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Quant Bootcamp

Onsite, live stream or on demand
Intensive onsite, intensive online or on demand training on data science for finance, quantitative risk modeling and portfolio construction
New York, August 10 - 15, 2020 | On Demand Any Time

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Quant Marathon

In-depth, master-level online program in modern quantitative finance in 4 core courses, with emphasis on data science

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Short math/coding online courses to prepare for the Quant Bootcamp or the Quant Marathon

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Scripts
S.I. The “Checklist”
[ S.0 ] Executive summary
[ S.0.1 ] s_checklist_executive_summary
[ S.0.1.1 ] Summary
[ S.0.1.2 ] Data
[ S.0.1.3 ] Parameters
[ S.0.1.4 ] Outcomes and figures
[ S.0.1.5 ] Implementation
[ S.0.1.6 ] 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.1.20 ] s_checklist_scenariobased_step01
[ S.1.20.1 ] Summary
[ S.1.20.2 ] Data
[ S.1.20.3 ] Parameters
[ S.1.20.4 ] Outcomes and figures
[ S.1.20.5 ] Implementation
[ S.1.20.6 ] See also
[ 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.2.37 ] s_checklist_scenariobased_step02
[ S.2.37.1 ] Summary
[ S.2.37.2 ] Data
[ S.2.37.3 ] Parameters
[ S.2.37.4 ] Outcomes and figures
[ S.2.37.5 ] Implementation
[ S.2.37.6 ] See also
[ 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_glivenko_cantelli
[ 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_glivenko_cantelli_hfp
[ 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 ] HFP ellipsoid dependence on flexible probabilities
[ S.3.9 ] HFP quantile dependence on flexible probabilities
[ S.3.10 ] s_ens_two_scenarios
[ 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 ] Quantile estimation: maximum likelihood vs non-parametric
[ S.3.12 ] Consistency of the maximum likelihood estimator: Student t case study
[ S.3.13 ] MLFP estimators for elliptical variables: numerical study on the Hessian matrix of Student t distribution
[ S.3.14 ] MLFP estimators for the Student t distribution. Application
[ S.3.15 ] s_mlfp_ellipsoid_convergence
[ S.3.15.1 ] Summary
[ S.3.15.2 ] Data
[ S.3.15.3 ] Parameters
[ S.3.15.4 ] Outcomes and figures
[ S.3.15.5 ] Implementation
[ S.3.15.6 ] See also
[ S.3.16 ] MLFP quantile dependence on flexible probabilities in extreme value theory
[ S.3.17 ] Quantile function tail approximation
[ S.3.18 ] Non-robustness of sample mean and sample covariance
[ S.3.19 ] Jackknife test on sample mean and covariance
[ S.3.20 ] Sample mean and sample median breakdown point
[ S.3.21 ] Minimum volume ellipsoid enclosing data
[ S.3.22 ] Farthest outlier detection
[ S.3.23 ] High breakdown point with flexible probabilities estimators
[ S.3.24 ] Robustness comparison between HFP and HBFP-estimators
[ S.3.25 ] Method of moments with flexible probabilities: reflected shifted lognormal case study
[ S.3.26 ] Generalized method of moments with flexible probabilities: Poisson distribution
[ S.3.27 ] P&L unconditional distribution
[ S.3.28 ] MLFP estimation of unconditional distribution: properties
[ S.3.29 ] MLFP estimation of unconditional distribution: skew-t fit
[ S.3.30 ] Non-synchronous data
[ S.3.31 ] Outlier detection with flexible probabilities
[ S.3.32 ] Expectation-maximization with flexible probabilities for missing values. Application
[ S.3.33 ] s_different_length_series
[ S.3.33.1 ] Summary
[ S.3.33.2 ] Data
[ S.3.33.3 ] Parameters
[ S.3.33.4 ] Outcomes and figures
[ S.3.33.5 ] Implementation
[ S.3.33.6 ] See also
[ S.3.34 ] Proxies
[ S.3.35 ] Fix non-synchroneity in HFP
[ S.3.36 ] Flexible probabilities ensemble posterior and classical equivalent
[ S.3.37 ] Hellinger distance and diversity indicator
[ S.3.38 ] Ensemble FP: curse of dimensionality?
[ S.3.39 ] Likelihood with flexible probabilities of GARCH(1,1)
[ S.3.40 ] Alternative FP specifications: bootstrap
[ S.3.41 ] Alternative FP specifications: Dirichlet distribution
[ S.3.42 ] s_estimation_copmarg_calloption
[ S.3.42.1 ] Script summary
[ S.3.42.2 ] Data
[ S.3.42.3 ] Parameters
[ S.3.42.4 ] Outcomes and figures
[ S.3.42.5 ] Implementation
[ S.3.42.6 ] See also
[ S.3.43 ] s_estimation_copmarg_ratings
[ S.3.43.1 ] Script summary
[ S.3.43.2 ] Data
[ S.3.43.3 ] Parameters
[ S.3.43.4 ] Outcomes and figures
[ S.3.43.5 ] Implementation
[ S.3.43.6 ] See also
[ S.3.44 ] Copula-marginal estimation for invariants of different asset classes
[ S.3.45 ] Standardization of invariants’ marginals into uniform realizations: case study
[ S.3.46 ] Copula-marginal distribution
[ S.3.47 ] s_dcc_fit
[ S.3.47.1 ] Summary
[ S.3.47.2 ] Data
[ S.3.47.3 ] Parameters
[ S.3.47.4 ] Outcomes and figures
[ S.3.47.5 ] Implementation
[ S.3.47.6 ] See also
[ S.3.48 ] Covariance normalization
[ S.3.49 ] s_ewm_statistics
[ S.3.49.1 ] Script 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 ] s_ewm_bf_statistics
[ S.3.50.1 ] Script summary
[ S.3.50.2 ] Data
[ S.3.50.3 ] Parameters
[ S.3.50.4 ] Outcomes and figures
[ S.3.50.5 ] Implementation
[ S.3.50.6 ] See also
[ S.3.51 ] s_shrinkage_location
[ S.3.51.1 ] Summary
[ S.3.51.2 ] Data
[ S.3.51.3 ] Parameters
[ S.3.51.4 ] Outcomes and figures
[ S.3.51.5 ] Implementation
[ S.3.52 ] s_shrinkage_factor
[ S.3.52.1 ] 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_shrink_corr_clusters
[ S.3.53.1 ] Summary
[ S.3.53.2 ] Datas
[ S.3.53.3 ] Outcomes and figures
[ S.3.53.4 ] Implementation
[ S.3.54 ] Homogeneous cluster shrinkage
[ S.3.55 ] Graphical lasso estimation of the correlation and inverse correlation matrix
[ S.3.56 ] Random matrix theory: Marchenko-Pastur limit
[ S.3.57 ] Numerical integration of Marchenko-Pastur distribution
[ S.3.58 ] Spectrum of the covariance matrix: normal noise
[ S.3.59 ] Spectrum of the covariance matrix: exponential noise
[ S.3.60 ] s_shrink_spectrum_filt
[ S.3.60.1 ] Summary
[ S.3.60.2 ] Data
[ S.3.60.3 ] Parameters
[ S.3.60.4 ] Outcomes and figures
[ S.3.60.5 ] Implementation
[ S.3.60.6 ] See also
[ S.3.61 ] Sparse matrix transformation shrinkage
[ S.3.62 ] Shrinkage estimator of location: implementation
[ S.3.63 ] Sample covariance and eigenvalue dispersion
[ S.3.64 ] Shrinkage estimator of dispersion: implementation
[ S.3.65 ] Random matrix theory: Wigner semicircle law
