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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.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 ] Spectrum of the covariance matrix: normal noise
[ 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 ] Sample covariance and eigenvalue dispersion
[ 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_location_estimators
[ 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 ] s_sample_mean_covariance
[ S.3.75.1 ] Summary
[ S.3.75.2 ] Parameters
[ S.3.75.3 ] Outcomes and figures
[ S.3.75.4 ] Implementation
[ S.3.75.5 ] See also
[ S.3.76 ] Estimation assessment of a moment-based functional of a mixture I
[ S.3.77 ] Estimation assessment of a moment-based functional of a mixture II
[ S.3.78 ] Estimation assessment of the quantile of a mixture I
[ S.3.79 ] Estimation assessment of the quantile of a mixture II
[ S.3.80 ] s_location_stress_error
[ S.3.80.1 ] Summary
[ S.3.80.2 ] Parameters
[ S.3.80.3 ] Outcomes and figures
[ S.3.80.4 ] Implementation
[ S.3.80.5 ] See also
[ S.3.81 ] Estimation assessment
[ S.3.82 ] Sample mean and sample covariance estimation error
[ S.3.83 ] Sample quantiles distribution
[ S.3.84 ] 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.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_lfm
[ 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_factor_analysis_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 ] Parameters
[ S.12.8.3 ] Outcomes and figures
[ S.12.8.4 ] 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.12.29 ] s_cpca_vs_pca
[ S.12.29.1 ] Script summary
[ S.12.29.2 ] Parameters
[ S.12.29.3 ] Outcomes and figures
[ S.12.29.4 ] Implementation
[ S.12.29.5 ] See also
[ 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_logn_mean_lin_regression
[ S.14.2.1 ] Summary
[ S.14.2.2 ] Parameters
[ S.14.2.3 ] Outcomes and figures
[ S.14.2.4 ] Implementation
[ S.14.2.5 ] See also
[ S.14.3 ] s_continuum_discrete_point_pred
[ S.14.3.1 ] Summary
[ S.14.3.2 ] Parameters
[ S.14.3.3 ] Outcomes and figures
[ S.14.3.4 ] Implementation
[ S.14.3.5 ] See also
[ S.14.4 ] s_sp_anova
[ S.14.4.1 ] Summary
[ S.14.4.2 ] Data
[ S.14.4.3 ] Parameters
[ S.14.4.4 ] Outcomes and figures
[ S.14.4.5 ] Implementation
[ S.14.4.6 ] See also
[ S.14.5 ] s_interactions
[ S.14.5.1 ] Summary
[ S.14.5.2 ] Input parameters
[ S.14.5.3 ] Outcomes and figures
[ S.14.5.4 ] Implementation
[ S.14.5.5 ] See also
[ S.14.6 ] s_kernel_trick
[ S.14.7 ] s_cart
[ S.14.7.1 ] Summary
[ S.14.7.2 ] Data
[ S.14.7.3 ] Parameters
[ S.14.7.4 ] Outcomes and figures
[ S.14.7.5 ] Implementation
[ S.14.8 ] s_ann_regression
[ S.14.8.1 ] Summary
[ S.14.8.2 ] Data
[ S.14.8.3 ] Input parameters
[ S.14.8.4 ] Outcomes and figures
[ S.14.8.5 ] Implementation
[ S.14.9 ] s_gradient_boosting
[ 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_logn_quant_regression
[ S.14.10.1 ] Summary
[ S.14.10.2 ] Parameters
[ S.14.10.3 ] Outcomes and figures
[ S.14.10.4 ] Implementation
[ S.14.10.5 ] See also
[ S.14.11 ] s_logn_quant_lin_regression
[ S.14.11.1 ] Summary
[ S.14.11.2 ] Parameters
[ S.14.11.3 ] Outcomes and figures
[ S.14.11.4 ] Implementation
[ S.14.11.5 ] See also
[ S.14.12 ] s_binary_margin_losses
