Abstract
The R package stochvol provides a fully Bayesian implementation of heteroskedasticity modeling within the framework of stochastic volatility. It utilizes Markov chain Monte Carlo (MCMC) samplers to conduct inference by obtaining draws from the posterior distribution of parameters and latent variables which can then be used for predicting future volatilities. The package can straightforwardly be employed as a stand-alone tool; moreover, it allows for easy incorporation into other MCMC samplers. The main focus of this paper is to show the functionality of stochvol. In addition, it provides a brief mathematical description of the model, an overview of the sampling schemes used, and several illustrative examples using exchange rate data.
Highlights
Returns – in particular financial returns – are commonly analyzed by estimating and predicting potentially time-varying volatilities
Even though several papers (e.g., Jacquier, Polson, and Rossi 1994; Ghysels, Harvey, and Renault 1996; Kim, Shephard, and Chib 1998) provide early evidence in favor of using stochastic volatility (SV), these models have found comparably little use in applied work. This obvious discrepancy is discussed in Bos (2012) who points out two reasons: the variety of estimation methods for SV models – whereas the many variants of the GARCH model have basically a single estimation method – and the lack of standard software packages implementing these methods
We demonstrate how the stochvol package can be used to incorporate stochastic volatility into any given Markov chain Monte Carlo (MCMC) sampler
Summary
Returns – in particular financial returns – are commonly analyzed by estimating and predicting potentially time-varying volatilities This focus has a long history, dating back at least to Markowitz (1952) who investigates portfolio construction with optimal expected returnvariance trade-off. Even though several papers (e.g., Jacquier, Polson, and Rossi 1994; Ghysels, Harvey, and Renault 1996; Kim, Shephard, and Chib 1998) provide early evidence in favor of using SV, these models have found comparably little use in applied work This obvious discrepancy is discussed in Bos (2012) who points out two reasons: the variety (and potential incompatibility) of estimation methods for SV models – whereas the many variants of the GARCH model have basically a single estimation method – and the lack of standard software packages implementing these methods
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