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Code
»
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 ]
Parameters
[ S.0.1.3 ]
Outcomes and figures
[ S.0.1.4 ]
Implementation
[ S.0.1.5 ]
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 ]
s_bond_convergence
[ S.1.3.1 ]
Summary
[ S.1.3.2 ]
Data
[ S.1.3.3 ]
Parameters
[ S.1.3.4 ]
Outcomes and figures
[ S.1.3.5 ]
Implementation
[ S.1.3.6 ]
See also
[ S.1.4 ]
Zero-coupon bond rolling price
[ S.1.5 ]
s_logrollvalues_vs_yields
[ 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_yield_curve_evolution
[ 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 ]
Log-yield representation
[ S.1.8 ]
s_analyze_rates_jgb
[ 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 ]
Shadow rates evolution
[ S.1.10 ]
Nelson-Siegel parametrization of ...
[ S.1.11 ]
Nelson-Siegel parametrization of ...
[ S.1.12 ]
Call options value
[ S.1.13 ]
Call options normalized value
[ S.1.14 ]
Implied volatility surface
[ S.1.15 ]
Delta parametrization of the impl...
[ S.1.16 ]
Log-implied volatility
[ S.1.17 ]
Inverse-call implied volatility
[ S.1.18 ]
Market quotes in delta-moneyness ...
[ S.1.19 ]
SVI parametrization of the implie...
[ S.1.20 ]
s_fx_rates_vs_logrates
[ S.1.20.1 ]
Summary
[ S.1.20.2 ]
Data
[ S.1.20.3 ]
Outcomes and figures
[ S.1.20.4 ]
Implementation
[ S.1.21 ]
Cumulative number of rating migra...
[ S.1.22 ]
s_default_merton_model
[ S.1.22.1 ]
Summary
[ S.1.22.2 ]
Parameters
[ S.1.22.3 ]
Outcomes and figures
[ S.1.22.4 ]
Implementation
[ S.1.23 ]
s_high_freq_stock_var
[ S.1.23.1 ]
Summary
[ S.1.23.2 ]
Data
[ S.1.23.3 ]
Parameters
[ S.1.23.4 ]
Outcomes and figures
[ S.1.23.5 ]
Implementation
[ S.1.23.6 ]
See also
[ S.1.24 ]
High frequency flow variables
[ S.1.25 ]
s_high_freq_tick_time
[ S.1.25.1 ]
Summary
[ S.1.25.2 ]
Data
[ S.1.25.3 ]
Parameters
[ S.1.25.4 ]
Outcomes and figures
[ S.1.25.5 ]
Implementation
[ S.1.25.6 ]
See also
[ S.1.26 ]
Volume time activity evolution of...
[ S.1.27 ]
Vasicek fit of the yield curve
[ S.1.28 ]
Heston parametrization of the imp...
[ S.1.29 ]
Rating thresholds and Merton mode...
[ S.1.30 ]
s_checklist_scenariobased_step01
[ S.2 ]
Quest for invariance
[ S.2.1 ]
Ellipsoid test for invariance on ...
[ S.2.2 ]
Kolmogorov-Smirnov test for invar...
[ S.2.3 ]
Ellipsoid test for invariance on ...
[ S.2.4 ]
Kolmogorov-Smirnov test for invar...
[ S.2.5 ]
Ellipsoid test for invariance on ...
[ S.2.6 ]
Kolmogorov-Smirnov test for invar...
[ 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 ]
Kolmogorov-Smirnov test for invar...
[ S.2.9 ]
Ellipsoid test for invariance on ...
[ S.2.10 ]
Kolmogorov-Smirnov test for invar...
[ S.2.11 ]
Ellipsoid test for invariance on ...
[ S.2.12 ]
Kolmogorov-Smirnov test for invar...
[ S.2.13 ]
Ellipsoid test for invariance in ...
[ S.2.14 ]
Kolmogorov-Smirnov test for invar...
[ S.2.15 ]
Ellipsoid test for invariance on ...
[ S.2.16 ]
Kolmogorov-Smirnov test for invar...
[ S.2.17 ]
Ellipsoid test for invariance on ...
[ S.2.18 ]
Kolmogorov-Smirnov test for invar...
[ S.2.19 ]
Estimation of a quantile of a mix...
[ 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 ]
Markov chain model for bid-ask sp...
[ S.2.23 ]
Next-step function for Markov cha...
[ S.2.24 ]
Long Memory in high frequency tra...
[ S.2.25 ]
Long Memory in high frequency tra...
[ S.2.26 ]
Volatility clustering in the stoc...
[ S.2.27 ]
Volatility Clustering in the stoc...
[ S.2.28 ]
P&L realizations modeled by a GAR...
[ S.2.29 ]
Ellipsoid test for invariance on ...
[ S.2.30 ]
Stochastic volatility and leverag...
[ S.2.31 ]
Implied leverage effect
[ S.2.32 ]
s_fit_yields_var1
[ S.2.32.1 ]
Summary
[ S.2.32.2 ]
Data
[ S.2.32.3 ]
Parameters
[ S.2.32.4 ]
Outcomes and figures
[ S.2.32.5 ]
Implementation
[ S.2.32.6 ]
See also
[ S.2.33 ]
Multivariate quest for invariance
[ S.2.34 ]
Error correction representation o...
[ S.2.35 ]
Invariants for a call option
[ S.2.36 ]
s_fit_garch_stocks
[ S.2.37 ]
s_checklist_scenariobased_step02
[ S.3 ]
Estimation
[ S.3.1 ]
Standard deviation estimation wit...
[ S.3.2 ]
State crisp probabilities
[ S.3.3 ]
Time exponential decay probabilit...
[ S.3.4 ]
Gaussian kernel and state conditi...
[ S.3.5 ]
s_glivenko_cantelli_hfp
[ S.3.5.1 ]
Summary
[ S.3.5.2 ]
Parameters
[ S.3.5.3 ]
Outcomes and figures
[ S.3.5.4 ]
Implementation
[ S.3.5.5 ]
See also
[ S.3.6 ]
s_min_entropy_fp
[ S.3.6.1 ]
Summary
[ S.3.6.2 ]
Data
[ S.3.6.3 ]
Parameters
[ S.3.6.4 ]
Outcomes and figures
[ S.3.6.5 ]
Implementation
[ S.3.6.6 ]
See also
[ S.3.7 ]
s_glivenko_cantelli
[ 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 flexi...
[ S.3.9 ]
HFP quantile dependence on flexib...
[ S.3.10 ]
Maximum of the effective number o...
[ S.3.11 ]
Quantile estimation: maximum like...
[ S.3.12 ]
Consistency of the maximum likeli...
[ S.3.13 ]
MLFP estimators for elliptical va...
[ S.3.14 ]
MLFP estimators for the Student t...
[ 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 flexi...
[ S.3.17 ]
Quantile function tail approximat...
[ S.3.18 ]
Non-robustness of sample mean and...
[ S.3.19 ]
Jackknife test on sample mean and...
[ S.3.20 ]
Sample mean and sample median bre...
[ S.3.21 ]
Minimum volume ellipsoid enclosin...
[ S.3.22 ]
Farthest outlier detection
[ S.3.23 ]
High breakdown point with flexibl...
[ S.3.24 ]
Robustness comparison between HFP...
[ S.3.25 ]
Method of moments with flexible p...
[ S.3.26 ]
Generalized method of moments wit...
[ S.3.27 ]
P&L unconditional distribution
[ S.3.28 ]
MLFP estimation of unconditional ...
[ S.3.29 ]
MLFP estimation of unconditional ...
[ S.3.30 ]
Non-synchronous data
[ S.3.31 ]
Outlier detection with flexible p...
[ S.3.32 ]
s_default_probabilities
[ S.3.33 ]
Expectation-maximization with fle...
[ S.3.34 ]
s_different_length_series
[ S.3.34.1 ]
Summary
[ S.3.34.2 ]
Data
[ S.3.34.3 ]
Parameters
[ S.3.34.4 ]
Outcomes and figures
[ S.3.34.5 ]
Implementation
[ S.3.34.6 ]
See also
[ S.3.35 ]
Proxies
[ S.3.36 ]
Fix non-synchroneity in HFP
[ S.3.37 ]
Flexible probabilities ensemble p...
[ S.3.38 ]
Hellinger distance and diversity ...
[ S.3.39 ]
Ensemble FP: curse of dimensional...
[ S.3.40 ]
Likelihood with flexible probabil...
[ S.3.41 ]
Alternative FP specifications: bo...
[ S.3.42 ]
Alternative FP specifications: Di...
[ S.3.43 ]
Copula-marginal estimation for th...
[ S.3.44 ]
Copula-marginal estimation for in...