[ S.3.66 ] Shrinkage estimators of location: assessment
[ S.3.67 ] s_bayes_prior_niw
[ S.3.67.1 ] Summary
[ S.3.67.2 ] Parameters
[ S.3.67.3 ] Outcomes and figures
[ S.3.67.4 ] Implementation
[ S.3.68 ] s_bayes_posterior_niw
[ S.3.68.1 ] Summary
[ S.3.68.2 ] Parameters
[ S.3.68.3 ] Outcomes and figures
[ S.3.68.4 ] Implementation
[ S.3.68.5 ] See also
[ S.3.69 ] s_bayesian_estimation
[ S.3.70 ] Markov chain Monte Carlo: application of Metropolis-Hastings algorithm
[ S.3.71 ] Agnostic prior on correlation
[ S.3.72 ] s_location_estimators
[ S.3.72.1 ] Summary
[ S.3.72.2 ] Parameters
[ S.3.72.3 ] Outcomes and figures
[ S.3.72.4 ] Implementation
[ S.3.72.5 ] See also
[ S.3.73 ] s_sample_mean_covariance
[ S.3.73.1 ] Summary
[ S.3.73.2 ] Parameters
[ S.3.73.3 ] Outcomes and figures
[ S.3.73.4 ] Implementation
[ S.3.73.5 ] See also
[ S.3.74 ] Estimation assessment of a moment-based functional of a mixture I
[ S.3.75 ] Estimation assessment of a moment-based functional of a mixture II
[ S.3.76 ] Estimation assessment of the quantile of a mixture I
[ S.3.77 ] Estimation assessment of the quantile of a mixture II
[ S.3.78 ] s_location_stress_error
[ S.3.78.1 ] Summary
[ S.3.78.2 ] Parameters
[ S.3.78.3 ] Outcomes and figures
[ S.3.78.4 ] Implementation
[ S.3.78.5 ] See also
[ S.3.79 ] Estimation assessment
[ S.3.80 ] Sample mean and sample covariance estimation error
[ S.3.81 ] Sample quantiles distribution
[ S.3.82 ] Distribution of conditioning variable for an aggregate of clusters
[ S.3.83 ] s_checklist_scenariobased_step03
[ S.3.83.1 ] Summary
[ S.3.83.2 ] Data
[ S.3.83.3 ] Parameters
[ S.3.83.4 ] Outcomes and figures
[ S.3.83.5 ] Implementation
[ S.3.83.6 ] See also
[ 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_calloption
[ 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 ] Projection of cluster aggregates: case study
[ S.4.14 ] Projection of risk drivers whose invariants stem from marginal GARCH(1,1) fit and joint DCC fit: stocks case study
[ S.4.15 ] s_projection_stock_hfp
[ S.4.15.1 ] Summary
[ S.4.15.2 ] Data
[ S.4.15.3 ] Parameters
[ S.4.15.4 ] Outcomes and figures
[ S.4.15.5 ] Implementation
[ S.4.15.6 ] See also
[ S.4.16 ] s_projection_stock_bootstrap
[ 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 ] Projection via historical approach: call option risk drivers
[ S.4.18 ] Historical projection: bootstrap
[ S.4.19 ] Projection via historical bootstrapping: case study
[ S.4.20 ] Hybrid Monte Carlo-historical projection: case study
[ S.4.21 ] Hybrid historical and Monte Carlo projection: modeling default via a one-factor normal copula
[ S.4.22 ] Copula-marginal projection for risk drivers of different asset classes
[ S.4.23 ] Risk propagation in the Heston process
[ S.4.24 ] s_checklist_scenariobased_steps03_04_historical
[ S.4.24.1 ] Summary
[ S.4.24.2 ] Data
[ S.4.24.3 ] Parameters
[ S.4.24.4 ] Outcomes and figures
[ S.4.24.5 ] Implementation
[ S.4.24.6 ] See also
[ S.4.25 ] s_checklist_scenariobased_step04
[ S.4.25.1 ] Summary
[ S.4.25.2 ] Data
[ S.4.25.3 ] Parameters
[ S.4.25.4 ] Outcomes and figures
[ S.4.25.5 ] Implementation
[ S.4.25.6 ] See also
[ 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 ] Equity P&L
[ S.5.3 ] Equity P&L in base currency
[ S.5.4 ] Zero-coupon bond value
[ 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 ] Taylor approximation of an equity P&L
[ 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.5.23 ] s_checklist_scenariobased_step05
[ S.5.23.1 ] Summary
[ S.5.23.2 ] Data
[ S.5.23.3 ] Parameters
[ S.5.23.4 ] Outcomes and figures
[ S.5.23.5 ] Implementation
[ S.5.23.6 ] See also
[ 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.6.10 ] s_checklist_scenariobased_step06
[ S.6.10.1 ] Script summary
[ S.6.10.2 ] Data
[ S.6.10.3 ] Parameters
[ S.6.10.4 ] Outcomes and figures
[ S.6.10.5 ] Implementation
[ S.6.10.6 ] See also
[ S.7 ] Ex-ante evaluation
[ S.7.1 ] s_evaluation_certainty_equiv
[ S.7.1.1 ] Summary
[ S.7.1.2 ] Data
[ S.7.1.3 ] Parameters
[ S.7.1.4 ] Outcomes and figures
[ S.7.1.5 ] Implementation
[ S.7.1.6 ] 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 ] Step 9
[ S.7.2.7 ] Step 11
[ S.7.2.8 ] 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 ] Cornish Fisher expansion: an application
[ S.7.5 ] s_evaluation_eco_cap
[ S.7.5.1 ] Summary
[ S.7.5.2 ] Data
[ S.7.5.3 ] Parameters
[ S.7.5.4 ] Outcomes
[ S.7.5.5 ] Implementation
[ S.7.5.6 ] See also
[ S.7.6 ] s_evaluation_cornishfisher_stocks
[ S.7.6.1 ] Summary
[ S.7.6.2 ] Data
[ S.7.6.3 ] Parameters
[ S.7.6.4 ] Outcomes and figures
[ S.7.6.5 ] Implementation
[ S.7.6.6 ] See also
[ S.7.7 ] The mean-lower partial moment trade-off is not co-monotonic additive: practice
[ S.7.8 ] s_checklist_scenariobased_step07
[ S.7.8.1 ] Script summary
[ S.7.8.2 ] Data
[ S.7.8.3 ] Parameters
[ S.7.8.4 ] Outcomes and figures
[ S.7.8.5 ] Implementation
[ S.7.8.6 ] See also
[ S.8a ] Ex-ante attribution: performance
[ S.8a.1 ] s_attribution_norm
[ S.8a.1.1 ] Summary
[ S.8a.1.2 ] Data
[ S.8a.1.3 ] Outcomes and figures
[ S.8a.1.4 ] Implementation
[ S.8a.1.5 ] See also
[ S.8a.2 ] s_attribution_scen_prob
[ S.8a.2.1 ] Summary
[ S.8a.2.2 ] Parameters
[ S.8a.2.3 ] Outcomes and figures
[ S.8a.2.4 ] Implementation
[ S.8a.2.5 ] 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_norm
[ 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.2 ] s_risk_attribution_scen_prob
[ 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.2.6 ] See also
[ 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.8b.5 ] s_checklist_scenariobased_step08
[ S.8b.5.1 ] Script summary
[ S.8b.5.2 ] Data
[ S.8b.5.3 ] Parameters
[ S.8b.5.4 ] Outcomes and figures
[ S.8b.5.5 ] Implementation
[ S.8b.5.6 ] 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 ] s_checklist_scenariobased_step09
[ S.9a.4.1 ] Script summary
[ S.9a.4.2 ] Data
[ S.9a.4.3 ] Parameters
[ S.9a.4.4 ] Outcomes and figures
[ S.9a.4.5 ] Implementation
[ S.9a.4.6 ] See also
[ S.9a.5 ] 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.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.10.12 ] s_checklist_scenariobased_step10
[ S.10.12.1 ] Script summary
[ S.10.12.2 ] Data
[ S.10.12.3 ] Parameters
[ S.10.12.4 ] Outcomes and figures
[ S.10.12.5 ] Implementation
[ S.10.12.6 ] See also
