Dealing with Stochastic Volatility in Time Series Using the<i>R</i>Package<b>stochvol</b>

Kastner, Gregor

doi:10.18637/jss.v069.i05

Cited by 126 publications

(127 citation statements)

References 23 publications

(25 reference statements)

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“…For this, we employ the BTF‐DHS model (1), with a Gaussian AR(1) model on

log (σ_{t}^{2})

. For the additional sampling of the SV parameters, we use the algorithm of Kastner and Frühwirth‐Schnatter () implemented in the R package stochvol (Kastner, ).…”

Section: Bayesian Trend Filtering With Dynamic Shrinkage Processesmentioning

confidence: 99%

Dynamic Shrinkage Processes

Kowal

Matteson

Ruppert

2019

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Summary We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence between the local scale parameters. The resulting processes inherit the desirable shrinkage behaviour of popular global–local priors, such as the horseshoe prior, but provide additional localized adaptivity, which is important for modelling time series data or regression functions with local features. We construct a computationally efficient Gibbs sampling algorithm based on a Pólya–gamma scale mixture representation of the process proposed. Using dynamic shrinkage processes, we develop a Bayesian trend filtering model that produces more accurate estimates and tighter posterior credible intervals than do competing methods, and we apply the model for irregular curve fitting of minute‐by‐minute Twitter central processor unit usage data. In addition, we develop an adaptive time varying parameter regression model to assess the efficacy of the Fama–French five‐factor asset pricing model with momentum added as a sixth factor. Our dynamic analysis of manufacturing and healthcare industry data shows that, with the exception of the market risk, no other risk factors are significant except for brief periods.

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“…For this, we employ the BTF‐DHS model (1), with a Gaussian AR(1) model on

log (σ_{t}^{2})

. For the additional sampling of the SV parameters, we use the algorithm of Kastner and Frühwirth‐Schnatter () implemented in the R package stochvol (Kastner, ).…”

Section: Bayesian Trend Filtering With Dynamic Shrinkage Processesmentioning

confidence: 99%

Dynamic Shrinkage Processes

Kowal

Matteson

Ruppert

2019

Journal of the Royal Statistical Society Series B: Statistical Methodology

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“…The procedure is repeated over a fine grid of values that is determined by the prior and an approximation to the inverse cumulative distribution function of the posterior is constructed. Finally, this approximation is used to perform inverse transform sampling. The coefficients of each of the the log‐volatility equations and the corresponding histories of the log‐volatilities are sampled as in Kastner and Frühwirth‐Schnatter () through the R package stochvol (Kastner, ). Under homoskedasticity,

σ_{i}^{- 2}

is simulated from

σ_{i}^{- 2} false| • \sim scriptG ((), c_{0} + T false/ 2, c_{1} + \sum_{t = 1}^{T} {false(y_{i t} - z_{i t}^{'} {\overset{β}{true}}_{i t} false)}^{2} false/ 2)

.…”

Section: Econometric Frameworkmentioning

confidence: 99%

Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models

Huber

Kastner

Feldkircher

2019

J of Applied Econometrics

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Summary We propose a straightforward algorithm to estimate large Bayesian time‐varying parameter vector autoregressions with mixture innovation components for each coefficient in the system. The computational burden becomes manageable by approximating the mixture indicators driving the time‐variation in the coefficients with a latent threshold process that depends on the absolute size of the shocks. Two applications illustrate the merits of our approach. First, we forecast the US term structure of interest rates and demonstrate forecast gains relative to benchmark models. Second, we apply our approach to US macroeconomic data and find significant evidence for time‐varying effects of a monetary policy tightening.

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“…For a discussion concerning the choice of the hyperparameters, see, for example, Kim, Shephard and Chib (). Finally, for

σ_{η} \in R^{+}

, following Kastner () and Frühwirth‐Schnatter and Wagner (), we choose a centered normal prior with hyperparameter equal to 1, that is,

\pm \sqrt{σ_{η}^{2}} \sim scriptN false(0, 1 false)

. The choice of hyperparameter value has a minor influence in empirical applications if it is not too small; see, for example, Kastner and Frühwirth‐Schnatter () for further discussion regarding prior specification.…”

Section: Volatility Model Specification and Estimationmentioning

confidence: 99%

“…For a discussion concerning the choice of the hyperparameters, see, for example, Kim, Shephard and Chib (1998). Finally, for ∈ R + , following Kastner (2016) and Frühwirth-Schnatter and Wagner (2010), we choose a centered normal prior with hyperparameter equal to 1, that is, ± √ 2 ∼  (0, 1).…”

Section: Sv Modelmentioning

confidence: 99%

Time series forecasting using functional partial least square regression with stochastic volatility, GARCH, and exponential smoothing

Kim

Jung

2017

Journal of Forecasting

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We propose a method for improving the predictive ability of standard forecasting models used in financial economics. Our approach is based on the functional partial least squares (FPLS) model, which is capable of avoiding multicollinearity in regression by efficiently extracting information from the high‐dimensional market data. By using its well‐known ability, we can incorporate auxiliary variables that improve the predictive accuracy. We provide an empirical application of our proposed methodology in terms of its ability to predict the conditional average log return and the volatility of crude oil prices via exponential smoothing, Bayesian stochastic volatility, and GARCH (generalized autoregressive conditional heteroskedasticity) models, respectively. In particular, what we call functional data analysis (FDA) traces in this article are obtained via the FPLS regression from both the crude oil returns and auxiliary variables of the exchange rates of major currencies. For forecast performance evaluation, we compare out‐of‐sample forecasting accuracy of the standard models with FDA traces to the accuracy of the same forecasting models with the observed crude oil returns, principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO) models. We find evidence that the standard models with FDA traces significantly outperform our competing models. Finally, they are also compared with the test for superior predictive ability and the reality check for data snooping. Our empirical results show that our new methodology significantly improves predictive ability of standard models in forecasting the latent average log return and the volatility of financial time series.

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Dealing with Stochastic Volatility in Time Series Using theRPackagestochvol

Cited by 126 publications

References 23 publications

Dynamic Shrinkage Processes

Dynamic Shrinkage Processes

Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models

Time series forecasting using functional partial least square regression with stochastic volatility, GARCH, and exponential smoothing

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