
[ F.4.44 ]
simulate_var1
[ F.4.44.1 ]
Summary
[ F.4.44.2 ]
Input
[ F.4.44.3 ]
Output
[ F.4.44.4 ]
Implementation
[ F.4.44.5 ]
Example
[ F.4.44.6 ]
Case studies
[ F.4.44.7 ]
References
[ F.4.44.8 ]
See also
[ F.4.45 ]
simulate_wishart
[ F.4.45.1 ]
Summary
[ F.4.45.2 ]
Input
[ F.4.45.3 ]
Output
[ F.4.45.4 ]
Implementation
[ F.4.45.5 ]
Example
[ F.4.45.6 ]
Case studies
[ F.4.45.7 ]
References
[ F.4.45.8 ]
See also
[ F.4.46 ]
smoothing
[ F.4.46.1 ]
Summary
[ F.4.46.2 ]
Input
[ F.4.46.3 ]
Output
[ F.4.46.4 ]
Implementation
[ F.4.46.5 ]
Example
[ F.4.46.6 ]
Case studies
[ F.4.46.7 ]
References
[ F.4.46.8 ]
See also
[ F.4.47 ]
twist_prob_mom_match
[ F.4.47.1 ]
Summary
[ F.4.47.2 ]
Input
[ F.4.47.3 ]
Output
[ F.4.47.4 ]
Implementation
[ F.4.47.5 ]
Tips
[ F.4.47.6 ]
Example
[ F.4.47.7 ]
Case studies
[ F.4.47.8 ]
References
[ F.4.47.9 ]
See also
[ F.4.48 ]
twist_scenarios_mom_match
[ F.4.48.1 ]
Summary
[ F.4.48.2 ]
Input
[ F.4.48.3 ]
Output
[ F.4.48.4 ]
Implementation
[ F.4.48.5 ]
Example
[ F.4.48.6 ]
Case studies
[ F.4.48.7 ]
References
[ F.4.48.8 ]
See also
[ F.4.49 ]
Flexible probabilities and scenarios of panic distribution
[ F.4.50 ]
norm_cop_pdf
[ F.4.50.1 ]
Summary
[ F.4.50.2 ]
Input
[ F.4.50.3 ]
Output
[ F.4.50.4 ]
Implementation
[ F.4.50.5 ]
Example
[ F.4.50.6 ]
Case studies
[ F.4.51 ]
t_cop_pdf
[ F.4.51.1 ]
Summary
[ F.4.51.2 ]
Input
[ F.4.51.3 ]
Output
[ F.4.51.4 ]
Implementation
[ F.4.51.5 ]
Example
[ F.4.51.6 ]
Case studies
[ F.4.52 ]
Elliptical Copula Scenarios via dimension reduction function
[ F.4.53 ]
Normalized empirical histogram of scenario-probability distribution: function implementation
[ F.4.54 ]
Metropolis-Hastings algorithm: implementation
[ F.4.55 ]
Projection to horizon of central moments. Routine
[ F.4.56 ]
Normal innovation function
[ F.4.57 ]
Projection of the pdf via DFT: function implementation
[ F.4.58 ]
GHCalibration
[ F.4.59 ]
Stats
[ F.4.60 ]
Raw2Cumul
[ F.4.61 ]
PnlVolatility_ewma
[ F.4.62 ]
ffgn
[ F.4.63 ]
Central2Raw
[ F.4.64 ]
CentralAndStandardizedStatistics
[ F.4.65 ]
Schout2ConTank
[ F.4.66 ]
ProjectionStudentT
[ F.4.67 ]
QuantileMixture
[ F.4.68 ]
ScoringAssessmentFP
[ F.4.69 ]
SharpeRatio
[ F.4.70 ]
PathsCauchy
[ F.4.71 ]
SimulateCompPoisson
[ F.4.72 ]
JumpDiffusionKou
[ F.4.73 ]
JumpDiffusionMerton
[ F.4.74 ]
NIG
[ F.4.75 ]
VGpdf
[ F.4.76 ]
SmoothStep
[ F.4.77 ]
SpinOutlier
[ F.4.78 ]
ShiftedVGMoments
[ F.4.79 ]
ParamChangeVG
[ F.4.80 ]
VG
[ F.5 ]
Views
[ F.5.1 ]
black_litterman
[ F.5.2 ]
min_rel_entropy_normal
[ F.5.3 ]
min_rel_entropy_sp
[ F.5.3.1 ]
Summary
[ F.5.3.2 ]
Input
[ F.5.3.3 ]
Output
[ F.5.3.4 ]
Implementation
[ F.5.3.5 ]
Example
[ F.5.3.6 ]
Case studies
[ F.5.3.7 ]
References
[ F.5.3.8 ]
See also
[ F.5.4 ]
rel_entropy_normal
[ F.5.4.1 ]
Summary
[ F.5.4.2 ]
Input
[ F.5.4.3 ]
Output
[ F.5.4.4 ]
Implementation
[ F.5.4.5 ]
Tips
[ F.5.4.6 ]
Example
[ F.5.4.7 ]
Case studies
[ F.5.4.8 ]
References
[ F.5.5 ]
Relative entropy function
[ F.5.6 ]
Signal-to-noise constraint function
[ F.5.7 ]
Copula opinion pooling function
[ F.6 ]
Tools
[ F.6.1 ]
adjusted_value
[ F.6.1.1 ]
Summary
[ F.6.1.2 ]
Input
[ F.6.1.3 ]
Output
[ F.6.1.4 ]
Implementation
[ F.6.1.5 ]
Tips
[ F.6.1.6 ]
Example
[ F.6.1.7 ]
Case studies
[ F.6.1.8 ]
References
[ F.6.2 ]
aggregate_rating_migrations
[ F.6.2.1 ]
Summary
[ F.6.2.2 ]
Input
[ F.6.2.3 ]
Output
[ F.6.2.4 ]
Implementation
[ F.6.2.5 ]
Example
[ F.6.2.6 ]
Case studies
[ F.6.3 ]
backward_selection
[ F.6.3.1 ]
Summary
[ F.6.3.2 ]
Input
[ F.6.3.3 ]
Output
[ F.6.3.4 ]
Implementation
[ F.6.3.5 ]
Example
[ F.6.3.6 ]
Case studies
[ F.6.4 ]
colormap_fp
[ F.6.4.1 ]
Summary
[ F.6.4.2 ]
Input
[ F.6.4.3 ]
Output
[ F.6.4.4 ]
Example
[ F.6.4.5 ]
See also
[ F.6.5 ]
cpca_cov
Quantitative finance
Data science Featured case studies Functions documentation
Quantitative finance Data science Featured case studies Functions documentation