[ S.14.12.1 ] Summary
[ S.14.12.2 ] Parameters
[ S.14.12.3 ] Outcomes and figures
[ S.14.12.4 ] Implementation
[ S.14.13 ] s_point_classification_normal_mixtures
[ S.14.13.1 ] Summary
[ S.14.13.2 ] Parameters
[ S.14.13.3 ] Outcomes and figures
[ S.14.13.4 ] Implementation
[ S.14.13.5 ] See also
[ S.14.14 ] s_linclass_regression
[ S.14.15 ] s_linclass_fda
[ S.14.15.1 ] Summary
[ S.14.15.2 ] Data
[ S.14.15.3 ] Outcomes and figures
[ S.14.15.4 ] Implementation
[ S.14.15.5 ] See also
[ S.14.16 ] s_linclass_perceptron
[ S.14.16.1 ] Summary
[ S.14.16.2 ] Data
[ S.14.16.3 ] Outcomes and figures
[ S.14.16.4 ] Implementation
[ S.14.17 ] s_linclass_svm
[ S.14.17.1 ] Summary
[ S.14.17.2 ] Data
[ S.14.17.3 ] Parameters
[ S.14.17.4 ] Outcomes and figures
[ S.14.17.5 ] Implementation
[ S.14.18 ] s_linclass_ann_svm
[ S.14.19 ] s_autoencoders_kmeans
[ 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.15 ] Probabilistic machine learning models
[ S.15.1 ] s_factor_analysis_algos
[ 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.15.2 ] s_continuum_discrete_generative_pred
[ 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.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 spatial 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.III. Valuation
[ S.18 ] Executive summary
[ S.19 ] Background definitions
[ S.20 ] Linear pricing theory: core
[ S.20.1 ] s_current_values
[ 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 ] Stochastic discount factor comparison
[ S.20.3 ] s_fund_theorem_mre
[ S.20.3.1 ] Summary
[ S.20.3.2 ] Data
[ S.20.3.3 ] Outcomes and figures
[ S.20.3.4 ] Implementation
[ S.20.3.5 ] See also
[ S.20.4 ] Fundamental theorem of asset pricing with forward numeraire
[ S.20.5 ] s_risk_neutral_density
[ S.20.5.1 ] Summary
[ S.20.5.2 ] Data
[ S.20.5.3 ] Parameters
[ S.20.5.4 ] Outcomes and figures
[ S.20.5.5 ] Implementation
[ S.20.6 ] s_capm_like_identity
[ S.20.6.1 ] 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.21 ] Linear pricing theory: further assumptions
[ S.21.1 ] s_simulate_call
[ S.21.1.1 ] Summary
[ S.21.1.2 ] Parameters
[ S.21.1.3 ] Outcomes and figures
[ S.21.1.4 ] Implementation
[ S.21.1.5 ] See also
[ S.21.2 ] s_intertemporal_valuation
[ S.21.2.1 ] Summary
[ S.21.2.2 ] Input
[ S.21.2.3 ] Outcomes and figures
[ S.21.2.4 ] Implementation
[ S.21.2.5 ] Tips
[ S.21.2.6 ] See also
[ S.21.3 ] s_intertemporal_valuation_complete
[ S.21.3.1 ] Summary
[ S.21.3.2 ] Input
[ S.21.3.3 ] Outcomes and figures
[ S.21.3.4 ] Implementation
[ S.21.3.5 ] See also
[ S.21.4 ] Minimum variance factor-replicating portfolio
[ S.22 ] Non-linear pricing theory
[ S.23 ] Valuation implementation
[ S.23.1 ] Fitting implied volatility from the Black-Scholes-Merton valuation
S.IV. Performance analysis
[ S.24 ] Executive summary
[ S.25 ] Performance definitions
[ S.25.1 ] Compounded return of a portfolio
[ S.26 ] Performance attribution
S.V. Quant toolbox
[ S.27 ] Summary
[ S.28 ] Distributions
[ S.28.1 ] Conditional expectation between normal random variables
[ S.28.2 ] Innovation: the normal case, implementation
[ S.28.3 ] Innovation: the lognormal case, implementation
[ S.28.4 ] s_bivariate_normal
[ S.28.4.1 ] Summary
