Abstract
The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken from the Oxford Man Realised Library. Both models demonstrated comparable performance and were correlated to a similar extent with RV estimates when measured by ordinary least squares (OLS). However, a crude variant of Corsi’s (2009) Heterogeneous Autoregressive (HAR) model, applied to squared demeaned daily returns on FTSE, appeared to predict the daily RV of FTSE better than either of the two models. Quantile regressions suggest that all three methods capture tail behaviour similarly and adequately. This leads to the question of whether we need either of the two standard volatility models if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at least in the context of the sample.
Highlights
The paper explores the performance of Stochastic Volatility (SV) and Generalised AutoregressiveConditional Heteroscedasticity (GARCH) (1,1) models as estimators of the volatility of the FTSE Index.The volatilities estimated by these models are compared with realised volatility estimates for FTSE, obtained from the Oxford Man Realised Library and sampled at 5 min intervals, as described inHeber et al (2009)
We contrasted the estimates of volatility from the SV model with those from a Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) model, and assessed which better explained the behaviour of the realized volatility (RV) of FTSE sampled at 5 min intervals
The paper featured an examination of the effectiveness of SV and GARCH(1,1) models as explainers of modelfree estimates of FTSE volatility using RV samples at 5 min intervals as a benchmark, as provided by Oxford Man Institute’s Realised Library
Summary
The paper explores the performance of Stochastic Volatility (SV) and Generalised Autoregressive. We used the stochvol R package, which uses Markov chain Monte Carlo (MCMC) samplers, to conduct inference by obtaining draws from the posterior distribution of parameters and latent variables, which could be used for predicting future volatilities This is done within the context of a fully Bayesian implementation of heteroscedasticity modelling within the framework of stochastic. The advantage of the stochvol R package is that it incorporates an efficient MCMC estimation scheme for SV models, as discussed by Kastner and Frühwirth-Schnatter (2014) This facilitated analysis in this paper, which features a direct comparison of the volatility predictions of a SV model, a GARCH (1,1) model, and a simple application of a historical-volatility-based estimation method, as applied to the FTSE index.
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