S.II. Factor models and learning
[ S.11 ] Executive summary
[ S.11.1 ] s_lfm_executive_summary
[ S.11.1.1 ] Summary
[ S.11.1.2 ] Data
[ S.11.1.3 ] Parameters
[ S.11.1.4 ] Outcomes and figures
[ S.11.1.5 ] Implementation
[ S.11.1.6 ] See also
[ S.12 ] Linear factor models
[ S.12.1 ] s_normal_mean_regression
[ S.12.1.1 ] Summary
[ S.12.1.2 ] Parameters
[ S.12.1.3 ] Outcomes and figures
[ S.12.1.4 ] Implementation
[ S.12.1.5 ] See also
[ S.12.2 ] s_regression_lfm
[ S.12.2.1 ] Summary
[ S.12.2.2 ] Parameters
[ S.12.2.3 ] Outcomes and figures
[ S.12.2.4 ] Implementation
[ S.12.2.5 ] See also
[ S.12.3 ] Comparison of LFM’s
[ S.12.4 ] Symmetric regression: numerical example
[ S.12.5 ] s_principal_component_lfm
[ S.12.5.1 ] Summary
[ S.12.5.2 ] Parameters
[ S.12.5.3 ] Outcomes and figures
[ S.12.5.4 ] Implementation
[ S.12.5.5 ] See also
[ S.12.6 ] Principal-component LFM’s: joint distribution of residuals and factors
[ S.12.7 ] s_systematic_idiosyncratic_lfm
[ S.12.7.1 ] Summary
[ S.12.7.2 ] Parameters
[ S.12.7.3 ] Outcomes and figures
[ S.12.7.4 ] Implementation
[ S.12.7.5 ] See also
[ S.12.8 ] s_cross_section_lfm
[ S.12.8.1 ] Summary
[ S.12.8.2 ] Data
[ S.12.8.3 ] Parameters
[ S.12.8.4 ] Outcomes and figures
[ S.12.8.5 ] Implementation
[ S.12.9 ] s_factor_replication_logn
[ S.12.9.1 ] Summary
[ S.12.9.2 ] Parameters
[ S.12.9.3 ] Outcomes and figures
[ S.12.9.4 ] Implementation
[ S.12.9.5 ] See also
[ S.12.10 ] Factor-replicating portfolio convergence under normal factors and residuals
[ S.12.11 ] Cross-sectional LFM’s loadings versus regression LFM’s loadings
[ S.12.12 ] Cross-sectional factor extraction matrix is variance-minimizing: numerical example
[ S.12.13 ] Cross-sectional LFM’s optimal r-squared: numerical example
[ S.12.14 ] Correlation of residuals in generalized cross-sectional LFM
[ S.12.15 ] Factor extraction: numerical test
[ S.12.16 ] Hidden factors: puzzle
[ S.12.17 ] Hidden regression versus Principal-component LFM’s: numerical example
[ S.12.18 ] Correlation of residuals in generalized regression LFM’s
[ S.12.19 ] Fully observable factor model
[ S.12.20 ] Fully observable models: study of factor-residual distribution
[ S.12.21 ] Cross-sectional LFM’s: generalized cross-sectional industry factors
[ S.12.22 ] Regression LFM’s: shifted-lognormal regression II
[ S.12.23 ] s_reg_truncated_lfm
[ S.12.23.1 ] Summary
[ S.12.23.2 ] Data
[ S.12.23.3 ] Parameters
[ S.12.23.4 ] Outcomes and figures
[ S.12.23.5 ] Implementation
[ S.12.23.6 ] See also
[ S.12.24 ] s_pca_truncated_lfm
[ S.12.24.1 ] Summary
[ S.12.24.2 ] Data
[ S.12.24.3 ] Parameters
[ S.12.24.4 ] Outcomes and figures
[ S.12.24.5 ] Implementation
[ S.12.24.6 ] See also
[ S.12.25 ] s_cross_section_truncated_lfm
[ S.12.25.1 ] Summary
[ S.12.25.2 ] Data
[ S.12.25.3 ] Parameters
[ S.12.25.4 ] Outcomes and figures
[ S.12.25.5 ] Implementation
[ S.12.25.6 ] See also
[ S.12.26 ] Simulation of the distribution of statistics of regression parameters
[ S.12.27 ] Regression LFM’s: truncated estimations
[ S.12.28 ] Regression LFM’s: generalized regression industry factors
[ S.13 ] Machine learning foundations
[ S.14 ] Point machine learning models
[ S.14.1 ] s_logn_mean_regression
[ S.14.1.1 ] Summary
[ S.14.1.2 ] Parameters
[ S.14.1.3 ] Outcomes and figures
[ S.14.1.4 ] Implementation
[ S.14.1.5 ] See also
[ S.14.2 ] s_call_mean_regression
[ S.14.2.1 ] Summary
[ S.14.2.2 ] Data
[ S.14.2.3 ] Outcomes and figures
[ S.14.2.4 ] Implementation
[ S.14.2.5 ] See also
[ S.14.3 ] s_call_mean_lin_regression
[ S.14.3.1 ] Summary
[ S.14.3.2 ] Data
[ S.14.3.3 ] Outcomes and figures
[ S.14.3.4 ] Implementation
[ S.14.3.5 ] See also
[ S.14.4 ] s_logn_mean_lin_regression
[ S.14.4.1 ] Summary
[ S.14.4.2 ] Parameters
[ S.14.4.3 ] Outcomes and figures
[ S.14.4.4 ] Implementation
[ S.14.4.5 ] See also
[ S.14.5 ] s_continuum_discrete_point_pred
[ S.14.5.1 ] Summary
[ S.14.5.2 ] Parameters
[ S.14.5.3 ] Outcomes and figures
[ S.14.5.4 ] Implementation
[ S.14.5.5 ] See also
[ S.14.6 ] s_sp_anova
[ S.14.6.1 ] Summary
[ S.14.6.2 ] Data
[ S.14.6.3 ] Parameters
[ S.14.6.4 ] Outcomes and figures
[ S.14.6.5 ] Implementation
[ S.14.6.6 ] See also
[ S.14.7 ] s_interactions
[ S.14.7.1 ] Summary
[ S.14.7.2 ] Input parameters
[ S.14.7.3 ] Outcomes and figures
[ S.14.7.4 ] Implementation
[ S.14.7.5 ] See also
[ S.14.8 ] s_kernel_trick
[ S.14.9 ] s_cart
[ S.14.9.1 ] Summary
[ S.14.9.2 ] Data
[ S.14.9.3 ] Parameters
[ S.14.9.4 ] Outcomes and figures
[ S.14.9.5 ] Implementation
[ S.14.10 ] s_ann_regression
[ S.14.10.1 ] Summary
[ S.14.10.2 ] Data
[ S.14.10.3 ] Input parameters
[ S.14.10.4 ] Outcomes and figures
[ S.14.10.5 ] Implementation
[ S.14.11 ] s_gradient_boosting
[ S.14.11.1 ] Summary
[ S.14.11.2 ] Data
[ S.14.11.3 ] Parameters
[ S.14.11.4 ] Outcomes and figures
[ S.14.11.5 ] Implementation
[ S.14.12 ] s_logn_quant_regression
[ S.14.12.1 ] Summary
[ S.14.12.2 ] Parameters
[ S.14.12.3 ] Outcomes and figures
[ S.14.12.4 ] Implementation
[ S.14.12.5 ] See also
[ S.14.13 ] s_call_quant_regression
[ S.14.13.1 ] Summary
[ S.14.13.2 ] Data
[ S.14.13.3 ] Parameters
[ S.14.13.4 ] Outcomes and figures
[ S.14.13.5 ] Implementation
[ S.14.13.6 ] See also
[ S.14.14 ] s_logn_quant_lin_regression
[ S.14.14.1 ] Summary
[ S.14.14.2 ] Parameters
[ S.14.14.3 ] Outcomes and figures
[ S.14.14.4 ] Implementation
[ S.14.14.5 ] See also
[ S.14.15 ] s_call_quant_lin_regression
[ S.14.15.1 ] Summary
[ S.14.15.2 ] Data
[ S.14.15.3 ] Parameters
[ S.14.15.4 ] Outcomes and figures
[ S.14.15.5 ] Implementation
[ S.14.15.6 ] See also
[ S.14.16 ] s_call_mean_cart
[ S.14.17 ] s_call_quant_cart
[ S.14.18 ] s_binary_margin_losses
[ S.14.18.1 ] Summary
[ S.14.18.2 ] Parameters
[ S.14.18.3 ] Outcomes and figures
[ S.14.18.4 ] Implementation
[ S.14.19 ] s_point_classification_normal_mixtures
[ S.14.19.1 ] Summary
[ S.14.19.2 ] Parameters
[ S.14.19.3 ] Outcomes and figures
[ S.14.19.4 ] Implementation
[ S.14.19.5 ] See also
[ S.14.20 ] s_linclass_regression
[ S.14.21 ] s_linclass_fda
[ S.14.21.1 ] Summary
[ S.14.21.2 ] Data
[ S.14.21.3 ] Outcomes and figures
[ S.14.21.4 ] Implementation
[ S.14.21.5 ] See also
[ S.14.22 ] s_linclass_perceptron
[ S.14.22.1 ] Summary
[ S.14.22.2 ] Data
[ S.14.22.3 ] Outcomes and figures
[ S.14.22.4 ] Implementation
[ S.14.23 ] s_linclass_svm
[ S.14.23.1 ] Summary