[ S.3.45 ]
Standardization of invariants’ ...
[ 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 ]
Credit migrations as time homogen...
[ S.3.50 ]
EWMA: numerical example
[ S.3.51 ]
Backward/forward EWMA: numerical ...
[ S.3.52 ]
s_shrinkage_location
[ 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.3.53 ]
s_shrinkage_factor
[ 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.3.53.6 ]
See also
[ S.3.54 ]
s_shrink_corr_clusters
[ S.3.54.1 ]
Summary
[ S.3.54.2 ]
Datas
[ S.3.54.3 ]
Outcomes and figures
[ S.3.54.4 ]
Implementation
[ S.3.55 ]
Homogeneous cluster shrinkage
[ S.3.56 ]
Graphical lasso estimation of the...
[ S.3.57 ]
Random matrix theory: Marchenko-P...
[ S.3.58 ]
Numerical integration of Marchenk...
[ S.3.59 ]
Spectrum of the covariance matrix...
[ S.3.60 ]
Spectrum of the covariance matrix...
[ S.3.61 ]
s_shrink_spectrum_filt
[ S.3.61.1 ]
Summary
[ S.3.61.2 ]
Data
[ S.3.61.3 ]
Parameters
[ S.3.61.4 ]
Outcomes and figures
[ S.3.61.5 ]
Implementation
[ S.3.61.6 ]
See also
[ S.3.62 ]
Sparse matrix transformation shri...
[ S.3.63 ]
Shrinkage estimator of location: ...
[ S.3.64 ]
Sample covariance and eigenvalue ...
[ S.3.65 ]
Shrinkage estimator of dispersion...
[ S.3.66 ]
Random matrix theory: Wigner semi...
[ S.3.67 ]
Shrinkage estimators of location:...
[ S.3.68 ]
s_bayes_prior_niw
[ S.3.68.1 ]
Summary
[ S.3.68.2 ]
Parameters
[ S.3.68.3 ]
Outcomes and figures
[ S.3.68.4 ]
Implementation
[ S.3.69 ]
s_bayes_posterior_niw
[ S.3.69.1 ]
Summary
[ S.3.69.2 ]
Parameters
[ S.3.69.3 ]
Outcomes and figures
[ S.3.69.4 ]
Implementation
[ S.3.69.5 ]
See also
[ S.3.70 ]
s_bayesian_estimation
[ S.3.71 ]
Markov chain Monte Carlo: applica...
[ S.3.72 ]
Agnostic prior on correlation
[ S.3.73 ]
s_location_estimators
[ 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 ]
s_sample_mean_covariance
[ S.3.74.1 ]
Summary
[ S.3.74.2 ]
Parameters
[ S.3.74.3 ]
Outcomes and figures
[ S.3.74.4 ]
Implementation
[ S.3.74.5 ]
See also
[ S.3.75 ]
Estimation assessment of a moment...
[ S.3.76 ]
Estimation assessment of a moment...
[ S.3.77 ]
Estimation assessment of the quan...
[ S.3.78 ]
Estimation assessment of the quan...
[ S.3.79 ]
s_location_stress_error
[ S.3.79.1 ]
Summary
[ S.3.79.2 ]
Parameters
[ S.3.79.3 ]
Outcomes and figures
[ S.3.79.4 ]
Implementation
[ S.3.79.5 ]
See also
[ S.3.80 ]
Estimation assessment
[ S.3.81 ]
Sample mean and sample covariance...
[ S.3.82 ]
Sample quantiles distribution
[ S.3.83 ]
Distribution of conditioning vari...
[ S.3.84 ]
s_checklist_scenariobased_steps03...
[ S.3.85 ]
s_checklist_scenariobased_step03
[ S.4 ]
Projection
[ S.4.1 ]
Projection of historical with fle...
[ S.4.2 ]
Projection of the reflected shift...
[ S.4.3 ]
Sum of random variables via simul...
[ S.4.4 ]
Empirical verification of the squ...
[ S.4.5 ]
Projection of higher-order standa...
[ 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 ]
Projecting the credit structural ...
[ S.4.9 ]
s_projection_multiv_ratings
[ S.4.9.1 ]
Summary
[ S.4.9.2 ]
Data
[ S.4.9.3 ]
Outcomes and figures
[ S.4.9.4 ]
Implementation
[ S.4.9.5 ]
See also
[ S.4.10 ]
Hybrid historical and Monte Carlo...
[ S.4.11 ]
Projection of a MVOU process with...
[ S.4.12 ]
Multivariate GARCH: fit and proje...
[ S.4.13 ]
Equity market: linear vs. compoun...
[ S.4.14 ]
Projection of shadow rates via Mo...
[ S.4.15 ]
Projection of cluster aggregates:...
[ S.4.16 ]
Projection of risk drivers whose ...
[ S.4.17 ]
s_projection_stock_hfp
[ 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 ]
s_projection_stock_bootstrap
[ S.4.18.1 ]
Summary
[ S.4.18.2 ]
Data
[ S.4.18.3 ]
Parameters
[ S.4.18.4 ]
Outcomes and figures
[ S.4.18.5 ]
Implementation
[ S.4.18.6 ]
See also
[ S.4.19 ]
Projection via historical approac...
[ S.4.20 ]
Historical projection: bootstrap
[ S.4.21 ]
Projection via historical bootstr...
[ S.4.22 ]
Hybrid Monte Carlo-historical pro...
[ S.4.23 ]
Hybrid historical and Monte Carlo...
[ S.4.24 ]
Copula-marginal projection for ri...
[ S.4.25 ]
Risk propagation in the Heston pr...
[ S.4.26 ]
s_checklist_scenariobased_steps03...
[ S.4.27 ]
s_checklist_scenariobased_step04
[ S.5 ]
Pricing at the horizon
[ S.5.1 ]
s_pricing_stocks_hfp
[ 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...
[ 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...
[ S.5.11 ]
Call option value: parametric pro...
[ 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 approximat...
[ 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 nor...
[ S.5.16 ]
Decomposition of a coupon bond P&...
[ S.5.17 ]
Decomposition of a call option P&...
[ 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...
[ S.5.21 ]
Equity P&L’s: historical approa...
[ S.5.22 ]
Call option P&L: historical appro...
[ S.5.23 ]
s_checklist_scenariobased_step05
[ 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 ]
Aggregate return in the scenario ...
[ 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: scen...
[ 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 stock...
[ 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_checklist_scenariobased_step06
[ 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 ]
Satisfaction measures: scenario-p...
[ S.7.3 ]
s_evaluation_satis_scenprob
[ 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.7.3.6 ]
See also
[ S.7.4 ]
s_evaluation_satis_norm
[ 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.7.5 ]
Cornish Fisher expansion: an appl...
[ S.7.6 ]
s_evaluation_eco_cap
[ S.7.6.1 ]
Summary
[ S.7.6.2 ]
Data
[ S.7.6.3 ]
Parameters
[ S.7.6.4 ]
Outcomes
[ S.7.6.5 ]
Implementation
[ S.7.6.6 ]
See also
[ S.7.7 ]
s_evaluation_cornishfisher_stocks
[ S.7.7.1 ]
Summary
[ S.7.7.2 ]
Data
[ S.7.7.3 ]
Parameters
[ S.7.7.4 ]
Outcomes and figures
[ S.7.7.5 ]
Implementation
[ S.7.7.6 ]
See also
[ S.7.8 ]
The mean-lower partial moment tra...
[ S.7.9 ]
s_checklist_scenariobased_step07
[ 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 ]
Top-down and bottom-up exposures ...
[ S.8a.4 ]
s_attribution_hedging
[ S.8a.4.1 ]
Summary
[ S.8a.4.2 ]
Data
[ S.8a.4.3 ]
Outcomes and figures
[ S.8a.4.4 ]
Implementation
[ S.8a.4.5 ]
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 ]
Outcomes and figures
[ S.8b.1.4 ]
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.9a ]
Construction: portfolio optimizat...
[ S.9a.1 ]
Mean-variance efficient frontier:...
[ S.9a.2 ]
Mean-variance robust efficient fr...
[ S.9a.3 ]
s_checklist_scenariobased_step09
[ S.9a.4 ]
Portfolio optimization: S&P 500 s...
[ S.9b ]
Construction: cross-sectional str...
[ S.9b.1 ]
Characteristic portfolio construc...
[ S.9b.2 ]
s_characteristic_port_rev
[ S.9b.2.1 ]
Summary
[ S.9b.2.2 ]
Data
[ S.9b.2.3 ]
Parameters
[ S.9b.2.4 ]
Outcomes and figures
[ S.9b.2.5 ]
Implementation
[ S.9b.2.6 ]
See also
[ S.9b.3 ]
s_flexible_characteristic_port_re...