[ S.28.4.2 ] Parameters
[ S.28.4.3 ] Outcomes and figures
[ S.28.4.4 ] Implementation
[ S.28.5 ] s_uniform_inside_circle
[ S.28.5.1 ] Summary
[ S.28.5.2 ] Parameters
[ S.28.5.3 ] Outcomes and figures
[ S.28.5.4 ] Implementation
[ S.28.6 ] s_elliptical_uniform_radial_rep
[ S.28.6.1 ] Summary
[ S.28.6.2 ] Parameters
[ S.28.6.3 ] Outcomes and figures
[ S.28.6.4 ] Implementation
[ S.28.7 ] s_simulate_unif_in_ellipse
[ S.28.7.1 ] Summary
[ S.28.7.2 ] Parameters
[ S.28.7.3 ] Outcomes and figures
[ S.28.7.4 ] Implementation
[ S.28.7.5 ] See also
[ S.28.8 ] s_q_chi_grid
[ S.28.8.1 ] Summary
[ S.28.8.2 ] Parameters
[ S.28.8.3 ] Outcomes and figures
[ S.28.8.4 ] Implementation
[ S.28.8.5 ] See also
[ S.28.9 ] s_scen_prob_pdf
[ S.28.9.1 ] Summary
[ S.28.9.2 ] Parameters
[ S.28.9.3 ] Outcomes and figures
[ S.28.9.4 ] Implementation
[ S.28.9.5 ] See also
[ S.28.10 ] s_scen_prob_cdf_quantile
[ S.28.10.1 ] Summary
[ S.28.10.2 ] Input
[ S.28.10.3 ] Outcomes
[ S.28.10.4 ] Implementation
[ S.28.10.5 ] See also
[ S.28.11 ] s_normal_exponential_family
[ S.28.11.1 ] Summary
[ S.28.11.2 ] Parameters
[ S.28.11.3 ] Outcomes
[ S.28.11.4 ] Implementation
[ S.28.11.5 ] See also
[ S.28.12 ] s_saddle_point_vs_mcfp_quadn
[ S.28.12.1 ] Summary
[ S.28.12.2 ] Parameters
[ S.28.12.3 ] Outcomes and figures
[ S.28.12.4 ] Implementation
[ S.28.12.5 ] See also
[ S.28.13 ] s_bivariate_wishart
[ S.28.13.1 ] Summary
[ S.28.13.2 ] Input
[ S.28.13.3 ] Outcomes and figures
[ S.28.13.4 ] Implementation
[ S.28.13.5 ] See also
[ S.28.14 ] s_gaussian_mixture
[ S.28.14.1 ] Summary
[ S.28.14.2 ] Parameters
[ S.28.14.3 ] Outcomes and figures
[ S.28.14.4 ] Implementation
[ S.28.14.5 ] See also
[ S.28.15 ] s_discrete_partitioned_variable
[ S.29 ] Geometry of distributions
[ S.30 ] Copulas
[ S.30.1 ] s_nlg_to_uniform
[ 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_uniform_to_normal
[ 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.30.3 ] s_uniform_to_lognorm
[ S.30.3.1 ] Summary
[ S.30.3.2 ] Parameters
[ S.30.3.3 ] Outcomes and figures
[ S.30.3.4 ] Implementation
[ S.30.3.5 ] See also
[ S.30.4 ] s_uniform_to_gamma
[ S.30.4.1 ] Summary
[ S.30.4.2 ] Parameters
[ S.30.4.3 ] Outcomes and figures
[ S.30.4.4 ] Implementation
[ S.30.4.5 ] See also
[ S.30.5 ] Combination: from FP-copula/historical marginal to joint
[ S.30.6 ] s_ncop_nmarg
[ S.30.6.1 ] Script summary
[ S.30.6.2 ] Parameters
[ S.30.6.3 ] Outcomes and figures
[ S.30.6.4 ] Implementation
[ S.30.6.5 ] See also
[ S.30.7 ] s_tcop_tmarg
[ S.30.7.1 ] Script summary
[ S.30.7.2 ] Parameters
[ S.30.7.3 ] Outcomes and figures
[ S.30.7.4 ] Implementation
[ S.30.7.5 ] See also
[ S.30.8 ] s_display_norm_copula
[ S.30.8.1 ] Summary
[ S.30.8.2 ] Parameters
[ S.30.8.3 ] Outcomes and figures
[ S.30.8.4 ] Implementation
[ S.30.8.5 ] See also
[ S.30.9 ] s_display_t_copula
[ S.30.9.1 ] Summary
[ S.30.9.2 ] Parameters
[ S.30.9.3 ] Outcomes and figures
[ S.30.9.4 ] Implementation
[ S.30.9.5 ] See also
[ S.30.10 ] s_copula_returns
[ S.30.10.1 ] Summary
[ S.30.10.2 ] Parameters
[ S.30.10.3 ] Outcomes and figures
[ S.30.10.4 ] Implementation
[ S.30.10.5 ] See also
[ S.30.11 ] s_cop_marg_separation
[ S.30.11.1 ] Script summary
[ S.30.11.2 ] Parameters