[ S.14.23.2 ] Data
[ S.14.23.3 ] Parameters
[ S.14.23.4 ] Outcomes and figures
[ S.14.23.5 ] Implementation
[ S.14.24 ] s_linclass_ann_svm
[ S.14.25 ] s_autoencoders_kmeans
[ S.14.25.1 ] Summary
[ S.14.25.2 ] Parameters
[ S.14.25.3 ] Outcomes and figures
[ S.14.25.4 ] Implementation
[ S.14.25.5 ] See also
[ S.15 ] Probabilistic machine learning models
[ S.15.1 ] s_continuum_discrete_generative_pred
[ S.15.1.1 ] Summary
[ S.15.1.2 ] Parameters
[ S.15.1.3 ] Outcomes and figures
[ S.15.1.4 ] Implementation
[ S.15.1.5 ] See also
[ S.16 ] Generalized probabilistic inference
[ S.16.1 ] s_min_rel_ent_point_view
[ S.16.1.1 ] Summary
[ S.16.1.2 ] Parameters
[ S.16.1.3 ] Outcomes
[ S.16.1.4 ] Implementation
[ S.16.1.5 ] See also
[ S.16.2 ] s_min_rel_ent_distr_view
[ S.16.2.1 ] Summary
[ S.16.2.2 ] Parameters
[ S.16.2.3 ] Outcomes
[ S.16.2.4 ] Implementation
[ S.16.2.5 ] See also
[ S.16.3 ] s_min_rel_ent_partial_view
[ S.16.3.1 ] Summary
[ S.16.3.2 ] Parameters
[ S.16.3.3 ] Outcomes
[ S.16.3.4 ] Implementation
[ S.16.3.5 ] See also
[ S.16.4 ] s_entropy_view
[ S.16.4.1 ] Summary
[ S.16.4.2 ] Parameters
[ S.16.4.3 ] Outcomes and figures
[ S.16.4.4 ] Implementation
[ S.16.4.5 ] See also
[ S.16.5 ] s_views_linear_exp
[ S.16.5.1 ] Summary
[ S.16.5.2 ] Parameters
[ S.16.5.3 ] Outcomes
[ S.16.5.4 ] Implementation
[ S.16.5.5 ] See also
[ S.16.6 ] s_views_gen_expectations
[ S.16.6.1 ] Summary
[ S.16.6.2 ] Parameters
[ S.16.6.3 ] Outcomes
[ S.16.6.4 ] Implementation
[ S.16.6.5 ] See also
[ S.16.7 ] s_views_sorted_exp
[ S.16.7.1 ] Summary
[ S.16.7.2 ] Parameters
[ S.16.7.3 ] Outcomes
[ S.16.7.4 ] Implementation
[ S.16.7.5 ] See also
[ S.16.8 ] s_views_st_deviations
[ S.16.8.1 ] Summary
[ S.16.8.2 ] Parameters
[ S.16.8.3 ] Outcomes
[ S.16.8.4 ] Implementation
[ S.16.8.5 ] See also
[ S.16.9 ] s_views_correlations
[ S.16.9.1 ] Summary
[ S.16.9.2 ] Parameters
[ S.16.9.3 ] Outcomes
[ S.16.9.4 ] Implementation
[ S.16.9.5 ] See also
[ S.16.10 ] s_views_cond_prob
[ S.16.10.1 ] Summary
[ S.16.10.2 ] Parameters
[ S.16.10.3 ] Outcomes
[ S.16.10.4 ] Implementation
[ S.16.10.5 ] See also
[ S.16.11 ] s_views_cdf
[ S.16.11.1 ] Summary
[ S.16.11.2 ] Parameters
[ S.16.11.3 ] Outcomes
[ S.16.11.4 ] Implementation
[ S.16.11.5 ] See also
[ S.16.12 ] s_views_cond_exp
[ S.16.12.1 ] Summary
[ S.16.12.2 ] Parameters
[ S.16.12.3 ] Outcomes
[ S.16.12.4 ] Implementation
[ S.16.12.5 ] See also
[ S.16.13 ] Convexity test for relative entropy
[ S.16.14 ] Numerical verification of the gradient of entropy
[ S.16.15 ] Numerical verification of gradient of constraint function on signal
[ S.16.16 ] Numerical verification of the Hessian of entropy
[ S.16.17 ] Numerical and analytical Hessian of constraints on signal
[ S.16.18 ] Copula opinion pooling: uninformative views
[ S.17 ] Dynamic and spacial models
[ S.17.1 ] s_dyn_principal_component_var
[ S.17.1.1 ] Summary
[ S.17.1.2 ] Parameters
[ S.17.1.3 ] Outcomes and figures
[ S.17.1.4 ] Implementation
[ S.17.1.5 ] See also
[ S.17.2 ] s_kalman_filter_yield_curve
[ S.17.2.1 ] Summary
[ S.17.2.2 ] Data
[ S.17.2.3 ] Parameters
[ S.17.2.4 ] Outcomes and figures
[ S.17.2.5 ] Implementation
[ S.17.2.6 ] See also
[ S.17.3 ] s_hidden_markov_model_stocks
[ S.17.3.1 ] Summary
[ S.17.3.2 ] Data
[ S.17.3.3 ] Parameters
[ S.17.3.4 ] Outcomes and figures
[ S.17.3.5 ] Implementation
[ S.18 ] Application: probabilistic regression in the stock market
[ S.18.1 ] s_market_prediction_regression
[ S.18.2 ] s_lasso_vs_ridge
[ S.18.2.1 ] Summary
[ S.18.2.2 ] Data
[ S.18.2.3 ] Parameters
[ S.18.2.4 ] Outcomes and figures
[ S.18.2.5 ] Implementation
[ S.18.2.6 ] See also
[ S.18.3 ] Regression LFM’s: randomized least square estimates
[ S.18.4 ] s_reg_lfm_bayes_prior_niw
[ S.18.4.1 ] Summary
[ S.18.4.2 ] Parameters
[ S.18.4.3 ] Outcomes and figures
[ S.18.4.4 ] Implementation
[ S.18.5 ] s_reg_lfm_bayes_posterior_niw
[ S.18.5.1 ] Summary
[ S.18.5.2 ] Parameters
[ S.18.5.3 ] Outcomes and figures
[ S.18.5.4 ] Implementation
[ S.18.5.5 ] See also
[ S.19 ] Application: principal component analysis of the yield curve
[ S.19.1 ] Eigenvectors for Toeplitz structure
[ S.19.2 ] s_yield_change_correlation
[ S.19.2.1 ] Summary
[ S.19.2.2 ] Data
[ S.19.2.3 ] Parameters
[ S.19.2.4 ] Outcomes and figures
[ S.19.2.5 ] Implementation
[ S.19.2.6 ] See also
[ S.19.3 ] s_pca_empirical
[ S.19.3.1 ] Summary
[ S.19.3.2 ] Data
[ S.19.3.3 ] Parameters
[ S.19.3.4 ] Outcomes and figures
[ S.19.3.5 ] Implementation
[ S.19.3.6 ] See also
[ S.19.4 ] s_pca_yield
[ S.19.4.1 ] Summary
[ S.19.4.2 ] Data
[ S.19.4.3 ] Outcomes and figures
[ S.19.4.4 ] Implementation
[ S.19.5 ] Equivalent formulations of maximum likelihood with flexible probabilities estimates (numerical proof)
[ S.19.6 ] s_factors_selection
[ S.19.6.1 ] Summary
[ S.19.6.2 ] Parameters
[ S.19.6.3 ] Outcomes and figures
[ S.19.6.4 ] Implementation
[ S.19.6.5 ] See also
[ S.20 ] Application: credit default classification
[ S.20.1 ] s_default_probabilities
[ S.21 ] Application: clustering
S.III. Valuation
[ S.22 ] Executive summary
[ S.23 ] Background definitions
[ S.24 ] Linear pricing theory: core
[ S.24.1 ] s_current_values
[ 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 ] Stochastic discount factor comparison
[ S.24.3 ] s_fund_theorem
[ S.24.3.1 ] Summary
[ S.24.3.2 ] Data
[ S.24.3.3 ] Outcomes and figures
[ S.24.3.4 ] Implementation
[ S.24.3.5 ] See also
[ S.24.4 ] s_rn_derivative_mre
[ S.24.4.1 ] Summary
[ S.24.4.2 ] Data
[ S.24.4.3 ] Outcomes and figures
[ S.24.4.4 ] Implementation
[ S.24.4.5 ] See also
[ S.24.5 ] Fundamental theorem of asset pricing with risk-neutral measure: empirical verification
[ S.24.6 ] s_risk_neutral_density
[ S.24.6.1 ] Summary
[ S.24.6.2 ] Data
[ S.24.6.3 ] Parameters
[ S.24.6.4 ] Outcomes and figures
[ S.24.6.5 ] Implementation
[ S.24.7 ] s_capm_like_identity
[ S.24.7.1 ] Summary
[ S.24.7.2 ] Parameters
[ S.24.7.3 ] Outcomes and figures
[ S.24.7.4 ] Implementation
[ S.24.7.5 ] See also
[ S.25 ] Linear pricing theory: further assumptions
[ S.25.1 ] s_simulate_call
[ S.25.1.1 ] Summary
[ S.25.1.2 ] Parameters
[ S.25.1.3 ] Outcomes and figures
[ S.25.1.4 ] Implementation
[ S.25.1.5 ] See also
[ S.25.2 ] s_simulate_payoff
[ S.25.3 ] Minimum variance factor-replicating portfolio
[ S.26 ] Non-linear pricing theory