[ S.9b.3.1 ]
Summary
[ S.9b.3.2 ]
Data
[ S.9b.3.3 ]
Parameters
[ S.9b.3.4 ]
Outcomes and figures
[ S.9b.3.5 ]
Implementation
[ S.9b.3.6 ]
See also
[ S.9b.4 ]
s_generalized_flam_toy
[ S.9c ]
Construction: time series strateg...
[ S.9c.1 ]
s_dynamic_port_strats
[ S.9c.1.1 ]
Summary
[ S.9c.1.2 ]
Parameters
[ S.9c.1.3 ]
Outcomes and figures
[ S.9c.1.4 ]
Implementation
[ S.9c.1.5 ]
See also
[ S.9d ]
Construction: estimation and mode...
[ 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 trajector...
[ S.10.3 ]
Non-monotone trading trajectories...
[ 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_quan...
[ 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 multi...
[ S.10.7 ]
Liquidation trajectory and tradin...
[ S.10.8 ]
Liquidation trajectory and tradin...
[ 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.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 ]
Parameters
[ S.11.1.3 ]
Outcomes and figures
[ S.11.1.4 ]
Implementation
[ S.11.1.5 ]
See also
[ S.12 ]
Linear factor models: theory
[ S.12.1 ]
Fully observable factor model
[ S.12.2 ]
Fully observable models: study of...
[ S.12.3 ]
s_regression_lfm
[ S.12.3.1 ]
Summary
[ S.12.3.2 ]
Parameters
[ S.12.3.3 ]
Outcomes and figures
[ S.12.3.4 ]
Implementation
[ S.12.3.5 ]
See also
[ S.12.4 ]
s_normal_mean_regression
[ S.12.4.1 ]
Summary
[ S.12.4.2 ]
Parameters
[ S.12.4.3 ]
Outcomes and figures
[ S.12.4.4 ]
Implementation
[ S.12.4.5 ]
See also
[ S.12.5 ]
Regression LFM’s linear formula...
[ S.12.6 ]
s_display_corr_norm_ellips
[ S.12.6.1 ]
Summary
[ S.12.6.2 ]
Parameters
[ S.12.6.3 ]
Outcomes and figures
[ S.12.6.4 ]
Implementation
[ S.12.6.5 ]
See also
[ S.12.7 ]
s_principal_component_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 ]
Principal-component LFM’s: join...
[ S.12.9 ]
s_systematic_idiosyncratic_lfm
[ 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 ]
s_cross_section_lfm
[ S.12.10.1 ]
Summary
[ S.12.10.2 ]
Parameters
[ S.12.10.3 ]
Outcomes and figures
[ S.12.10.4 ]
Implementation
[ S.12.11 ]
Cross-sectional LFM’s loadings ...
[ S.12.12 ]
Cross-sectional factor extraction...
[ S.12.13 ]
Cross-sectional LFM’s optimal r...
[ S.12.14 ]
Factor-replicating portfolio conv...
[ S.12.15 ]
s_factor_replication_logn
[ S.12.15.1 ]
Summary
[ S.12.15.2 ]
Parameters
[ S.12.15.3 ]
Outcomes and figures
[ S.12.15.4 ]
Implementation
[ S.12.15.5 ]
See also
[ S.12.16 ]
Factor extraction: numerical test
[ S.12.17 ]
Comparison between observable and...
[ S.12.18 ]
s_cross_section_truncated_lfm
[ S.12.18.1 ]
Summary
[ S.12.18.2 ]
Data
[ S.12.18.3 ]
Parameters
[ S.12.18.4 ]
Outcomes and figures
[ S.12.18.5 ]
Implementation
[ S.12.18.6 ]
See also
[ S.12.19 ]
Comparison of LFM’s
[ S.12.20 ]
Correlation of residuals in gener...
[ S.12.21 ]
Correlation of residuals in gener...
[ S.12.22 ]
Hidden regression versus Principa...
[ S.12.23 ]
Symmetric regression: numerical e...
[ S.12.24 ]
Hidden factors: puzzle
[ S.13 ]
Linear factor models: estimation
[ S.13.1 ]
s_reg_truncated_lfm
[ S.13.1.1 ]
Summary
[ S.13.1.2 ]
Data
[ S.13.1.3 ]
Parameters
[ S.13.1.4 ]
Outcomes and figures
[ S.13.1.5 ]
Implementation
[ S.13.1.6 ]
See also
[ S.13.2 ]
Simulation of the distribution of...
[ S.13.3 ]
Regression LFM’s: truncated est...
[ S.13.4 ]
Regression LFM’s: generalized r...
[ S.13.5 ]
Principal-component LFM’s: trun...
[ S.13.6 ]
Cross-sectional LFM’s: generali...
[ S.14 ]
Non-parametric LFM’s: pitfalls
[ S.14.1 ]
Regression LFM’s: shifted-logno...
[ S.15 ]
Machine learning foundations
[ S.15.1 ]
s_continuum_discrete_model
[ S.15.1.1 ]
Summary
[ S.15.1.2 ]
Parameters
[ S.15.1.3 ]
Outcomes and figures
[ S.15.1.4 ]
Implementation
[ S.15.2 ]
s_continuum_discrete_point_pred
[ S.15.2.1 ]
Summary
[ S.15.2.2 ]
Parameters
[ S.15.2.3 ]
Outcomes and figures
[ S.15.2.4 ]
Implementation
[ S.15.3 ]
s_continuum_discrete_generative_p...
[ S.15.3.1 ]
Summary
[ S.15.3.2 ]
Parameters
[ S.15.3.3 ]
Outcomes and figures
[ S.15.3.4 ]
Implementation
[ S.15.4 ]
s_logn_mean_regression
[ S.15.4.1 ]
Summary
[ S.15.4.2 ]
Parameters
[ S.15.4.3 ]
Outcomes and figures
[ S.15.4.4 ]
Implementation
[ S.15.4.5 ]
See also
[ S.15.5 ]
s_logn_quant_regression
[ S.15.5.1 ]
Summary
[ S.15.5.2 ]
Parameters
[ S.15.5.3 ]
Outcomes and figures
[ S.15.5.4 ]
Implementation
[ S.15.5.5 ]
See also
[ S.15.6 ]
s_logn_mean_lin_regression
[ S.15.6.1 ]
Summary
[ S.15.6.2 ]
Parameters
[ S.15.6.3 ]
Outcomes and figures
[ S.15.6.4 ]
Implementation
[ S.15.6.5 ]
See also
[ S.15.7 ]
s_logn_quant_lin_regression
[ S.15.7.1 ]
Summary
[ S.15.7.2 ]
Parameters
[ S.15.7.3 ]
Outcomes and figures
[ S.15.7.4 ]
Implementation
[ S.15.7.5 ]
See also
[ S.15.8 ]
s_supervised_point_prediction
[ S.15.8.1 ]
Summary
[ S.15.8.2 ]
Parameters
[ S.15.8.3 ]
Outcomes and figures
[ S.15.8.4 ]
Implementation
[ S.15.8.5 ]
See also
[ S.15.9 ]
s_continuum_discrete_kmeans
[ S.15.9.1 ]
Summary
[ S.15.9.2 ]
Parameters
[ S.15.9.3 ]
Outcomes and figures
[ S.15.9.4 ]
Implementation
[ S.15.10 ]
s_bias_vs_variance_lognormal
[ S.16 ]
Estimation and assessment
[ S.16.1 ]
Predictive distribution assessmen...
[ S.17 ]
Bias-variance enhancements
[ S.17.1 ]
s_decision_tree_normal
[ S.17.1 ]
s_encoding
[ S.17.2 ]
s_ann_normal
[ S.17.3 ]
s_perceptron_normal
[ S.17.4 ]
s_svm_normal
[ S.17.5 ]
s_bias_vs_variance_normal
[ S.17.6 ]
s_gradient_boosting
[ S.18 ]
Deep learning
[ S.19 ]
Dynamic models
[ S.19.1 ]
s_dyn_principal_component_var
[ S.19.1.1 ]
Summary
[ S.19.1.2 ]
Parameters
[ S.19.1.3 ]
Outcomes and figures
[ S.19.1.4 ]
Implementation
[ S.19.1.5 ]
See also
[ S.19.2 ]
s_kalman_filter_yield_curve
[ 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_hidden_markov_model_stocks
[ 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.20 ]
Application: principal component ...
[ S.20.1 ]
Eigenvectors for Toeplitz structu...
