API Reference

This page lists contains a list of the essential end-user API functions and classes.

Volatility Modeling

High-level

arch_model(y[, x, mean, lags, vol, p, o, q, ...])

Initialization of common ARCH model specifications

Mean Specification

ConstantMean([y, hold_back, volatility, ...])

Constant mean model estimation and simulation.

ZeroMean([y, hold_back, volatility, ...])

Model with zero conditional mean estimation and simulation

HARX([y, x, lags, constant, use_rotated, ...])

Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation

ARX([y, x, lags, constant, hold_back, ...])

Autoregressive model with optional exogenous regressors estimation and simulation

LS([y, x, constant, hold_back, volatility, ...])

Least squares model estimation and simulation

Volatility Process Specification

GARCH([p, o, q, power])

GARCH and related model estimation

EGARCH([p, o, q])

EGARCH model estimation

HARCH([lags])

Heterogeneous ARCH process

FIGARCH([p, q, power, truncation])

FIGARCH model

MIDASHyperbolic([m, asym])

MIDAS Hyperbolic ARCH process

EWMAVariance([lam])

Exponentially Weighted Moving-Average (RiskMetrics) Variance process

RiskMetrics2006([tau0, tau1, kmax, rho])

RiskMetrics 2006 Variance process

ConstantVariance()

Constant volatility process

FixedVariance(variance[, unit_scale])

Fixed volatility process

Shock Distributions

Normal([random_state, seed])

Standard normal distribution for use with ARCH models

StudentsT([random_state, seed])

Standardized Student's distribution for use with ARCH models

SkewStudent([random_state, seed])

Standardized Skewed Student's distribution for use with ARCH models

GeneralizedError([random_state, seed])

Generalized Error distribution for use with ARCH models

Unit Root Testing

ADF(y[, lags, trend, max_lags, method, ...])

Augmented Dickey-Fuller unit root test

DFGLS(y[, lags, trend, max_lags, method, ...])

Elliott, Rothenberg and Stock's ([ers]) GLS detrended Dickey-Fuller

PhillipsPerron(y[, lags, trend, test_type])

Phillips-Perron unit root test

ZivotAndrews(y[, lags, trend, trim, ...])

Zivot-Andrews structural-break unit-root test

VarianceRatio(y[, lags, trend, debiased, ...])

Variance Ratio test of a random walk.

KPSS(y[, lags, trend])

Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test

Cointegration Testing

engle_granger(y, x[, trend, lags, max_lags, ...])

Test for cointegration within a set of time series.

phillips_ouliaris(y, x[, trend, test_type, ...])

Test for cointegration within a set of time series.

Cointegrating Relationship Estimation

CanonicalCointegratingReg(y, x[, trend, x_trend])

Canonical Cointegrating Regression cointegrating vector estimation.

DynamicOLS(y, x[, trend, lags, leads, ...])

Dynamic OLS (DOLS) cointegrating vector estimation

FullyModifiedOLS(y, x[, trend, x_trend])

Fully Modified OLS cointegrating vector estimation.

Bootstraps

IIDBootstrap(*args[, random_state, seed])

Bootstrap using uniform resampling

IndependentSamplesBootstrap(*args[, ...])

Bootstrap where each input is independently resampled

StationaryBootstrap(block_size, *args[, ...])

Politis and Romano (1994) bootstrap with expon distributed block sizes

CircularBlockBootstrap(block_size, *args[, ...])

Bootstrap using blocks of the same length with end-to-start wrap around

MovingBlockBootstrap(block_size, *args[, ...])

Bootstrap using blocks of the same length without wrap around

Block-length Selection

optimal_block_length(x)

Estimate optimal window length for time-series bootstraps

Testing with Multiple-Comparison

SPA(benchmark, models[, block_size, reps, ...])

Test of Superior Predictive Ability (SPA) of White and Hansen.

MCS(losses, size[, reps, block_size, ...])

Model Confidence Set (MCS) of Hansen, Lunde and Nason.

StepM(benchmark, models[, size, block_size, ...])

StepM multiple comparison procedure of Romano and Wolf.

Long-run Covariance (HAC) Estimation

Bartlett(x[, bandwidth, df_adjust, center, ...])

Bartlett's (Newey-West) kernel covariance estimation.

Parzen(x[, bandwidth, df_adjust, center, ...])

Parzen's kernel covariance estimation.

ParzenCauchy(x[, bandwidth, df_adjust, ...])

Parzen's Cauchy kernel covariance estimation.

ParzenGeometric(x[, bandwidth, df_adjust, ...])

Parzen's Geometric kernel covariance estimation.

ParzenRiesz(x[, bandwidth, df_adjust, ...])

Parzen-Reisz kernel covariance estimation.

QuadraticSpectral(x[, bandwidth, df_adjust, ...])

Quadratic-Spectral (Andrews') kernel covariance estimation.

TukeyHamming(x[, bandwidth, df_adjust, ...])

Tukey-Hamming kernel covariance estimation.

TukeyHanning(x[, bandwidth, df_adjust, ...])

Tukey-Hanning kernel covariance estimation.

TukeyParzen(x[, bandwidth, df_adjust, ...])

Tukey-Parzen kernel covariance estimation.