[ S.27 ] Valuation implementation
[ S.27.1 ] Fitting implied volatility from the Black-Scholes-Merton valuation
S.IV. Performance analysis
[ S.28 ] Executive summary
[ S.29 ] Performance definitions
[ S.29.1 ] Compounded return of a portfolio
[ S.30 ] Performance attribution
S.V. Quant toolbox
[ S.31 ] Summary
[ S.32 ] Distributions
[ S.32.1 ] Conditional expectation between normal random variables
[ S.32.2 ] Innovation: the normal case, implementation
[ S.32.3 ] Innovation: the lognormal case, implementation
[ S.32.4 ] s_bivariate_normal
[ S.32.4.1 ] Summary
[ S.32.4.2 ] Parameters
[ S.32.4.3 ] Outcomes and figures
[ S.32.4.4 ] Implementation
[ S.32.5 ] s_uniform_inside_circle
[ S.32.5.1 ] Summary
[ S.32.5.2 ] Parameters
[ S.32.5.3 ] Outcomes and figures
[ S.32.5.4 ] Implementation
[ S.32.6 ] s_elliptical_uniform_radial_rep
[ S.32.6.1 ] Summary
[ S.32.6.2 ] Parameters
[ S.32.6.3 ] Outcomes and figures
[ S.32.6.4 ] Implementation
[ S.32.7 ] s_simulate_unif_in_ellipse
[ S.32.7.1 ] Summary
[ S.32.7.2 ] Parameters
[ S.32.7.3 ] Outcomes and figures
[ S.32.7.4 ] Implementation
[ S.32.7.5 ] See also
[ S.32.8 ] s_ens_exp_decay
[ S.32.8.1 ] Summary
[ S.32.8.2 ] Parameters
[ S.32.8.3 ] Outcomes and figures
[ S.32.8.4 ] Implementation
[ S.32.8.5 ] See also
[ S.32.9 ] s_display_dirac_delta
[ S.32.9.1 ] Summary
[ S.32.9.2 ] Input
[ S.32.9.3 ] Outcomes and figures
[ S.32.9.4 ] Implementation
[ S.32.9.5 ] See also
[ S.32.10 ] s_scen_prob_pdf
[ S.32.10.1 ] Summary
[ S.32.10.2 ] Parameters
[ S.32.10.3 ] Outcomes and figures
[ S.32.10.4 ] Implementation
[ S.32.10.5 ] See also
[ S.32.11 ] s_scen_prob_cdf_quantile
[ S.32.11.1 ] Summary
[ S.32.11.2 ] Input
[ S.32.11.3 ] Outcomes
[ S.32.11.4 ] Implementation
[ S.32.11.5 ] See also
[ S.32.12 ] s_generalized_expon_entropy
[ S.32.12.1 ] Summary
[ S.32.12.2 ] Parameters
[ S.32.12.3 ] Outcomes and figures
[ S.32.12.4 ] Implementation
[ S.32.12.5 ] See also
[ S.32.13 ] s_normal_exponential_family
[ S.32.13.1 ] Summary
[ S.32.13.2 ] Parameters
[ S.32.13.3 ] Outcomes
[ S.32.13.4 ] Implementation
[ S.32.13.5 ] See also
[ S.32.14 ] s_saddle_point_vs_mcfp_quadn
[ S.32.14.1 ] Summary
[ S.32.14.2 ] Parameters
[ S.32.14.3 ] Outcomes and figures
[ S.32.14.4 ] Implementation
[ S.32.14.5 ] See also
[ S.32.15 ] s_bivariate_wishart
[ S.32.15.1 ] Summary
[ S.32.15.2 ] Input
[ S.32.15.3 ] Outcomes and figures
[ S.32.15.4 ] Implementation
[ S.32.15.5 ] See also
[ S.32.16 ] s_gaussian_mixture
[ S.32.16.1 ] Summary
[ S.32.16.2 ] Parameters
[ S.32.16.3 ] Outcomes and figures
[ S.32.16.4 ] Implementation
[ S.32.16.5 ] See also
[ S.32.17 ] s_discrete_partitioned_rv
[ S.33 ] Geometry of distributions
[ S.34 ] Copulas
[ S.34.1 ] Display copula of a bivariate normal
[ S.34.2 ] s_normal_to_uniform
[ 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 ] See also
[ S.34.3 ] s_lognorm_to_uniform
[ 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_gamma_to_uniform
[ 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.34.5 ] s_uniform_to_normal
[ S.34.5.1 ] Summary
[ S.34.5.2 ] Parameters
[ S.34.5.3 ] Outcomes and figures
[ S.34.5.4 ] Implementation
[ S.34.5.5 ] See also
[ S.34.6 ] s_uniform_to_lognorm
[ S.34.6.1 ] Summary
[ S.34.6.2 ] Parameters
[ S.34.6.3 ] Outcomes and figures
[ S.34.6.4 ] Implementation
[ S.34.6.5 ] See also
[ S.34.7 ] s_uniform_to_gamma
[ S.34.7.1 ] Summary
[ S.34.7.2 ] Parameters
[ S.34.7.3 ] Outcomes and figures
[ S.34.7.4 ] Implementation
[ S.34.7.5 ] See also
[ S.34.8 ] Combination: from FP-copula/historical marginal to joint
[ S.34.9 ] s_display_norm_copula
[ S.34.9.1 ] Summary
[ S.34.9.2 ] Parameters
[ S.34.9.3 ] Outcomes and figures
[ S.34.9.4 ] Implementation
[ S.34.9.5 ] See also
[ S.34.10 ] s_display_t_copula
[ S.34.10.1 ] Summary
[ S.34.10.2 ] Parameters
[ S.34.10.3 ] Outcomes and figures
[ S.34.10.4 ] Implementation
[ S.34.10.5 ] See also
[ S.34.11 ] s_copula_returns
[ S.34.11.1 ] Summary
[ S.34.11.2 ] Parameters
[ S.34.11.3 ] Outcomes and figures
[ S.34.11.4 ] Implementation
[ S.34.11.5 ] See also
[ S.34.12 ] s_cop_marg_separation
[ S.34.12.1 ] Script summary
[ S.34.12.2 ] Parameters
[ S.34.12.3 ] Outcomes and figures
[ S.34.12.4 ] Implementation
[ S.34.12.5 ] See also
[ S.34.13 ] s_cop_marg_combination
[ S.34.13.1 ] Script summary
[ S.34.13.2 ] Parameters
[ S.34.13.3 ] Outcomes and figures
[ S.34.13.4 ] Implementation
[ S.34.14 ] s_t_copula_norm_marginals
[ S.34.14.1 ] Summary
[ S.34.14.2 ] Parameters
[ S.34.14.3 ] Outcomes and figures
[ S.34.14.4 ] Implementation
[ S.34.14.5 ] See also
[ S.34.15 ] Separation: from historical joint to copula/marginal
[ S.34.16 ] Display the pdf of a bivariate t-copula
[ S.35 ] Correlation and generalizations
[ S.35.1 ] s_norm_const_sw
[ S.35.1.1 ] Summary
[ S.35.1.2 ] Parameters
[ S.35.1.3 ] Outcomes and figures
[ S.35.1.4 ] Implementation
[ S.35.2 ] s_dependence_structure_call_put
[ S.35.2.1 ] Summary
[ S.35.2.2 ] Parameters
[ S.35.2.3 ] Outcomes and figures
[ S.35.2.4 ] Implementation
[ S.35.2.5 ] See also
[ S.35.3 ] Correlation between normal variables (numerical proof)
[ S.35.4 ] Correlation between lognormal variables (numerical proof)
[ S.35.5 ] Correlation between normalized Wishart marginals(numerical proof)
[ S.35.6 ] s_uncorr_no_indep
[ S.35.6.1 ] Summary
[ S.35.6.2 ] Parameters
[ S.35.6.3 ] Outcomes and figures
[ S.35.6.4 ] Implementation
[ S.35.6.5 ] See also
[ S.35.7 ] s_clt_student_t
[ S.35.7.1 ] Summary
[ S.35.7.2 ] Parameters
[ S.35.7.3 ] Outcomes and figures
[ S.35.7.4 ] Implementation
[ S.35.7.5 ] See also
[ S.35.8 ] s_full_dependence
[ S.35.8.1 ] Summary
[ S.35.8.2 ] Parameters
[ S.35.8.3 ] Outcomes and figures
[ S.35.8.4 ] Implementation
[ S.35.8.5 ] See also
[ S.35 ] Stochastic dominance
[ S.35.1 ] s_strong_dominance
[ 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 ] s_weak_dominance
[ S.35.2.1 ] Summary
[ S.35.2.2 ] Parameters
[ S.35.2.3 ] Outcomes and figures
[ S.35.2.4 ] Implementation
[ S.35.2.5 ] See also
[ S.35.3 ] s_second_order_dominance
[ S.35.3.1 ] Summary
[ S.35.3.2 ] Parameters
[ S.35.3.3 ] Outcomes and figures
[ S.35.3.4 ] Implementation
[ S.36 ] Location and dispersion
[ S.36.1 ] s_logn_uncertainty_bands
[ S.36.1.1 ] Summary
[ S.36.1.2 ] Parameters
[ S.36.1.3 ] Outcomes and figures
[ S.36.1.4 ] Implementation
[ S.36.2 ] Different visualizations of the multivariate uncertainty band