[ S.20.2 ]
s_pca_empirical
[ S.20.2.1 ]
Summary
[ S.20.2.2 ]
Data
[ S.20.2.3 ]
Parameters
[ S.20.2.4 ]
Outcomes and figures
[ S.20.2.5 ]
Implementation
[ S.20.2.6 ]
See also
[ S.20.3 ]
s_pca_continuum_limit
[ S.20.3.1 ]
Summary
[ S.20.3.2 ]
Parameters
[ S.20.3.3 ]
Outcomes and figures
[ S.20.3.4 ]
Implementation
[ S.20.3.5 ]
See also
[ S.21 ]
Application: market prediction re...
[ S.21.1 ]
Equivalent formulations of maximu...
[ S.21.2 ]
s_market_prediction_regression
[ S.21.3 ]
s_lasso_vs_ridge
[ S.21.3.1 ]
Summary
[ S.21.3.2 ]
Data
[ S.21.3.3 ]
Parameters
[ S.21.3.4 ]
Outcomes and figures
[ S.21.3.5 ]
Implementation
[ S.21.3.6 ]
See also
[ S.21.4 ]
Regression LFM’s: randomized le...
[ S.21.5 ]
s_reg_lfm_bayes_prior_niw
[ S.21.5.1 ]
Summary
[ S.21.5.2 ]
Parameters
[ S.21.5.3 ]
Outcomes and figures
[ S.21.5.4 ]
Implementation
[ S.21.6 ]
s_reg_lfm_bayes_posterior_niw
[ S.21.6.1 ]
Summary
[ S.21.6.2 ]
Parameters
[ S.21.6.3 ]
Outcomes and figures
[ S.21.6.4 ]
Implementation
[ S.21.6.5 ]
See also
[ S.21.7 ]
s_factors_selection
[ S.21.7.1 ]
Summary
[ S.21.7.2 ]
Parameters
[ S.21.7.3 ]
Outcomes and figures
[ S.21.7.4 ]
Implementation
[ S.21.7.5 ]
See also
[ S.22 ]
Application: credit default
[ S.23 ]
Application: clustering
S.III. Valuation
[ S.24 ]
Executive summary
[ S.25 ]
Background definitions
[ S.26 ]
Linear pricing theory: core
[ S.26.1 ]
s_current_values
[ S.26.1.1 ]
Summary
[ S.26.1.2 ]
Data
[ S.26.1.3 ]
Parameters
[ S.26.1.4 ]
Outcomes and figures
[ S.26.1.5 ]
Implementation
[ S.26.1.6 ]
See also
[ S.26.2 ]
Stochastic discount factor compar...
[ S.26.3 ]
s_fund_theorem
[ S.26.3.1 ]
Summary
[ S.26.3.2 ]
Data
[ S.26.3.3 ]
Parameters
[ S.26.3.4 ]
Outcomes and figures
[ S.26.3.5 ]
Implementation
[ S.26.3.6 ]
See also
[ S.26.4 ]
s_plot_sdf_mre
[ S.26.5 ]
Fundamental theorem of asset pric...
[ S.26.6 ]
s_risk_neutral_density
[ S.26.6.1 ]
Summary
[ S.26.6.2 ]
Data
[ S.26.6.3 ]
Parameters
[ S.26.6.4 ]
Outcomes and figures
[ S.26.6.5 ]
Implementation
[ S.26.7 ]
s_capm_like_identity
[ S.26.7.1 ]
Summary
[ S.26.7.2 ]
Data
[ S.26.7.3 ]
Parameters
[ S.26.7.4 ]
Outcomes and figures
[ S.26.7.5 ]
Implementation
[ S.26.7.6 ]
See also
[ S.27 ]
Linear pricing theory: further as...
[ S.27.1 ]
s_simulate_call
[ S.27.1.1 ]
Summary
[ S.27.1.2 ]
Data
[ S.27.1.3 ]
Parameters
[ S.27.1.4 ]
Outcomes and figures
[ S.27.1.5 ]
Implementation
[ S.27.2 ]
s_simulate_payoff
[ S.27.3 ]
Minimum variance factor-replicati...
[ S.28 ]
Non-linear pricing theory
[ S.29 ]
Valuation implementation
[ S.29.1 ]
Fitting implied volatility from t...
[ S.29.2 ]
Fitting yield curve from the Vasi...
S.IV. Performance analysis
[ S.30 ]
Executive summary
[ S.31 ]
Performance definitions
[ S.31.1 ]
Compounded return of a portfolio
[ S.32 ]
Performance attribution
S.V. Quant toolbox
[ S.33 ]
Summary
[ S.34 ]
Distributions
[ S.34.1 ]
Conditional expectation between n...
[ S.34.2 ]
Innovation: the normal case, impl...
[ S.34.3 ]
Innovation: the lognormal case, i...
[ S.34.4 ]
Uniform distribution inside the u...
[ S.34.5 ]
s_simulate_unif_in_ellipse
[ 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 ]
Exponential of the entropy for ex...
[ S.34.7 ]
Approximation of the Dirac delta ...
[ S.34.8 ]
s_scen_prob_pdf
[ S.34.8.1 ]
Summary
[ S.34.8.2 ]
Parameters
[ S.34.8.3 ]
Outcomes and figures
[ S.34.8.4 ]
Implementation
[ S.34.8.5 ]
See also
[ S.34.9 ]
Scenario-probability framework: u...
[ S.34.10 ]
Generalized Exponential of the en...
[ S.34.11 ]
s_normal_exponential_family
[ S.34.11.1 ]
Summary
[ S.34.11.2 ]
Parameters
[ S.34.11.3 ]
Outcomes
[ S.34.11.4 ]
Implementation
[ S.34.11.5 ]
See also
[ S.35 ]
Estimation techniques
[ S.35.1 ]
Maximum likelihood estimation
[ S.36 ]
Views processing
[ S.36.1 ]
s_min_rel_ent_point_view
[ S.36.1.1 ]
Summary
[ S.36.1.2 ]
Parameters
[ S.36.1.3 ]
Outcomes
[ S.36.1.4 ]
Implementation
[ S.36.1.5 ]
See also
[ S.36.2 ]
s_min_rel_ent_distr_view
[ S.36.2.1 ]
Summary
[ S.36.2.2 ]
Parameters
[ S.36.2.3 ]
Outcomes
[ S.36.2.4 ]
Implementation
[ S.36.2.5 ]
See also
[ S.36.3 ]
s_min_rel_ent_partial_view
[ S.36.3.1 ]
Summary
[ S.36.3.2 ]
Parameters
[ S.36.3.3 ]
Outcomes
[ S.36.3.4 ]
Implementation
[ S.36.3.5 ]
See also
[ S.36.4 ]
s_entropy_view
[ S.36.4.1 ]
Summary
[ S.36.4.2 ]
Parameters
[ S.36.4.3 ]
Outcomes and figures
[ S.36.4.4 ]
Implementation
[ S.36.4.5 ]
See also
[ S.36.5 ]
s_views_linear_exp
[ S.36.5.1 ]
Summary
[ S.36.5.2 ]
Parameters
[ S.36.5.3 ]
Outcomes
[ S.36.5.4 ]
Implementation
[ S.36.5.5 ]
See also
[ S.36.6 ]
s_views_gen_expectations
[ S.36.6.1 ]
Summary
[ S.36.6.2 ]
Parameters
[ S.36.6.3 ]
Outcomes
[ S.36.6.4 ]
Implementation
[ S.36.6.5 ]
See also
[ S.36.7 ]
s_views_sorted_exp
[ S.36.7.1 ]
Summary
[ S.36.7.2 ]
Parameters
[ S.36.7.3 ]
Outcomes
[ S.36.7.4 ]
Implementation
[ S.36.7.5 ]
See also
[ S.36.8 ]
s_views_st_deviations
[ S.36.8.1 ]
Summary
[ S.36.8.2 ]
Parameters
[ S.36.8.3 ]
Outcomes
[ S.36.8.4 ]
Implementation
[ S.36.8.5 ]
See also
[ S.36.9 ]
s_views_correlations
[ S.36.9.1 ]
Summary
[ S.36.9.2 ]
Parameters
[ S.36.9.3 ]
Outcomes
[ S.36.9.4 ]
Implementation
[ S.36.9.5 ]
See also
[ S.36.10 ]
s_views_cond_prob
[ S.36.10.1 ]
Summary
[ S.36.10.2 ]
Parameters
[ S.36.10.3 ]
Outcomes
[ S.36.10.4 ]
Implementation
[ S.36.10.5 ]
See also
[ S.36.11 ]
s_views_cdf
[ S.36.11.1 ]
Summary
[ S.36.11.2 ]
Parameters
[ S.36.11.3 ]
Outcomes
[ S.36.11.4 ]
Implementation
[ S.36.11.5 ]
See also
[ S.36.12 ]
s_views_cond_exp
[ S.36.12.1 ]
Summary
[ S.36.12.2 ]
Parameters
[ S.36.12.3 ]
Outcomes
[ S.36.12.4 ]
Implementation
[ S.36.12.5 ]
See also
[ S.36.13 ]
Convexity test for relative entro...