[ S.30.11.3 ] Outcomes and figures
[ S.30.11.4 ] Implementation
[ S.30.11.5 ] See also
[ S.30.12 ] s_cop_marg_combination
[ S.30.12.1 ] Script summary
[ S.30.12.2 ] Parameters
[ S.30.12.3 ] Outcomes and figures
[ S.30.12.4 ] Implementation
[ S.30.12.5 ] See also
[ S.30.13 ] s_t_copula_norm_marginals
[ S.30.13.1 ] Summary
[ S.30.13.2 ] Parameters
[ S.30.13.3 ] Outcomes and figures
[ S.30.13.4 ] Implementation
[ S.30.13.5 ] See also
[ S.30.14 ] Separation: from historical joint to copula/marginal
[ S.30.15 ] Display the pdf of a bivariate t-copula
[ S.31 ] Correlation and generalizations
[ S.31.1 ] s_norm_const_sw
[ S.31.1.1 ] Summary
[ S.31.1.2 ] Parameters
[ S.31.1.3 ] Outcomes and figures
[ S.31.1.4 ] Implementation
[ S.31.2 ] s_dependence_structure_call_put
[ S.31.2.1 ] Summary
[ S.31.2.2 ] Parameters
[ S.31.2.3 ] Outcomes and figures
[ S.31.2.4 ] Implementation
[ S.31.2.5 ] See also
[ S.31.3 ] Correlation between normal variables (numerical proof)
[ S.31.4 ] Correlation between lognormal variables (numerical proof)
[ S.31.5 ] Correlation between normalized Wishart marginals(numerical proof)
[ S.31.6 ] s_uncorr_no_indep
[ S.31.6.1 ] Summary
[ S.31.6.2 ] Parameters
[ S.31.6.3 ] Outcomes and figures
[ S.31.6.4 ] Implementation
[ S.31.6.5 ] See also
[ S.31.7 ] s_clt_student_t
[ S.31.7.1 ] Summary
[ S.31.7.2 ] Parameters
[ S.31.7.3 ] Outcomes and figures
[ S.31.7.4 ] Implementation
[ S.31.7.5 ] See also
[ S.31.8 ] s_full_dependence
[ S.31.8.1 ] Summary
[ S.31.8.2 ] Parameters
[ S.31.8.3 ] Outcomes and figures
[ S.31.8.4 ] Implementation
[ S.31.8.5 ] See also
[ S.31 ] Stochastic dominance
[ S.31.1 ] s_strong_dominance
[ S.31.1.1 ] Summary
[ S.31.1.2 ] Parameters
[ S.31.1.3 ] Outcomes and figures
[ S.31.1.4 ] Implementation
[ S.31.1.5 ] See also
[ S.31.2 ] s_weak_dominance
[ S.31.2.1 ] Summary
[ S.31.2.2 ] Parameters
[ S.31.2.3 ] Outcomes and figures
[ S.31.2.4 ] Implementation
[ S.31.2.5 ] See also
[ S.31.3 ] s_second_order_dominance
[ S.31.3.1 ] Summary
[ S.31.3.2 ] Parameters
[ S.31.3.3 ] Outcomes and figures
[ S.31.3.4 ] Implementation
[ S.32 ] Location and dispersion
[ S.32.1 ] s_logn_uncertainty_bands
[ S.32.1.1 ] Summary
[ S.32.1.2 ] Parameters
[ S.32.1.3 ] Outcomes and figures
[ S.32.1.4 ] Implementation
[ S.32.2 ] Different visualizations of the multivariate uncertainty band
[ S.32.3 ] s_affine_equiv_exp_cov
[ S.32.3.1 ] Summary
[ S.32.3.2 ] Parameters
[ S.32.3.3 ] Outcomes and figures
[ S.32.3.4 ] Implementation
[ S.32.3.5 ] See also
[ S.32.4 ] Ellipsoid and uncertainty band of a bivariate normal
[ S.32.5 ] s_ellipsoid_multiv_exp_cov
[ S.32.5.1 ] Summary
[ S.32.5.2 ] Parameters
[ S.32.5.3 ] Outcomes and figures
[ S.32.5.4 ] Implementation
[ S.32.5.5 ] See also
[ S.32.6 ] s_display_corr_norm_ellips
[ S.32.6.1 ] Summary
[ S.32.6.2 ] Parameters
[ S.32.6.3 ] Outcomes and figures
[ S.32.6.4 ] Implementation
[ S.32.6.5 ] See also
[ S.32.7 ] Ellipsoid and uncertainty band of a bivariate lognormal
[ S.32.8 ] Ellipsoid and uncertainty band of normalized Wishart marginals
[ S.32.9 ] s_affine_equiv_mode_mod_disp
[ S.32.9.1 ] Summary
[ S.32.9.2 ] Parameters
[ S.32.9.3 ] Outcomes and figures
[ S.32.9.4 ] Implementation
[ S.32.9.5 ] See also