[ S.36.3 ] s_affine_equiv_exp_cov
[ S.36.3.1 ] Summary
[ S.36.3.2 ] Parameters
[ S.36.3.3 ] Outcomes and figures
[ S.36.3.4 ] Implementation
[ S.36.3.5 ] See also
[ S.36.4 ] Ellipsoid and uncertainty band of a bivariate normal
[ S.36.5 ] s_ellipsoid_multiv_exp_cov
[ S.36.5.1 ] Summary
[ S.36.5.2 ] Parameters
[ S.36.5.3 ] Outcomes and figures
[ S.36.5.4 ] Implementation
[ S.36.5.5 ] See also
[ S.36.6 ] s_display_corr_norm_ellips
[ S.36.6.1 ] Summary
[ S.36.6.2 ] Parameters
[ S.36.6.3 ] Outcomes and figures
[ S.36.6.4 ] Implementation
[ S.36.6.5 ] See also
[ S.36.7 ] Ellipsoid and uncertainty band of a bivariate lognormal
[ S.36.8 ] Ellipsoid and uncertainty band of normalized Wishart marginals
[ S.36.9 ] s_affine_equiv_mode_mod_disp
[ S.36.9.1 ] Summary
[ S.36.9.2 ] Parameters
[ S.36.9.3 ] Outcomes and figures
[ S.36.9.4 ] Implementation
[ S.36.9.5 ] See also
[ S.36.10 ] Iso-contour of a lognormal bivariate distribution
[ S.36.11 ] s_chebyshev_ineq
[ S.36.11.1 ] Summary
[ S.36.11.2 ] Parameters
[ S.36.11.3 ] Outcomes and figures
[ S.36.11.4 ] Implementation
[ S.36.11.5 ] See also
[ S.36.12 ] Expectation-covariance as minimum volume ellipsoid
[ S.36.13 ] R-squared: numerical example
[ S.36.14 ] Euclidean vectors of joint normal variables
[ S.36.15 ] s_lognormal_l2_geometry
[ S.36.15.1 ] Summary
[ S.36.15.2 ] Parameters
[ S.36.15.3 ] Outcomes and figures
[ S.36.15.4 ] Implementation
[ S.36.15.5 ] See also
[ S.36.16 ] Orthogonality of principal directions
[ S.36.17 ] Euclidean vectors via linear transformation and Riccati root
[ S.37 ] Decision theory with model uncertainty
[ S.38 ] Estimation techniques
[ S.38.1 ] Maximum likelihood estimation
[ S.39 ] Estimation and assessment
[ S.39.1 ] s_bias_vs_variance_lognormal
[ S.39.2 ] Predictive distribution assessment
[ S.41 ] Estimation and regularization
[ S.41.1 ] s_bias_vs_variance_normal
[ S.41.2 ] s_encoding
[ S.42 ] Hypothesis testing
[ S.43 ] Stochastic processes cheat sheet
[ S.43.1 ] s_discrete_partitioned_process
[ S.44 ] Invariance tests
[ S.44.1 ] s_elltest_normal
[ S.44.1.1 ] Summary
[ S.44.1.2 ] Parameters
[ S.44.1.3 ] Outcomes and figures
[ S.44.1.4 ] Implementation
[ S.44.1.5 ] See also
[ S.44.2 ] Kolmogorov-Smirnov test for invariance on simulations
[ S.44.3 ] Copula based tests for invariance on simulations
[ S.44.4 ] Invariance tests on realized time series
[ S.45 ] Continuous time processes
[ S.45.1 ] s_projection_brownian_motion
[ S.45.1.1 ] Summary
[ S.45.1.2 ] Data
[ S.45.1.3 ] Parameters
[ S.45.1.4 ] Outcomes and figures
[ S.45.1.5 ] Implementation
[ S.45.1.6 ] See also
[ S.45.2 ] s_projection_univ_rating
[ S.45.2.1 ] Summary
[ S.45.2.2 ] Data
[ S.45.2.3 ] Outcomes and figures
[ S.45.2.4 ] Implementation
[ S.45.2.5 ] See also
[ S.45.3 ] Projection of Poisson process
[ S.45.4 ] Simulate compound Poisson processes
[ S.45.5 ] Projection of a Compound Poisson Process
[ S.45.6 ] Projection of Cauchy distribution
[ S.45.7 ] Projection of the variance gamma distribution
[ S.45.8 ] Simulate the NIG and VG processes
[ S.45.9 ] Simulation of the variance gamma process by subordination
[ S.45.10 ] Projection of the Student t distribution
[ S.45.11 ] Projection of the uniform distribution
[ S.45.12 ] Projection of fractional Brownian motion
[ S.45.13 ] Projection of the Heston process
[ S.45.14 ] Simulation of the Heston process by Time Change
[ S.45.15 ] Simulation of a Ornstein-Uhlenbeck process
[ S.45.16 ] MVOU change of coordinates
[ S.45.17 ] Projection of conditional mean and covariance of the MVOU process
[ S.46 ] Covariance stationary processes
[ S.46.1 ] s_autocov_spec_dens_ar1
[ S.46.1.1 ] Summary
[ S.46.1.2 ] Parameters
[ S.46.1.3 ] Outcomes and figures
[ S.46.1.4 ] Implementation
[ S.46.2 ] s_spectral_representation
[ S.46.2.1 ] Summary
[ S.46.2.2 ] Parameters
[ S.46.2.3 ] Outcomes and figures
[ S.46.2.4 ] Implementation
[ S.46.2.5 ] Tips
[ S.46.2.6 ] See also
[ S.46.3 ] s_toeplitz_spectral
[ S.46.3.1 ] Summary
[ S.46.3.2 ] Parameters
[ S.46.3.3 ] Outcomes and figures
[ S.46.3.4 ] Implementation
[ S.46.3.5 ] See also
[ S.46.4 ] Bandpass filter applied to AR(1) processes
[ S.46.4.1 ] Summary
[ S.46.4.2 ] Parameters
[ S.46.4.3 ] Outcomes and figures
[ S.46.4.4 ] Implementation
[ S.46.4.5 ] See also
[ S.47 ] Signals
[ S.47.1 ] s_cointegration_detection
[ S.47.1.1 ] Summary
[ S.47.1.2 ] Data
[ S.47.1.3 ] Parameters
[ S.47.1.4 ] Outcomes and figures
[ S.47.1.5 ] Implementation
[ S.47.1.6 ] See also
[ S.47.2 ] s_cointegration_signal
[ S.47.2.1 ] Summary
[ S.47.2.2 ] Data
[ S.47.2.3 ] Parameters
[ S.47.2.4 ] Outcomes and figures
[ S.47.2.5 ] Implementation
[ S.47.2.6 ] See also
[ S.47.3 ] s_trade_autocorr_signal
[ S.47.3.1 ] Summary
[ S.47.3.2 ] Data
[ S.47.3.3 ] Parameters
[ S.47.3.4 ] Outcomes and figures
[ S.47.3.5 ] Implementation
[ S.47.3.6 ] See also
[ S.47.4 ] s_order_imbal_signal
[ S.47.4.1 ] Summary
[ S.47.4.2 ] Data
[ S.47.4.3 ] Parameters
[ S.47.4.4 ] Outcomes and figures
[ S.47.4.5 ] Implementation
[ S.47.4.6 ] See also
[ S.47.5 ] s_price_pred_signal
[ S.47.5.1 ] Summary
[ S.47.5.2 ] Data
[ S.47.5.3 ] Parameters
[ S.47.5.4 ] Outcomes and figures
[ S.47.5.5 ] Implementation
[ S.47.5.6 ] See also
[ S.47.6 ] s_volume_cluster_signal
[ S.47.6.1 ] Summary
[ S.47.6.2 ] Data
[ S.47.6.3 ] Parameters
[ S.47.6.4 ] Outcomes and figures
[ S.47.6.5 ] Implementation
[ S.47.6.6 ] See also
[ S.47.7 ] s_momentum_signals
[ S.47.7.1 ] Summary
[ S.47.7.2 ] Data
[ S.47.7.3 ] Parameters
[ S.47.7.4 ] Outcomes and figures
[ S.47.7.5 ] Implementation
[ S.47.7.6 ] See also
[ S.47.8 ] Signal filtering for systematic strategy (size)
[ S.48 ] Black-Litterman
[ S.48.1 ] s_bl_equilibrium_ret
[ S.48.1.1 ] Summary
[ S.48.1.2 ] Data
[ S.48.1.3 ] Parameters
[ S.48.1.4 ] Outcomes
[ S.48.1.5 ] Implementation
[ S.48.1.6 ] See also
[ S.48.2 ] s_info_processing_comparison
[ S.48.2.1 ] Summary
[ S.48.2.2 ] Data
[ S.48.2.3 ] Parameters
[ S.48.2.4 ] Outcomes and figures
[ S.48.2.5 ] Implementation
[ S.48.2.6 ] See also
[ S.49 ] Optimization primer
[ S.49.1 ] s_convex_programming
[ S.49.1.1 ] Summary
[ S.49.1.2 ] Implementation
[ S.49.1.3 ] See also
[ S.49.2 ] s_selection_toy
[ S.49.2.1 ] Summary
[ S.49.2.2 ] Parameters
[ S.49.2.3 ] Outcomes and figures
[ S.49.2.4 ] Implementation
[ S.49.2.5 ] See also