[ S.36.14 ]
Numerical verification of the gra...
[ S.36.15 ]
Numerical verification of gradien...
[ S.36.16 ]
Numerical verification of the Hes...
[ S.36.17 ]
Numerical and analytical Hessian ...
[ S.36.18 ]
Copula opinion pooling: uninforma...
[ S.37 ]
Black-Litterman
[ S.37.1 ]
s_bl_equilibrium_ret
[ S.37.1.1 ]
Summary
[ S.37.1.2 ]
Data
[ S.37.1.3 ]
Parameters
[ S.37.1.4 ]
Outcomes
[ S.37.1.5 ]
Implementation
[ S.37.1.6 ]
See also
[ S.37.2 ]
s_info_processing_comparison
[ S.37.2.1 ]
Summary
[ S.37.2.2 ]
Data
[ S.37.2.3 ]
Parameters
[ S.37.2.4 ]
Outcomes and figures
[ S.37.2.5 ]
Implementation
[ S.37.2.6 ]
See also
[ S.38 ]
Geometry of random variables
[ S.38.1 ]
R-squared: numerical example
[ S.38.2 ]
Euclidean vectors of joint normal...
[ S.38.3 ]
Euclidean vectors of joint lognor...
[ S.38.4 ]
Orthogonality of principal direct...
[ S.38.5 ]
Euclidean vectors via linear tran...
[ S.39 ]
Geometry of distributions
[ S.40 ]
Decision theory with model uncert...
[ S.41 ]
Copulas
[ S.41.1 ]
Display copula of a bivariate nor...
[ S.41.2 ]
Cdf-to-uniform mapping (normal di...
[ S.41.3 ]
s_lognorm_to_uniform
[ S.41.3.1 ]
Summary
[ S.41.3.2 ]
Parameters
[ S.41.3.3 ]
Outcomes and figures
[ S.41.3.4 ]
Implementation
[ S.41.3.5 ]
See also
[ S.41.4 ]
Cdf-to-uniform mapping (gamma dis...
[ S.41.5 ]
Uniform-to-cdf mapping (normal di...
[ S.41.6 ]
Uniform-to-cdf mapping (lognormal...
[ S.41.7 ]
Uniform-to-cdf mapping (gamma dis...
[ S.41.8 ]
Separation: from FP-joint to copu...
[ S.41.9 ]
Combination: from FP-copula/margi...
[ S.41.10 ]
Combination: from FP-copula/histo...
[ S.41.11 ]
s_copula_returns
[ S.41.11.1 ]
Summary
[ S.41.11.2 ]
Parameters
[ S.41.11.3 ]
Outcomes and figures
[ S.41.11.4 ]
Implementation
[ S.41.11.5 ]
See also
[ S.41.12 ]
Combination: from normal copula/m...
[ S.41.13 ]
Combination: from t-copula/margin...
[ S.41.14 ]
Separation: from historical joint...
[ S.41.15 ]
Display the pdf of a bivariate t-...
[ S.42 ]
Location and dispersion
[ S.42.1 ]
s_logn_uncertainty_bands
[ S.42.1.1 ]
Summary
[ S.42.1.2 ]
Parameters
[ S.42.1.3 ]
Outcomes and figures
[ S.42.1.4 ]
Implementation
[ S.42.2 ]
Different visualizations of the m...
[ S.42.3 ]
Ellipsoid and uncertainty band of...
[ S.42.4 ]
s_ellipsoid_multiv_loc_disp
[ S.42.4.1 ]
Summary
[ S.42.4.2 ]
Parameters
[ S.42.4.3 ]
Outcomes and figures
[ S.42.4.4 ]
Implementation
[ S.42.4.5 ]
See also
[ S.42.5 ]
Ellipsoid and uncertainty band of...
[ S.42.6 ]
Ellipsoid and uncertainty band of...
[ S.42.7 ]
Affine transformation of a bivari...
[ S.42.8 ]
Location-dispersion ellipsoid and...
[ S.42.9 ]
Iso-contour of a lognormal bivari...
[ S.42.10 ]
s_chebyshev_ineq
[ S.42.10.1 ]
Summary
[ S.42.10.2 ]
Parameters
[ S.42.10.3 ]
Outcomes and figures
[ S.42.10.4 ]
Implementation
[ S.42.10.5 ]
See also
[ S.42.11 ]
Expectation-covariance as minimum...
[ S.43 ]
Correlation and generalizations
[ S.43.1 ]
Schweizer-Wolff measure between n...
[ S.43.2 ]
Normalization constant of Schweiz...
[ S.43.3 ]
Normal copula and Frechet-Hoeffdi...
[ S.43.4 ]
Smooth approximation of call and ...
[ S.43.5 ]
Kendall’s tau between normal va...
[ S.43.6 ]
Spearman’s rho between normal v...
[ S.43.7 ]
Correlation between normal variab...
[ S.43.8 ]
Correlation between lognormal var...
[ S.43.9 ]
Correlation between normalized Wi...
[ S.43.10 ]
Correlation: normal stock prices,...
[ S.43.11 ]
Uncorrelation versus independence
[ S.43.12 ]
Uncorrelation versus independence...
[ S.43.13 ]
Fully dependent variables
[ 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 invar...
[ S.44.3 ]
Copula based tests for invariance...
[ S.44.4 ]
Invariance tests on realized time...
[ S.45 ]
Stochastic processes cheat sheet
[ S.46 ]
Continuous time processes
[ S.46.1 ]
s_projection_brownian_motion
[ S.46.1.1 ]
Summary
[ S.46.1.2 ]
Data
[ S.46.1.3 ]
Parameters
[ S.46.1.4 ]
Outcomes and figures
[ S.46.1.5 ]
Implementation
[ S.46.1.6 ]
See also
[ S.46.2 ]
s_projection_univ_rating
[ S.46.2.1 ]
Summary
[ S.46.2.2 ]
Data
[ S.46.2.3 ]
Outcomes and figures
[ S.46.2.4 ]
Implementation
[ S.46.2.5 ]
See also
[ S.46.3 ]
Projection of Poisson process
[ S.46.4 ]
Simulate compound Poisson process...
[ S.46.5 ]
Projection of a Compound Poisson ...
[ S.46.6 ]
Projection of Cauchy distribution
[ S.46.7 ]
Projection of the variance gamma ...
[ S.46.8 ]
Simulate the NIG and VG processes
[ S.46.9 ]
Simulation of the variance gamma ...
[ S.46.10 ]
Projection of the Student t distr...
[ S.46.11 ]
Projection of the uniform distrib...
[ S.46.12 ]
Projection of fractional Brownian...
[ S.46.13 ]
Projection of the Heston process
[ S.46.14 ]
Simulation of the Heston process ...
[ S.47 ]
First order autoregression
[ S.47.1 ]
Simulation of a Ornstein-Uhlenbec...
[ S.47.2 ]
MVOU change of coordinates
[ S.47.3 ]
Projection of conditional mean an...
[ S.48 ]
Spectral analysis
[ S.48.1 ]
Autocovariance of AR(1) processes...
[ S.48.2 ]
Spectral representation of AR(1) ...
[ S.48.3 ]
Bandpass filter applied to AR(1) ...
[ S.49 ]
Signals
[ S.49.1 ]
s_cointegration_detection
[ S.49.1.1 ]
Summary
[ S.49.1.2 ]
Data
[ S.49.1.3 ]
Parameters
[ S.49.1.4 ]
Outcomes and figures
[ S.49.1.5 ]
Implementation
[ S.49.1.6 ]
See also
[ S.49.2 ]
s_cointegration_signal
[ S.49.2.1 ]
Summary
[ S.49.2.2 ]
Data
[ S.49.2.3 ]
Parameters
[ S.49.2.4 ]
Outcomes and figures
[ S.49.2.5 ]
Implementation
[ S.49.2.6 ]
See also
[ S.49.3 ]
s_trade_autocorr_signal
[ S.49.3.1 ]
Summary
[ S.49.3.2 ]
Data
[ S.49.3.3 ]
Parameters
[ S.49.3.4 ]
Outcomes and figures
[ S.49.3.5 ]
Implementation
[ S.49.3.6 ]
See also
[ S.49.4 ]
s_order_imbal_signal
[ S.49.4.1 ]
Summary
[ S.49.4.2 ]
Data
[ S.49.4.3 ]
Parameters
[ S.49.4.4 ]
Outcomes and figures
[ S.49.4.5 ]
Implementation
[ S.49.4.6 ]
See also
[ S.49.5 ]
s_price_pred_signal
[ S.49.5.1 ]
Summary
[ S.49.5.2 ]
Data
[ S.49.5.3 ]
Parameters
[ S.49.5.4 ]
Outcomes and figures
[ S.49.5.5 ]
Implementation
[ S.49.5.6 ]
See also
[ S.49.6 ]
s_volume_cluster_signal
[ S.49.6.1 ]
Summary
[ S.49.6.2 ]
Data
[ S.49.6.3 ]
Parameters
[ S.49.6.4 ]
Outcomes and figures
[ S.49.6.5 ]
Implementation
[ S.49.6.6 ]
See also
[ S.49.7 ]
s_momentum_signals
[ S.49.7.1 ]
Summary
[ S.49.7.2 ]
Data
[ S.49.7.3 ]
Parameters
[ S.49.7.4 ]
Outcomes and figures
[ S.49.7.5 ]
Implementation
[ S.49.7.6 ]
See also
[ S.49.8 ]
Signal filtering for systematic s...