[ S.32.10 ] Iso-contour of a lognormal bivariate distribution
[ S.32.11 ] s_chebyshev_ineq
[ S.32.11.1 ] Summary
[ S.32.11.2 ] Parameters
[ S.32.11.3 ] Outcomes and figures
[ S.32.11.4 ] Implementation
[ S.32.11.5 ] See also
[ S.32.12 ] Expectation-covariance as minimum volume ellipsoid
[ S.32.13 ] R-squared: numerical example
[ S.32.14 ] Euclidean vectors of joint normal variables
[ S.32.15 ] s_lognormal_l2_geometry
[ S.32.15.1 ] Summary
[ S.32.15.2 ] Parameters
[ S.32.15.3 ] Outcomes and figures
[ S.32.15.4 ] Implementation
[ S.32.15.5 ] See also
[ S.32.16 ] Orthogonality of principal directions
[ S.32.17 ] Euclidean vectors via linear transformation and Riccati root
[ S.33 ] Bias reduction
[ S.33.1 ] s_bias_reduction_toy
[ S.34 ] Decision theory with model uncertainty
[ S.35 ] Estimation techniques
[ S.35.1 ] Maximum likelihood estimation
[ S.36 ] Estimation and assessment
[ S.36.1 ] s_bias_vs_variance_lognormal
[ S.36.2 ] Predictive distribution assessment
[ S.38 ] Estimation and regularization
[ S.38.1 ] s_encoding
[ S.39 ] Hypothesis testing
[ S.40 ] Stochastic processes cheat sheet
[ S.40.1 ] s_discrete_partitioned_process
[ S.40.1.1 ] Summary
[ S.40.1.2 ] Input
[ S.40.1.3 ] Outcomes and figures
[ S.40.1.4 ] Implementation
[ S.41 ] Invariance tests
[ S.41.1 ] s_elltest_normal
[ S.41.1.1 ] Summary
[ S.41.1.2 ] Parameters
[ S.41.1.3 ] Outcomes and figures
[ S.41.1.4 ] Implementation
[ S.41.1.5 ] See also
[ S.41.2 ] Kolmogorov-Smirnov test for invariance on simulations
[ S.41.3 ] Copula based tests for invariance on simulations
[ S.41.4 ] Invariance tests on realized time series
[ S.42 ] Continuous time processes
[ S.42.1 ] s_projection_brownian_motion
[ 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_projection_univ_rating
[ S.42.2.1 ] Summary
[ S.42.2.2 ] Data
[ S.42.2.3 ] Outcomes and figures
[ S.42.2.4 ] Implementation
[ S.42.2.5 ] See also
[ S.42.3 ] Projection of Poisson process
[ S.42.4 ] Simulate compound Poisson processes
[ S.42.5 ] Projection of a Compound Poisson Process
[ S.42.6 ] Projection of Cauchy distribution
[ S.42.7 ] Projection of the variance gamma distribution
[ S.42.8 ] Simulate the NIG and VG processes
[ S.42.9 ] Simulation of the variance gamma process by subordination
[ S.42.10 ] Projection of the Student t distribution
[ S.42.11 ] Projection of the uniform distribution
[ S.42.12 ] Projection of fractional Brownian motion
[ S.42.13 ] Projection of the Heston process
[ S.42.14 ] Simulation of the Heston process by Time Change
[ S.42.15 ] Simulation of a Ornstein-Uhlenbeck process
[ S.42.16 ] MVOU change of coordinates
[ S.42.17 ] Projection of conditional mean and covariance of the MVOU process
[ S.43 ] Covariance stationary processes
[ S.43.1 ] s_autocov_spec_dens_ar1
[ S.43.1.1 ] Summary
[ S.43.1.2 ] Parameters
[ S.43.1.3 ] Outcomes and figures
[ S.43.1.4 ] Implementation
[ S.43.2 ] s_spectral_representation
[ S.43.2.1 ] Summary
[ S.43.2.2 ] Parameters
[ S.43.2.3 ] Outcomes and figures
[ S.43.2.4 ] Implementation
[ S.43.2.5 ] Tips
[ S.43.2.6 ] See also
[ S.43.3 ] s_toeplitz_spectral
[ S.43.3.1 ] Summary
[ S.43.3.2 ] Parameters
[ S.43.3.3 ] Outcomes and figures
[ S.43.3.4 ] Implementation
[ S.43.3.5 ] See also