[ S.50 ] Useful algorithms
[ S.50.1 ] Cholesky decomposition of the covariance (numerical proof)
[ S.50.2 ] Numerical proof
[ S.50.3 ] s_cpca_vs_pca
[ S.50.3.1 ] Script summary
[ S.50.3.2 ] Parameters
[ S.50.3.3 ] Outcomes and figures
[ S.50.3.4 ] Implementation
[ S.50.3.5 ] See also
[ S.50.4 ] s_factor_analysis_algos
[ S.50.4.1 ] Summary
[ S.50.4.2 ] Parameters
[ S.50.4.3 ] Outcomes and figures
[ S.50.4.4 ] Implementation
[ S.50.4.5 ] See also
[ S.50.5 ] Transpose-square-root of a symmetric positive (semi)definite matrix
[ S.51 ] Linear algebra primer
[ S.51.1 ] s_vector_representation
[ S.51.1.1 ] Summary
[ S.51.1.2 ] Parameters
[ S.51.1.3 ] Outcomes and figures
[ S.51.2 ] s_vector_operations
[ S.51.2.1 ] Summary
[ S.51.2.2 ] Parameters
[ S.51.2.3 ] Outcomes and figures
[ S.51.2.4 ] Implementation
[ S.51.3 ] s_vector_subspaces
[ S.51.3.1 ] Summary
[ S.51.3.2 ] Parameters
[ S.51.3.3 ] Outcomes and figures
[ S.51.4 ] s_linear_transformations
[ S.51.4.1 ] Summary
[ S.51.4.2 ] Parameters
[ S.51.4.3 ] Outcomes and figures
[ S.51.4.4 ] Implementation
[ S.51.5 ] s_positive_definite_matrix
[ S.51.5.1 ] Summary
[ S.51.5.2 ] Parameters
[ S.51.5.3 ] Outcomes and figures
[ S.51.5.4 ] Implementation
[ S.51.6 ] s_spectral_theorem
[ S.51.6.1 ] Summary
[ S.51.6.2 ] Parameters
[ S.51.6.3 ] Outcomes and figures
[ S.51.6.4 ] Implementation
[ S.51.6.5 ] See also
[ S.52 ] Calculus primer
[ S.52.1 ] s_univariate_derivatives
[ S.52.1.1 ] Summary
[ S.52.1.2 ] Parameters
[ S.52.1.3 ] Outcomes and figures
[ S.52.1.4 ] Implementation
[ S.52.2 ] s_multivariate_derivatives
[ S.52.2.1 ] Summary
[ S.52.2.2 ] Parameters
[ S.52.2.3 ] Outcomes and figures
[ S.52.2.4 ] Implementation
[ S.52.3 ] s_riemann_integration_univariate
[ S.52.3.1 ] Summary
[ S.52.3.2 ] Parameters
[ S.52.3.3 ] Outcomes and figures
[ S.52.3.4 ] Implementation
[ S.52.4 ] s_riemann_integration_multivariate
[ S.52.4.1 ] Summary
[ S.52.4.2 ] Parameters
[ S.52.4.3 ] Outcomes and figures
[ S.52.4.4 ] Implementation
[ S.52.5 ] s_saddlepoint
[ S.52.5.1 ] Summary
[ S.52.5.2 ] Parameters
[ S.52.5.3 ] Outcomes and figures
[ S.52.5.4 ] Implementation
F.I. Functions
Functions
[ 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_factor_analysis
[ 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 ] References
[ F.1.11.8 ] See also
[ F.1.12 ] fit_garch_fp
[ F.1.12.1 ] Summary
[ F.1.12.2 ] Input
[ F.1.12.3 ] Output
[ F.1.12.4 ] Implementation
[ F.1.12.5 ] Tips
[ F.1.12.6 ] Example
[ F.1.12.7 ] Case studies
[ F.1.12.8 ] See also
[ F.1.13 ] fit_lfm_lasso
[ F.1.13.1 ] Summary
[ F.1.13.2 ] Input
[ F.1.13.3 ] Output
[ F.1.13.4 ] Implementation
[ F.1.13.5 ] Example
[ F.1.13.6 ] See also
[ F.1.14 ] fit_lfm_lasso_path
[ F.1.14.1 ] Example
[ F.1.15 ] fit_lfm_mlfp
[ 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 ] See also
[ F.1.16 ] fit_lfm_ols
[ F.1.16.1 ] Summary
[ F.1.16.2 ] Input
[ F.1.16.3 ] Output
[ F.1.16.4 ] Implementation
[ F.1.16.5 ] Example
[ F.1.16.6 ] Case studies
[ F.1.16.7 ] References
[ F.1.16.8 ] See also
[ F.1.17 ] fit_lfm_pcfp
[ F.1.17.1 ] Example
[ F.1.18 ] fit_lfm_ridge
[ F.1.18.1 ] Example
[ F.1.19 ] fit_lfm_ridge_path
[ F.1.19.1 ] Example
[ F.1.20 ] fit_lfm_roblasso
[ F.1.20.1 ] Example
[ F.1.21 ] fit_locdisp_mlfp
[ F.1.21.1 ] Summary
[ F.1.21.2 ] Input
[ F.1.21.3 ] Output
[ F.1.21.4 ] Implementation
[ F.1.21.5 ] Example
[ F.1.21.6 ] Case studies
[ F.1.21.7 ] See also
[ F.1.22 ] fit_locdisp_mlfp_difflength
[ F.1.22.1 ] Summary
[ F.1.22.2 ] Input
[ F.1.22.3 ] Output
[ F.1.22.4 ] Implementation
[ F.1.22.5 ] Tips
[ F.1.22.6 ] Example
[ F.1.22.7 ] Case studies
[ F.1.22.8 ] References
[ F.1.22.9 ] See also
[ F.1.23 ] fit_trans_matrix_credit
[ 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_state_space
[ F.1.24.1 ] Summary
[ F.1.24.2 ] Input
[ F.1.24.3 ] Output
[ F.1.24.4 ] Implementation
[ F.1.24.5 ] Example
[ F.1.24.6 ] Case studies
[ F.1.24.7 ] References
[ F.1.24.8 ] See also
[ F.1.25 ] fit_t_dof
[ F.1.25.1 ] Example
[ F.1.26 ] fit_t_fp
[ F.1.26.1 ] Example
[ F.1.27 ] fit_var1
[ F.1.27.1 ] Summary
[ F.1.27.2 ] Input
[ F.1.27.3 ] Output
[ F.1.27.4 ] Implementation
[ F.1.27.5 ] Example
[ F.1.27.6 ] Case studies
[ F.1.27.7 ] See also
[ F.1.28 ] markov_network
[ F.1.28.1 ] Example
[ F.1.29 ] min_corr_toeplitz
[ 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.1.30 ] smooth_kernel_fp
[ F.1.30.1 ] Summary
[ F.1.30.2 ] Input
[ F.1.30.3 ] Output
[ F.1.30.4 ] Implementation
[ F.1.30.5 ] Example
[ F.1.30.6 ] Case studies
[ F.1.30.7 ] References
[ F.1.30.8 ] See also
[ F.1.31 ] spectrum_shrink
[ F.1.31.1 ] Example
[ F.1.32 ] var2mvou
[ F.1.32.1 ] Summary
[ F.1.32.2 ] Input
[ F.1.32.3 ] Output
[ F.1.32.4 ] Implementation
[ F.1.32.5 ] Tips
[ F.1.32.6 ] Example
[ F.1.32.7 ] Case studies
[ F.1.32.8 ] References
[ F.1.33 ] Generate FP profiles via multivariate Gaussian kernel: function implementation
[ F.1.34 ] Expectation Maximization algorithm with flexible probabilities for Missing Values. Routine
[ F.1.35 ] Homogeneous correlation cluster shrinkage
[ F.1.36 ] Iterated generalized method of moments with flexible probabilities for the Poisson distribution
[ F.1.37 ] Minimum volume ellipsoid enclosing data. Routine
[ F.1.38 ] Farthest Outlier Detection. Routine
[ F.1.39 ] High breakdown point with flexible probabilities. Routine
[ F.1.40 ] Outlier detection with flexible probabilities. Routine
[ F.1.41 ] SMTCovariance
[ F.1.42 ] FitFractionalIntegration
[ F.1.43 ] FitGenParetoMLFP
[ F.1.44 ] NormalMixtureFit
[ F.1.45 ] FitSkewtMLFP
[ F.1.46 ] FitVar_ATMSVI
[ F.1.47 ] GarchResiduals
[ F.1.48 ] MMFP
[ F.1.49 ] kMeansClustering
[ F.1.50 ] QuantileGenParetoMLFP
[ F.1.51 ] ObjectiveToeplitz
[ F.2 ] Portfolio
[ F.2.1 ] almgren_chriss
[ F.2.1.1 ] Example
[ F.2.2 ] char_portfolio
[ F.2.2.1 ] Example
[ 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.2.6.1 ] Example
[ 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 ] Case studies
[ F.3.4.8 ] References
[ F.3.4.9 ] 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 ] Example
[ 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_pdf
[ F.4.19.1 ] Summary
[ F.4.19.2 ] Input
[ F.4.19.3 ] Output
[ F.4.19.4 ] Implementation
[ F.4.19.5 ] Example
[ F.4.19.6 ] Case studies
[ F.4.19.7 ] References
[ F.4.20 ] mvt_logpdf
[ F.4.20.1 ] Summary
[ F.4.20.2 ] Input
[ F.4.20.3 ] Output