[ S.50 ]
Stochastic dominance
[ S.50.1 ]
s_strong_dominance
[ S.50.1.1 ]
Summary
[ S.50.1.2 ]
Parameters
[ S.50.1.3 ]
Outcomes and figures
[ S.50.1.4 ]
Implementation
[ S.50.1.5 ]
See also
[ S.50.2 ]
s_weak_dominance
[ S.50.2.1 ]
Summary
[ S.50.2.2 ]
Parameters
[ S.50.2.3 ]
Outcomes and figures
[ S.50.2.4 ]
Implementation
[ S.50.2.5 ]
See also
[ S.50.3 ]
s_second_order_dominance
[ S.50.3.1 ]
Summary
[ S.50.3.2 ]
Parameters
[ S.50.3.3 ]
Outcomes and figures
[ S.50.3.4 ]
Implementation
[ S.51 ]
Optimization primer
[ S.51.1 ]
s_selection_toy
[ S.51.2 ]
s_stock_selection
[ 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.52 ]
Useful algorithms
[ S.52.1 ]
Cholesky decomposition of the cov...
[ S.52.2 ]
Numerical proof
[ S.52.3 ]
CPCA versus PCA
[ S.52.4 ]
s_factor_analysis_algos
[ S.52.4.1 ]
Summary
[ S.52.4.2 ]
Parameters
[ S.52.4.3 ]
Outcomes and figures
[ S.52.4.4 ]
Implementation
[ S.52.4.5 ]
See also
[ S.52.5 ]
Transpose-square-root of a symmet...
[ S.52.6 ]
s_saddle_point_vs_mcfp_quadn
[ S.52.6.1 ]
Summary
[ S.52.6.2 ]
Parameters
[ S.52.6.3 ]
Outcomes and figures
[ S.52.6.4 ]
Implementation
[ S.52.6.5 ]
See also
[ S.52.7 ]
s_bivariate_wishart
[ S.52.7.1 ]
Summary
[ S.52.7.2 ]
Input
[ S.52.7.3 ]
Outcomes and figures
[ S.52.7.4 ]
Implementation
[ S.52.7.5 ]
See also
[ S.53 ]
Matrix manipulations
[ S.53.1 ]
s_spectral_theorem
[ S.53.1.1 ]
Summary
[ S.53.1.2 ]
Parameters
[ S.53.1.3 ]
Outcomes and figures
[ S.53.1.4 ]
Implementation
[ S.53.1.5 ]
See also
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 ]
cov_2_corr
[ 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.1.3 ]
crisp_fp
[ 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.1.3.8 ]
See also
[ F.1.4 ]
effective_num_scenarios
[ 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 ]
exp_decay_fp
[ 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.1.6 ]
factor_analysis_mlf
[ F.1.6.1 ]
Summary
[ F.1.6.2 ]
Input
[ F.1.6.3 ]
Output
[ F.1.6.4 ]
Implementation
[ F.1.6.5 ]
Example
[ F.1.6.6 ]
Case studies
[ F.1.6.7 ]
References
[ F.1.6.8 ]
See also
[ F.1.7 ]
factor_analysis_paf
[ 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.1.7.7 ]
References
[ F.1.7.8 ]
See also
[ F.1.8 ]
fit_dcc_t
[ 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 ]
See also
[ F.1.9 ]
fit_factor_analysis
[ F.1.9.1 ]
Summary
[ F.1.9.2 ]
Input
[ F.1.9.3 ]
Output
[ F.1.9.4 ]
Implementation
[ F.1.9.5 ]
Tips
[ F.1.9.6 ]
Example
[ F.1.9.7 ]
References
[ F.1.9.8 ]
See also
[ F.1.10 ]
fit_garch_fp
[ F.1.10.1 ]
Summary
[ F.1.10.2 ]
Input
[ F.1.10.3 ]
Output
[ F.1.10.4 ]
Implementation
[ F.1.10.5 ]
Tips
[ F.1.10.6 ]
Example
[ F.1.10.7 ]
Case studies
[ F.1.10.8 ]
See also
[ F.1.11 ]
fit_lfm_lasso
[ F.1.11.1 ]
Example
[ F.1.12 ]
fit_lfm_lasso_path
[ F.1.12.1 ]
Example
[ F.1.13 ]
fit_lfm_mlfp
[ 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 ]
Case studies
[ F.1.13.7 ]
See also
[ F.1.14 ]
fit_lfm_ols
[ 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 ]
References
[ F.1.14.8 ]
See also
[ F.1.15 ]
fit_lfm_pcfp
[ F.1.15.1 ]
Example
[ F.1.16 ]
fit_lfm_ridge
[ F.1.16.1 ]
Example
[ F.1.17 ]
fit_lfm_ridge_path
[ F.1.17.1 ]
Example
[ F.1.18 ]
fit_lfm_roblasso
[ F.1.18.1 ]
Example
[ F.1.19 ]
fit_locdisp_mlfp
[ F.1.19.1 ]
Summary
[ F.1.19.2 ]
Input
[ F.1.19.3 ]
Output
[ F.1.19.4 ]
Implementation
[ F.1.19.5 ]
Example
[ F.1.19.6 ]
Case studies
[ F.1.19.7 ]
See also
[ F.1.20 ]
fit_locdisp_mlfp_difflength
[ F.1.20.1 ]
Summary
[ F.1.20.2 ]
Input
[ F.1.20.3 ]
Output
[ F.1.20.4 ]
Implementation
[ F.1.20.5 ]
Tips
[ F.1.20.6 ]
Example
[ F.1.20.7 ]
Case studies
[ F.1.20.8 ]
References
[ F.1.20.9 ]
See also
[ F.1.21 ]
fit_markov_chain
[ F.1.21.1 ]
Example
[ F.1.22 ]
fit_state_space
[ 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_t_dof
[ F.1.23.1 ]
Example
[ F.1.24 ]
fit_t_fp
[ F.1.24.1 ]
Example
[ F.1.25 ]
fit_var1
[ F.1.25.1 ]
Summary
[ F.1.25.2 ]
Input
[ F.1.25.3 ]
Output
[ F.1.25.4 ]
Implementation
[ F.1.25.5 ]
Example
[ F.1.25.6 ]
Case studies
[ F.1.25.7 ]
See also
[ F.1.26 ]
markov_network
[ F.1.26.1 ]
Example
[ F.1.27 ]
min_corr_toeplitz
[ 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.1.28 ]
spectrum_shrink
[ F.1.28.1 ]
Example
[ F.1.29 ]
var2mvou
[ F.1.29.1 ]
Summary
[ F.1.29.2 ]
Input
[ F.1.29.3 ]
Output
[ F.1.29.4 ]
Implementation
[ F.1.29.5 ]
Tips
[ F.1.29.6 ]
Example
[ F.1.29.7 ]
Case studies
[ F.1.29.8 ]
References
[ F.1.30 ]
Generate FP profiles via multivar...
[ F.1.31 ]
Expectation Maximization algorith...
[ F.1.32 ]
Homogeneous correlation cluster s...
[ F.1.33 ]
Iterated generalized method of mo...
[ F.1.34 ]
Minimum volume ellipsoid enclosin...
[ F.1.35 ]
Farthest Outlier Detection. Routi...
[ F.1.36 ]
High breakdown point with flexibl...
[ F.1.37 ]
Outlier detection with flexible p...