[ S.43.4 ] s_bandpass_filter_ar1
[ S.43.4.1 ] Summary
[ S.43.4.2 ] Parameters
[ S.43.4.3 ] Outcomes and figures
[ S.43.4.4 ] Implementation
[ S.43.4.5 ] See also
[ S.44 ] Signals
[ S.44.1 ] s_cointegration_detection
[ S.44.1.1 ] Summary
[ S.44.1.2 ] Data
[ S.44.1.3 ] Parameters
[ S.44.1.4 ] Outcomes and figures
[ S.44.1.5 ] Implementation
[ S.44.1.6 ] See also
[ S.44.2 ] s_cointegration_signal
[ 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_trade_autocorr_signal
[ 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_order_imbal_signal
[ S.44.4.1 ] Summary
[ S.44.4.2 ] Data
[ S.44.4.3 ] Parameters
[ S.44.4.4 ] Outcomes and figures
[ S.44.4.5 ] Implementation
[ S.44.4.6 ] See also
[ S.44.5 ] s_price_pred_signal
[ S.44.5.1 ] Summary
[ S.44.5.2 ] Data
[ S.44.5.3 ] Parameters
[ S.44.5.4 ] Outcomes and figures
[ S.44.5.5 ] Implementation
[ S.44.5.6 ] See also
[ S.44.6 ] s_volume_cluster_signal
[ S.44.6.1 ] Summary
[ S.44.6.2 ] Data
[ S.44.6.3 ] Parameters
[ S.44.6.4 ] Outcomes and figures
[ S.44.6.5 ] Implementation
[ S.44.6.6 ] See also
[ S.44.7 ] s_momentum_signals
[ S.44.7.1 ] Summary
[ S.44.7.2 ] Data
[ S.44.7.3 ] Parameters
[ S.44.7.4 ] Outcomes and figures
[ S.44.7.5 ] Implementation
[ S.44.7.6 ] See also
[ S.44.8 ] Signal filtering for systematic strategy (size)
[ S.45 ] Black-Litterman
[ S.45.1 ] s_bl_equilibrium_ret
[ S.45.1.1 ] Summary
[ S.45.1.2 ] Data
[ S.45.1.3 ] Parameters
[ S.45.1.4 ] Outcomes
[ S.45.1.5 ] Implementation
[ S.45.1.6 ] See also
[ S.45.2 ] s_info_processing_comparison
[ 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.46 ] Optimization primer
[ S.46.1 ] s_convex_programming
[ S.46.1.1 ] Summary
[ S.46.1.2 ] Implementation
[ S.46.1.3 ] See also
[ S.46.2 ] s_selection_toy
[ 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 ] See also
[ S.46.3 ] s_factors_selection
[ 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.47 ] Linear algebra primer
[ S.47.1 ] s_vector_operations
[ S.47.1.1 ] Summary
[ S.47.1.2 ] Parameters
[ S.47.1.3 ] Outcomes and figures
[ S.47.1.4 ] Implementation
[ S.47.2 ] s_spectral_theorem
[ S.47.2.1 ] Summary
[ S.47.2.2 ] Parameters
[ S.47.2.3 ] Outcomes and figures
[ S.47.2.4 ] Implementation
[ S.47.2.5 ] See also
[ S.48 ] Calculus primer
[ S.48.1 ] s_univariate_derivatives
[ S.48.1.1 ] Summary
[ S.48.1.2 ] Parameters
[ S.48.1.3 ] Outcomes and figures
[ S.48.1.4 ] Implementation
[ S.48.2 ] s_multivariate_derivatives
[ S.48.2.1 ] Summary
[ S.48.2.2 ] Parameters
[ S.48.2.3 ] Outcomes and figures
[ S.48.2.4 ] Implementation
[ S.48.3 ] s_riemann_integration_univariate
[ S.48.3.1 ] Summary
[ S.48.3.2 ] Parameters
[ S.48.3.3 ] Outcomes and figures
[ S.48.3.4 ] Implementation
[ S.48.4 ] s_riemann_integration_multivariate
[ S.48.4.1 ] Summary
[ S.48.4.2 ] Parameters
[ S.48.4.3 ] Outcomes and figures
[ S.48.4.4 ] Implementation
[ S.48.5 ] s_saddlepoint
[ S.48.5.1 ] Summary
[ S.48.5.2 ] Parameters
[ S.48.5.3 ] Outcomes and figures
[ S.48.5.4 ] Implementation
[ S.49 ] Functional analysis primer
[ S.49.1 ] s_display_dirac_delta
[ S.49.1.1 ] Summary
[ S.49.1.2 ] Input
[ S.49.1.3 ] Outcomes and figures
[ S.49.1.4 ] Implementation
[ S.49.1.5 ] See also
S.VI. Case studies: The 10-Step Checklist