[ F.4.20.4 ] Implementation
[ F.4.20.5 ] Example
[ F.4.20.6 ] References
[ F.4.21 ] normal_canonical
[ 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 ] objective_r2
[ 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 ] Case studies
[ F.4.22.7 ] See also
[ F.4.23 ] perceptron_fp
[ F.4.23.1 ] Summary
[ F.4.24 ] plot_kstest
[ F.4.24.1 ] Example
[ F.4.25 ] pdf_sp
[ F.4.25.1 ] Summary
[ F.4.25.2 ] Input
[ F.4.25.3 ] Output
[ F.4.25.4 ] Implementation
[ F.4.25.5 ] Example
[ F.4.25.6 ] Case studies
[ F.4.25.7 ] References
[ F.4.26 ] project_trans_matrix
[ F.4.26.1 ] Summary
[ F.4.26.2 ] Output
[ F.4.26.3 ] Implementation
[ F.4.26.4 ] Example
[ F.4.26.5 ] Case studies
[ F.4.26.6 ] References
[ F.4.26.7 ] See also
[ F.4.27 ] quantile_sp
[ F.4.27.1 ] Summary
[ F.4.27.2 ] Input
[ F.4.27.3 ] Output
[ F.4.27.4 ] Implementation
[ F.4.27.5 ] Example
[ F.4.27.6 ] Case studies
[ F.4.27.7 ] References
[ F.4.28 ] saddle_point_quadn
[ F.4.28.1 ] Summary
[ F.4.28.2 ] Input
[ F.4.28.3 ] Output
[ F.4.28.4 ] Implementation
[ F.4.28.5 ] Tips
[ F.4.28.6 ] Example
[ F.4.28.7 ] Case studies
[ F.4.28.8 ] References
[ F.4.28.9 ] See also
[ F.4.29 ] schweizer_wolff
[ F.4.29.1 ] Summary
[ F.4.29.2 ] Input
[ F.4.29.3 ] Output
[ F.4.29.4 ] Implementation
[ F.4.29.5 ] Example
[ F.4.29.6 ] Case studies
[ F.4.29.7 ] See also
[ F.4.30 ] scoring
[ 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 ] References
[ F.4.30.8 ] See also
[ F.4.31 ] simulate_garch
[ 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 ] See also
[ F.4.32 ] simulate_niw
[ F.4.32.1 ] Example
[ F.4.33 ] simulate_bm
[ F.4.33.1 ] Summary
[ F.4.33.2 ] Input
[ F.4.33.3 ] Output
[ F.4.33.4 ] Implementation
[ F.4.33.5 ] Example
[ F.4.33.6 ] Case studies
[ F.4.33.7 ] References
[ F.4.33.8 ] See also
[ F.4.34 ] simulate_markov_chain_univ
[ 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_multiv
[ 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_mvou
[ F.4.36.1 ] Summary
[ F.4.36.2 ] Input
[ F.4.36.3 ] Output
[ F.4.36.4 ] Implementation
[ F.4.36.5 ] Tips
[ F.4.36.6 ] Example
[ F.4.36.7 ] Case studies
[ F.4.36.8 ] References
[ F.4.36.9 ] See also
[ F.4.37 ] simulate_normal
[ 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_dimred
[ F.4.38.1 ] Example
[ F.4.39 ] simulate_quadn
[ F.4.39.1 ] Summary
[ F.4.39.2 ] Input
[ F.4.39.3 ] Output
[ F.4.39.4 ] Implementation
[ F.4.39.5 ] Example
[ F.4.39.6 ] Case studies
[ F.4.39.7 ] References
[ F.4.39.8 ] See also
[ F.4.40 ] simulate_rw_hfp
[ 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_t
[ F.4.41.1 ] Summary
[ F.4.41.2 ] Input
[ F.4.41.3 ] Output
[ F.4.41.4 ] Implementation
[ F.4.41.5 ] Tips
[ F.4.41.6 ] Example
[ F.4.41.7 ] References
[ F.4.41.8 ] See also
[ F.4.42 ] simulate_unif_in_ellips
[ 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_var1
[ F.4.43.1 ] Summary
[ F.4.43.2 ] Input
[ F.4.43.3 ] Output
[ F.4.43.4 ] Implementation
[ F.4.43.5 ] Example
[ F.4.43.6 ] Case studies
[ F.4.43.7 ] References
[ F.4.43.8 ] See also
[ F.4.44 ] simulate_wishart
[ 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 ] smoothing
[ 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 ] twist_prob_mom_match
[ F.4.46.1 ] Summary
[ F.4.46.2 ] Input
[ F.4.46.3 ] Output
[ F.4.46.4 ] Implementation
[ F.4.46.5 ] Tips
[ F.4.46.6 ] Example
[ F.4.46.7 ] Case studies
[ F.4.46.8 ] References
[ F.4.46.9 ] See also
[ F.4.47 ] twist_scenarios_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 ] Example
[ F.4.47.6 ] Case studies
[ F.4.47.7 ] References
[ F.4.47.8 ] See also
[ F.4.48 ] Flexible probabilities and scenarios of panic distribution
[ F.4.49 ] norm_cop_pdf
[ F.4.49.1 ] Summary
[ F.4.49.2 ] Input
[ F.4.49.3 ] Output
[ F.4.49.4 ] Implementation
[ F.4.49.5 ] Example
[ F.4.49.6 ] Case studies
[ F.4.50 ] t_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 ] Elliptical Copula Scenarios via dimension reduction function
[ F.4.52 ] Normalized empirical histogram of scenario-probability distribution: function implementation
[ F.4.53 ] Metropolis-Hastings algorithm: implementation
[ F.4.54 ] Projection to horizon of central moments. Routine
[ F.4.55 ] Normal innovation function
[ F.4.56 ] Projection of the pdf via DFT: function implementation
[ F.4.57 ] GHCalibration
[ F.4.58 ] Stats
[ F.4.59 ] Raw2Cumul
[ F.4.60 ] PnlVolatility_ewma
[ F.4.61 ] ffgn
[ F.4.62 ] Central2Raw
[ F.4.63 ] CentralAndStandardizedStatistics
[ F.4.64 ] Schout2ConTank
[ F.4.65 ] ProjectionStudentT
[ F.4.66 ] QuantileMixture
[ F.4.67 ] ScoringAssessmentFP
[ F.4.68 ] SharpeRatio
[ F.4.69 ] PathsCauchy
[ F.4.70 ] SimulateCompPoisson
[ F.4.71 ] JumpDiffusionKou
[ F.4.72 ] JumpDiffusionMerton
[ F.4.73 ] NIG
[ F.4.74 ] VGpdf
[ F.4.75 ] SmoothStep
[ F.4.76 ] SpinOutlier
[ F.4.77 ] ShiftedVGMoments
[ F.4.78 ] ParamChangeVG
[ F.4.79 ] VG
[ F.5 ] Views
[ F.5.1 ] black_litterman
[ F.5.1.1 ] Example
[ F.5.2 ] min_rel_entropy_normal
[ F.5.2.1 ] Example
[ 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
[ F.6.5.1 ] Summary
[ F.6.5.2 ] Input
[ F.6.5.3 ] Output
[ F.6.5.4 ] Implementation
[ F.6.5.5 ] Tips
[ F.6.5.6 ] Example
[ F.6.5.7 ] Case studies
[ F.6.5.8 ] References
[ F.6.5.9 ] See also
[ F.6.6 ] enet_selection
[ F.6.6.1 ] Summary
[ F.6.6.2 ] Input
[ F.6.6.3 ] Output
[ F.6.6.4 ] Implementation
[ F.6.6.5 ] Example
[ F.6.6.6 ] Case studies
[ F.6.6.7 ] See also
[ F.6.7 ] forward_selection
[ F.6.7.1 ] Summary
[ F.6.7.2 ] Input
[ F.6.7.3 ] Output
[ F.6.7.4 ] Implementation
[ F.6.7.5 ] Example
[ F.6.7.6 ] Case studies
[ F.6.8 ] gram_schmidt
[ F.6.8.1 ] Summary
[ F.6.8.2 ] Input
[ F.6.8.3 ] Output
[ F.6.8.4 ] Implementation
[ F.6.8.5 ] Example
[ F.6.8.6 ] References
[ F.6.9 ] histogram_sp
[ F.6.9.1 ] Summary
[ F.6.9.2 ] Input
[ F.6.9.3 ] Output
[ F.6.9.4 ] Implementation
[ F.6.9.5 ] Example
[ F.6.9.6 ] Case studies
[ F.6.9.7 ] References
[ F.6.10 ] histogram2d_sp
[ F.6.10.1 ] Summary
[ F.6.10.2 ] Input
[ F.6.10.3 ] Output
[ F.6.10.4 ] Implementation
[ F.6.10.5 ] Example
[ F.6.10.6 ] Case studies
[ F.6.11 ] mahalanobis_dist
[ F.6.11.1 ] Summary
[ F.6.11.2 ] Input
[ F.6.11.3 ] Output
[ F.6.11.4 ] Implementation
[ F.6.11.5 ] Example
[ F.6.11.6 ] Case studies
[ F.6.11.7 ] References
[ F.6.12 ] naive_selection
[ F.6.12.1 ] Summary
[ F.6.12.2 ] Input
[ F.6.12.3 ] Output
[ F.6.12.4 ] Implementation
[ F.6.12.5 ]</