[ F.1.38 ]
SMTCovariance
[ F.1.39 ]
FitFractionalIntegration
[ F.1.40 ]
FitGenParetoMLFP
[ F.1.41 ]
NormalMixtureFit
[ F.1.42 ]
FitSkewtMLFP
[ F.1.43 ]
FitVar_ATMSVI
[ F.1.44 ]
GarchResiduals
[ F.1.45 ]
MMFP
[ F.1.46 ]
kMeansClustering
[ F.1.47 ]
QuantileGenParetoMLFP
[ F.1.48 ]
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 ]
Example
[ F.2.6 ]
opt_trade_meanvar
[ F.2.6.1 ]
Example
[ F.2.7 ]
smooth_quantile_port
[ F.2.7.1 ]
Example
[ F.2.8 ]
spectral_index
[ F.2.8.1 ]
Summary
[ F.2.8.2 ]
Input
[ F.2.8.3 ]
Output
[ F.2.8.4 ]
Implementation
[ F.2.8.5 ]
Example
[ F.2.8.6 ]
Case studies
[ F.2.8.7 ]
References
[ F.2.9 ]
Profit-and-loss statistical featu...
[ F.2.10 ]
EwmaIncludingStartingDays
[ F.2.11 ]
SolveGarleanuPedersen
[ F.2.12 ]
Leverage
[ F.2.13 ]
MomentumStrategy
[ F.3 ]
Pricing
[ F.3.1 ]
bond_value
[ F.3.1.1 ]
Example
[ F.3.2 ]
bootstrap_nelson_siegel
[ F.3.2.1 ]
Example
[ F.3.3 ]
bsm_price
[ F.3.3.1 ]
Example
[ F.3.4 ]
cash_flow_reinv
[ F.3.4.1 ]
Example
[ F.3.5 ]
fit_nelson_siegel_bonds
[ F.3.5.1 ]
Example
[ F.3.6 ]
fit_nelson_siegel_yield
[ F.3.6.1 ]
Example
[ F.3.7 ]
implvol_delta2m
[ F.3.7.1 ]
Example
[ F.3.8 ]
implvol_delta2m_moneyness
[ F.3.8.1 ]
Example
[ F.3.9 ]
inverse_call
[ 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 ]
Example
[ F.3.11 ]
perpetual_american_call
[ F.3.11.1 ]
Example
[ F.3.12 ]
rollvalue_value
[ F.3.12.1 ]
Summary
[ F.3.12.2 ]
Input
[ F.3.12.3 ]
Output
[ F.3.12.4 ]
Implementation
[ F.3.12.5 ]
Example
[ F.3.12.6 ]
Case studies
[ F.3.12.7 ]
References
[ F.3.13 ]
rollvalue_ytm
[ 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.3.14 ]
sdf_mre
[ F.3.14.1 ]
Summary
[ F.3.14.2 ]
Input
[ F.3.14.3 ]
Output
[ F.3.14.4 ]
Implementation
[ F.3.14.5 ]
Tips
[ F.3.14.6 ]
Example
[ F.3.14.7 ]
Case studies
[ F.3.14.8 ]
References
[ F.3.14.9 ]
See also
[ F.3.15 ]
zcb_value
[ F.3.15.1 ]
Example
[ F.3.16 ]
Vasicek yield curve
[ F.3.17 ]
Regularized put option payoff fun...
[ F.3.18 ]
Regularized call option payoff fu...
[ F.3.19 ]
Stochastic discount factor and ke...
[ F.3.20 ]
BachelierCallPrice
[ F.3.21 ]
BondPriceNelSieg
[ F.3.22 ]
blsimpv
[ F.3.23 ]
FilterStochasticVolatility
[ F.3.24 ]
FitCIR_FP
[ F.3.25 ]
FitHeston
[ F.3.26 ]
FitStochasticVolatilityModel
[ F.3.27 ]
FitSigmaSVI
[ F.3.28 ]
FitVasicek
[ F.3.29 ]
HestonChFun
[ F.3.30 ]
CallPriceHestonFFT
[ F.3.31 ]
MapSVIparams
[ F.3.32 ]
SigmaSVI
[ F.3.33 ]
StochTime
[ F.3.34 ]
RollPrices2Prices
[ F.3.35 ]
ZCBondPriceVasicek
[ F.4 ]
Statistics
[ F.4.1 ]
bootstrap_hfp
[ F.4.1.1 ]
Example
[ 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.4.4 ]
cop_marg_sep
[ F.4.4.1 ]
Example
[ 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 ]
ewma_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 ]
Case studies
[ F.4.6.7 ]
See also
[ F.4.7 ]
kalman_filter
[ F.4.7.1 ]
Example
[ F.4.7.2 ]
References
[ F.4.8 ]
kstest_ts
[ F.4.8.1 ]
Example
[ F.4.9 ]
marchenko_pastur
[ F.4.9.1 ]
Example
[ F.4.10 ]
meancov_sp
[ F.4.10.1 ]
Summary
[ F.4.10.2 ]
Input
[ F.4.10.3 ]
Output
[ F.4.10.4 ]
Implementation
[ F.4.10.5 ]
Tips
[ F.4.10.6 ]
Example
[ F.4.10.7 ]
Case studies
[ F.4.10.8 ]
References
[ F.4.11 ]
meancov_inverse_wishart
[ F.4.11.1 ]
Summary
[ F.4.11.2 ]
Input
[ F.4.11.3 ]
Output
[ F.4.11.4 ]
Implementation
[ F.4.11.5 ]
Example
[ F.4.11.6 ]
Case studies
[ F.4.11.7 ]
References
[ F.4.12 ]
meancov_wishart
[ F.4.12.1 ]
Summary
[ F.4.12.2 ]
Input
[ F.4.12.3 ]
Output
[ F.4.12.4 ]
Implementation
[ F.4.12.5 ]
Example
[ F.4.12.6 ]
Case studies
[ F.4.12.7 ]
References
[ F.4.13 ]
moments_logn
[ F.4.14 ]
moments_mvou
[ 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 ]
multi_r2
[ F.4.15.1 ]
Example
[ F.4.16 ]
mvt_pdf
[ F.4.16.1 ]
Example
[ F.4.17 ]
mvt_logpdf
[ F.4.17.1 ]
Example
[ F.4.18 ]
normal_canonical
[ 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 ]
Case studies
[ F.4.18.7 ]
References
[ F.4.19 ]
objective_r2
[ 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 ]
See also
[ F.4.20 ]
plot_all_tests
[ F.4.20.1 ]
Example
[ F.4.21 ]
plot_copula_test
[ F.4.21.1 ]
Example
[ F.4.22 ]
plot_kstest
[ F.4.22.1 ]
Example
[ F.4.23 ]
quantile_sp
[ 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 ]
References
[ F.4.24 ]
pdf_sp
[ 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 ]
References
[ F.4.25 ]
project_trans_matrix
[ F.4.25.1 ]
Example
[ F.4.26 ]
random_split
[ F.4.26.1 ]
Example
[ F.4.27 ]
saddle_point_quadn
[ F.4.27.1 ]
Summary
[ F.4.27.2 ]
Input
[ F.4.27.3 ]
Output
[ F.4.27.4 ]
Implementation
[ F.4.27.5 ]
Tips
[ F.4.27.6 ]
Example
[ F.4.27.7 ]
Case studies
[ F.4.27.8 ]
References
[ F.4.27.9 ]
See also
[ F.4.28 ]
schweizer_wolff
[ F.4.28.1 ]
Example
[ F.4.29 ]
scoring
[ F.4.29.1 ]
Example
[ F.4.30 ]
simulate_garch
[ 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 ]
See also
[ F.4.31 ]
simulate_niw
[ F.4.31.1 ]
Example
[ F.4.32 ]
simulate_bm
[ F.4.32.1 ]
Example
[ F.4.33 ]
simulate_markov_chain
[ F.4.33.1 ]
Example
[ F.4.34 ]
simulate_mvou
[ F.4.34.1 ]
Summary
[ F.4.34.2 ]
Input
[ F.4.34.3 ]
Output
[ F.4.34.4 ]
Implementation
[ F.4.34.5 ]
Tips
[ F.4.34.6 ]
Example
[ F.4.34.7 ]
Case studies
[ F.4.34.8 ]
References
[ F.4.34.9 ]
See also
[ F.4.35 ]
simulate_normal
[ F.4.35.1 ]
Summary
[ F.4.35.2 ]
Input
[ F.4.35.3 ]
Output
[ F.4.35.4 ]
Implementation
[ F.4.35.5 ]
Tips
[ F.4.35.6 ]
Example
[ F.4.35.7 ]
Case studies
[ F.4.35.8 ]
References
[ F.4.35.9 ]
See also
[ F.4.36 ]
simulate_normal_dimred
[ F.4.36.1 ]
Example
[ F.4.37 ]
simulate_quadn
[ F.4.37.1 ]
Summary
[ F.4.37.2 ]
Input
[ F.4.37.3 ]
Output
[ F.4.37.4 ]
Implementation
[ F.4.37.5 ]
Example
[ F.4.37.6 ]
Case studies
[ F.4.37.7 ]
References
[ F.4.37.8 ]
See also
[ F.4.38 ]
simulate_rw_hfp
[ F.4.38.1 ]
Example
[ F.4.39 ]
simulate_t
[ F.4.39.1 ]
Summary
[ F.4.39.2 ]
Input
[ F.4.39.3 ]
Output
[ F.4.39.4 ]
Implementation
[ F.4.39.5 ]
Tips
[ F.4.39.6 ]
Example
[ F.4.39.7 ]
References
[ F.4.39.8 ]
See also
[ F.4.40 ]
simulate_unif_in_ellips
[ F.4.40.1 ]
Summary
[ F.4.40.2 ]
Input
[ F.4.40.3 ]
Output
[ F.4.40.4 ]
Implementation
[ F.4.40.5 ]
Tips
[ F.4.40.6 ]
Example
[ F.4.40.7 ]
Case studies
[ F.4.40.8 ]
References
[ F.4.40.9 ]
See also
[ F.4.41 ]
simulate_var1
[ 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_wishart
[ F.4.42.1 ]
Summary
[ F.4.42.2 ]
Input
[ F.4.42.3 ]
Output
[ F.4.42.4 ]
Implementation
[ F.4.42.5 ]
Example
[ F.4.42.6 ]
Case studies
[ F.4.42.7 ]
References
[ F.4.42.8 ]
See also
[ F.4.43 ]
smooth_quantile
[ F.4.43.1 ]
Example
[ F.4.44 ]
smoothing
[ F.4.44.1 ]
Example
[ F.4.45 ]
twist_prob_mom_match
[ F.4.45.1 ]
Summary
[ F.4.45.2 ]
Input
[ F.4.45.3 ]
Output
[ F.4.45.4 ]
Implementation
[ F.4.45.5 ]
Tips
[ F.4.45.6 ]
Example
[ F.4.45.7 ]
Case studies
[ F.4.45.8 ]
References
[ F.4.45.9 ]
See also
[ F.4.46 ]
twist_scenarios_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 ]
Example
[ F.4.46.6 ]
Case studies
[ F.4.46.7 ]
References
[ F.4.46.8 ]
See also
[ F.4.47 ]
Flexible probabilities and scenar...