[ S.50 ] Historical Checklist
[ S.50.1 ] s_checklist_historical_step01
[ S.50.1.1 ] Summary
[ S.50.1.2 ] Data
[ S.50.1.3 ] Parameters
[ S.50.1.4 ] Outcomes and figures
[ S.50.1.5 ] Implementation
[ S.50.1.6 ] See also
[ S.50.2 ] s_checklist_historical_step02
[ S.50.2.1 ] Summary
[ S.50.2.2 ] Data
[ S.50.2.3 ] Parameters
[ S.50.2.4 ] Outcomes and figures
[ S.50.2.5 ] Implementation
[ S.50.2.6 ] See also
[ S.50.3 ] s_checklist_historical_step03
[ S.50.3.1 ] Summary
[ S.50.3.2 ] Data
[ S.50.3.3 ] Parameters
[ S.50.3.4 ] Outcomes and figures
[ S.50.3.5 ] Implementation
[ S.50.3.6 ] See also
[ S.50.4 ] s_checklist_historical_step04
[ S.50.4.1 ] Summary
[ S.50.4.2 ] Data
[ S.50.4.3 ] Parameters
[ S.50.4.4 ] Outcomes and figures
[ S.50.4.5 ] Implementation
[ S.50.4.6 ] See also
[ S.50.5 ] s_checklist_historical_step05
[ S.50.5.1 ] Summary
[ S.50.5.2 ] Data
[ S.50.5.3 ] Parameters
[ S.50.5.4 ] Outcomes and figures
[ S.50.5.5 ] Implementation
[ S.50.5.6 ] See also
[ S.50.6 ] s_checklist_historical_step06
[ S.50.6.1 ] Script summary
[ S.50.6.2 ] Data
[ S.50.6.3 ] Parameters
[ S.50.6.4 ] Outcomes and figures
[ S.50.6.5 ] Implementation
[ S.50.6.6 ] See also
[ S.50.7 ] s_checklist_historical_step07
[ S.50.7.1 ] Script summary
[ S.50.7.2 ] Data
[ S.50.7.3 ] Parameters
[ S.50.7.4 ] Outcomes and figures
[ S.50.7.5 ] Implementation
[ S.50.7.6 ] See also
[ S.50.8 ] s_checklist_historical_step08
[ S.50.8.1 ] Script summary
[ S.50.8.2 ] Data
[ S.50.8.3 ] Parameters
[ S.50.8.4 ] Outcomes and figures
[ S.50.8.5 ] Implementation
[ S.50.8.6 ] See also
[ S.50.9 ] s_checklist_historical_step09
[ S.50.9.1 ] Script summary
[ S.50.9.2 ] Data
[ S.50.9.3 ] Parameters
[ S.50.9.4 ] Outcomes and figures
[ S.50.9.5 ] Implementation
[ S.50.9.6 ] See also
[ S.50.10 ] s_checklist_historical_step10
[ S.50.10.1 ] Script summary
[ S.50.10.2 ] Data
[ S.50.10.3 ] Parameters
[ S.50.10.4 ] Outcomes and figures
[ S.50.10.5 ] Implementation
[ S.50.10.6 ] See also
[ S.51 ] Monte Carlo Checklist
[ S.51.1 ] s_checklist_montecarlo_step01
[ S.51.1.1 ] Summary
[ S.51.1.2 ] Data
[ S.51.1.3 ] Parameters
[ S.51.1.4 ] Outcomes and figures
[ S.51.1.5 ] Implementation
[ S.51.1.6 ] See also
[ S.51.2 ] s_checklist_montecarlo_step02
[ S.51.2.1 ] Summary
[ S.51.2.2 ] Data
[ S.51.2.3 ] Parameters
[ S.51.2.4 ] Outcomes and figures
[ S.51.2.5 ] Implementation
[ S.51.2.6 ] See also
[ S.51.3 ] s_checklist_montecarlo_step03
[ S.51.3.1 ] Summary
[ S.51.3.2 ] Data
[ S.51.3.3 ] Parameters
[ S.51.3.4 ] Outcomes and figures
[ S.51.3.5 ] Implementation
[ S.51.3.6 ] See also
[ S.51.4 ] s_checklist_montecarlo_step04
[ S.51.4.1 ] Summary
[ S.51.4.2 ] Data
[ S.51.4.3 ] Parameters
[ S.51.4.4 ] Outcomes and figures
[ S.51.4.5 ] Implementation
[ S.51.4.6 ] See also
[ S.51.5 ] s_checklist_montecarlo_step05
[ S.51.5.1 ] Summary
[ S.51.5.2 ] Data
[ S.51.5.3 ] Parameters
[ S.51.5.4 ] Outcomes and figures
[ S.51.5.5 ] Implementation
[ S.51.5.6 ] See also
[ S.51.6 ] s_checklist_montecarlo_step06
[ S.51.6.1 ] Script summary
[ S.51.6.2 ] Data
[ S.51.6.3 ] Parameters
[ S.51.6.4 ] Outcomes and figures
[ S.51.6.5 ] Implementation
[ S.51.6.6 ] See also
[ S.51.7 ] s_checklist_montecarlo_step07
[ S.51.7.1 ] Script summary
[ S.51.7.2 ] Data
