The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets

By T. Busch, Bent Jesper Christensen, Morten Ørregaard Nielsen. 2007

340 citations recorded by the source corpus.

We study the forecasting of future realized volatility in the foreign exchange, stock, and bond markets from variables in our information set, including implied volatility backed out from option prices. Realized volatility is separated into its continuous and jump components, and the heterogeneous autoregressive (HAR) model is applied with implied volatility as an additional forecasting variable. A vector HAR (VecHAR) model for the resulting simultaneous system is introduced, controlling for possible endogeneity issues. We find that implied volatility contains incremental information about future volatility in all three markets, relative to past continuous and jump components, and it is an unbiased forecast in the foreign exchange and stock markets. Out-of-sample forecasting experiments confirm that implied volatility is important in forecasting future realized volatility components in all three markets. Perhaps surprisingly, the jump component is, to some extent, predictable, and options appear calibrated to incorporate information about future jumps in all three markets.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/e78d13151595f59f05bcf39e7813004dd89d8131

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Citations in the corpus

Forecasting oil price realized volatility using information channels from other asset classes (2017, background): “Andersen et al. (2007), Corsi (2009), Busch et al. (2011) and Fernandes et al. (2014) also argue that HAR models are more successful in forecasting asset price volatility as they are parsimonious and they capture the longmemory that is observed in asset price volatility. time-series, the authors…”
Forecasting global stock market implied volatility indices (2018, background): “…autoregressive models (HAR) are more successful in forecasting volatility due to the fact that they are parsimonious and they can capture the longmemory that is observed in volatility (see, inter alia, Andersen et al., 2007; Corsi, 2009; Busch et al., 2011; Fernandes et al., 2014, Sevi, 2014).”
Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility (2013, background): “ion models. Besides modeling conditional quantiles of future returns, we propose simple models for the quantiles of future realized volatility. We follow Andersen, Bollerslev & Diebold (2007) and Bush et al. (2011) and consider a heterogeneous quantile autoregressive model (HQAR) with jumps and ”
The VIX, the Variance Premium and Stock Market Volatility (2013, neutral): “Recent articles using the VIX in similar forecasting exercises include Busch et al. (2011) who examine a number of variance forecasting models embedding option-implied volatility for bond, currency and stockmarkets, and Andersen and Bondarenko (2007) who mostly focus on measurement issues with the…”
Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets (2016, methodology): “However, to simplify our notation, we write it as RVt. 7Similar to Busch et al. (2011), among others, we focus on annualized variance.”
Systemic Co-Jumps (2017, methodology): “The model is a natural multivariate generalization of the univariate models adopted in Andersen, Bollerslev and Diebold (2007), Corsi et al. (2010) and Busch et al. (2011), which are all based on the Heterogeneous Auto-Regressive (HAR) model of Corsi (2009).”
Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility (2015, result): “This may explain earlier results in this literature (see Andersen et al., 2007, and Busch et al., 2011, for example), which found that jumps are of limited use for forecasting future volatility; only by including the jump size and sign are the gains from jumps realized.”
Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection (2020, background): “(9) The implied volatility indices, CBOE VIX and VXO, are used to measure market expectation of volatility conveyed by stock index option prices, and they are important in forecasting future financial volatility (Busch et al., 2011).”
Forecasting with Option-Implied Information (2012, neutral): “Busch et al. (2011) find that BSIV is an effi cient and unbiased predictor in equity index markets.”
Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting (2010, result): “3), ABD estimate βj to be not significant and negative, and a very similar conclusion has been reached by Forsberg and Ghysels (2007), Giot and Laurent (2007) and Busch et al. (2009); see also the analysis of Ghysels et al.”
Option Valuation with Observable Volatility and Jump Dynamics (2015, background): “6 Conditional Second Moments From the model above, it is relatively straightforward to derive the following one-day-ahead conditional second moments V art [Rt+1] = hz;t + 2 + 2 hy;t (12) V art [RBVt+1] = 2 2 1 + 2 (2)hz;t V art [RJVt+1] = 4 + 3 4 + 6 2 2 hy;t Covt (Rt+1; RBVt+1) = 2 hz;t Covt (Rt+1;”
Bond Variance Risk Premia (2012, positive): “Busch, Christensen, and Nielsen (2011) for example use this measure to study the forecasting power of implied volatility for realized volatility of Treasury bond futures.”