[ F.4.48 ]
Pdf of a multivariate normal copu...
[ F.4.49 ]
Pdf of a multivariate t-copula
[ F.4.50 ]
Elliptical Copula Scenarios via d...
[ F.4.51 ]
Normalized empirical histogram of...
[ F.4.52 ]
Metropolis-Hastings algorithm: im...
[ F.4.53 ]
Projection to horizon of central ...
[ F.4.54 ]
Normal innovation function
[ F.4.55 ]
Projection of the pdf via DFT: fu...
[ F.4.56 ]
GHCalibration
[ F.4.57 ]
Stats
[ F.4.58 ]
Raw2Cumul
[ F.4.59 ]
PnlVolatility_ewma
[ F.4.60 ]
ffgn
[ F.4.61 ]
Central2Raw
[ F.4.62 ]
CentralAndStandardizedStatistics
[ F.4.63 ]
Schout2ConTank
[ F.4.64 ]
ProjectionStudentT
[ F.4.65 ]
QuantileMixture
[ F.4.66 ]
ScoringAssessmentFP
[ F.4.67 ]
SharpeRatio
[ F.4.68 ]
PathsCauchy
[ F.4.69 ]
SimulateCompPoisson
[ F.4.70 ]
JumpDiffusionKou
[ F.4.71 ]
JumpDiffusionMerton
[ F.4.72 ]
NIG
[ F.4.73 ]
VGpdf
[ F.4.74 ]
SmoothStep
[ F.4.75 ]
SpinOutlier
[ F.4.76 ]
ShiftedVGMoments
[ F.4.77 ]
ParamChangeVG
[ F.4.78 ]
VG
[ F.5 ]
Views
[ F.5.1 ]
black_litterman
[ F.5.1.1 ]
Example
[ F.5.2 ]
conditional_fp
[ F.5.2.1 ]
Summary
[ F.5.2.2 ]
Input
[ F.5.2.3 ]
Output
[ F.5.2.4 ]
Implementation
[ F.5.2.5 ]
Example
[ F.5.2.6 ]
Case studies
[ F.5.2.7 ]
References
[ F.5.2.8 ]
See also
[ F.5.3 ]
min_rel_entropy_normal
[ F.5.3.1 ]
Example
[ F.5.4 ]
min_rel_entropy_sp
[ F.5.4.1 ]
Summary
[ F.5.4.2 ]
Input
[ F.5.4.3 ]
Output
[ F.5.4.4 ]
Implementation
[ F.5.4.5 ]
Example
[ F.5.5 ]
rel_entropy_normal
[ F.5.5.1 ]
Summary
[ F.5.5.2 ]
Input
[ F.5.5.3 ]
Output
[ F.5.5.4 ]
Implementation
[ F.5.5.5 ]
Tips
[ F.5.5.6 ]
Example
[ F.5.5.7 ]
Case studies
[ F.5.5.8 ]
References
[ F.5.6 ]
Relative entropy function
[ F.5.7 ]
Signal-to-noise constraint functi...
[ F.5.8 ]
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 ]
Example
[ 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 ]
Example
[ 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 ]
forward_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.6.7 ]
gram_schmidt
[ F.6.7.1 ]
Summary
[ F.6.7.2 ]
Input
[ F.6.7.3 ]
Output
[ F.6.7.4 ]
Implementation
[ F.6.7.5 ]
Tips
[ F.6.7.6 ]
Example
[ F.6.7.7 ]
Case studies
[ F.6.7.8 ]
References
[ F.6.8 ]
histogram_sp
[ 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 ]
Case studies
[ F.6.8.7 ]
References
[ F.6.9 ]
histogram2d_sp
[ F.6.9.1 ]
Example
[ F.6.10 ]
mahalanobis_dist
[ F.6.11 ]
naive_selection
[ 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.6.12 ]
pca_cov
[ F.6.12.1 ]
Summary
[ F.6.12.2 ]
Input
[ F.6.12.3 ]
Output
[ F.6.12.4 ]
Implementation
[ F.6.12.5 ]
Example
[ F.6.12.6 ]
Case studies
[ F.6.12.7 ]
See also
[ F.6.13 ]
plot_dynamic_strats
[ F.6.13.1 ]
Example
[ F.6.14 ]
plot_ellipse
[ F.6.14.1 ]
Summary
[ F.6.14.2 ]
Input
[ F.6.14.3 ]
Output
[ F.6.14.4 ]
Implementation
[ F.6.14.5 ]
Example
[ F.6.14.6 ]
Case studies
[ F.6.14.7 ]
References
[ F.6.14.8 ]
See also
[ F.6.15 ]
plot_ellipsoid
[ F.6.15.1 ]
Example
[ F.6.16 ]
quad_prog
[ F.6.16.1 ]
Example
[ F.6.17 ]
sector_select
[ F.6.17.1 ]
Example
[ F.6.18 ]
smart_solve
[ F.6.18.1 ]
Example
[ F.6.19 ]
solve_riccati
[ F.6.19.1 ]
Summary
[ F.6.19.2 ]
Input
[ F.6.19.3 ]
Output
[ F.6.19.4 ]
Implementation
[ F.6.19.5 ]
Tips
[ F.6.19.6 ]
Example
[ F.6.19.7 ]
Case studies
[ F.6.19.8 ]
References
[ F.6.20 ]
test_ellipsoid
[ F.6.20.1 ]
Summary
[ F.6.20.2 ]
Input
[ F.6.20.3 ]
Output
[ F.6.20.4 ]
Implementation
[ F.6.20.5 ]
Tips
[ F.6.20.6 ]
Example
[ F.6.20.7 ]
Case studies
[ F.6.20.8 ]
References
[ F.6.20.9 ]
See also
[ F.6.21 ]
trade_quote_processing
[ F.6.21.1 ]
Example
[ F.6.22 ]
trade_quote_spreading
[ F.6.22.1 ]
Example
[ F.6.23 ]
transpose_square_root
[ F.6.23.1 ]
Summary
[ F.6.23.2 ]
Input
[ F.6.23.3 ]
Output
[ F.6.23.4 ]
Implementation
[ F.6.23.5 ]
Example
[ F.6.23.6 ]
Case studies
[ F.6.23.7 ]
References
[ F.6.23.8 ]
See also
[ F.6.24 ]
Multivariate uncertainty band fun...
[ F.6.25 ]
Log2Lin
[ F.6.26 ]
MatchTime
[ F.6.27 ]
binningHFseries
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