[ S.51.7.3 ] Parameters
[ S.51.7.4 ] Outcomes and figures
[ S.51.7.5 ] Implementation
[ S.51.7.6 ] See also
[ S.51.8 ] s_checklist_montecarlo_step08
[ S.51.8.1 ] Script summary
[ S.51.8.2 ] Data
[ S.51.8.3 ] Parameters
[ S.51.8.4 ] Outcomes and figures
[ S.51.8.5 ] Implementation
[ S.51.8.6 ] See also
[ S.51.9 ] s_checklist_montecarlo_step09
[ S.51.9.1 ] Script summary
[ S.51.9.2 ] Data
[ S.51.9.3 ] Parameters
[ S.51.9.4 ] Outcomes and figures
[ S.51.9.5 ] Implementation
[ S.51.9.6 ] See also
[ S.51.10 ] s_checklist_montecarlo_step10
[ S.51.10.1 ] Script summary
[ S.51.10.2 ] Data
[ S.51.10.3 ] Parameters
[ S.51.10.4 ] Outcomes and figures
[ S.51.10.5 ] Implementation
[ S.51.10.6 ] See also
S.VII. Case studies: Factor models and learning
[ S.52 ] Principal component analysis of the yield curve
[ S.52.1 ] Eigenvectors for Toeplitz structure
[ S.52.2 ] s_yield_change_correlation
[ S.52.2.1 ] Summary
[ S.52.2.2 ] Data
[ S.52.2.3 ] Parameters
[ S.52.2.4 ] Outcomes and figures
[ S.52.2.5 ] Implementation
[ S.52.2.6 ] See also
[ S.52.3 ] s_pca_empirical
[ S.52.3.1 ] Summary
[ S.52.3.2 ] Data
[ S.52.3.3 ] Parameters
[ S.52.3.4 ] Outcomes and figures
[ S.52.3.5 ] Implementation
[ S.52.3.6 ] See also
[ S.52.4 ] s_pca_yield
[ S.52.4.1 ] Summary
[ S.52.4.2 ] Data
[ S.52.4.3 ] Outcomes and figures
[ S.52.4.4 ] Implementation
[ S.53 ] Machine learning for hedging
[ S.53.1 ] s_call_bms_hedging
[ S.53.1.1 ] Summary
[ S.53.1.2 ] Data
[ S.53.1.3 ] Outcomes and figures
[ S.53.1.4 ] Implementation
[ S.53.1.5 ] See also
[ S.53.2 ] s_call_least_squares_regression
[ S.53.2.1 ] Summary
[ S.53.2.2 ] Data
[ S.53.2.3 ] Parameters
[ S.53.2.4 ] Outcomes and figures
[ S.53.2.5 ] Implementation
[ S.53.2.6 ] See also
[ S.53.3 ] s_call_least_abs_distance_regression
[ S.53.3.1 ] Summary
[ S.53.3.2 ] Data
[ S.53.3.3 ] Parameters
[ S.53.3.4 ] Outcomes and figures
[ S.53.3.5 ] Implementation
[ S.53.3.6 ] See also
[ S.54 ] Probabilistic prediction in the stock market
[ S.54.1 ] s_market_prediction_regression
[ S.54.2 ] s_lasso_vs_ridge
[ S.54.2.1 ] Summary
[ S.54.2.2 ] Data
[ S.54.2.3 ] Parameters
[ S.54.2.4 ] Outcomes and figures
[ S.54.2.5 ] Implementation
[ S.54.2.6 ] See also
[ S.54.3 ] Regression LFM’s: randomized least square estimates
[ S.54.4 ] s_reg_lfm_bayes_prior_niw
[ S.54.4.1 ] Summary
[ S.54.4.2 ] Parameters
[ S.54.4.3 ] Outcomes and figures
[ S.54.4.4 ] Implementation
[ S.54.5 ] s_reg_lfm_bayes_posterior_niw
[ S.54.5.1 ] Summary
[ S.54.5.2 ] Parameters
[ S.54.5.3 ] Outcomes and figures
[ S.54.5.4 ] Implementation
[ S.54.5.5 ] See also
[ S.55 ] Credit default classification
[ S.55.1 ] s_default_probabilities
[ S.56 ] Clustering for the stock market
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_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.4.13 ] 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_t
[ 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 ] References
[ F.4.42.8 ] See also
[ F.4.43 ] simulate_unif_in_ellips
[ 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 ] Case studies
[ F.4.43.8 ] References
[ F.4.43.9 ] 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
[ 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