Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility
Filip Zikes,Jozef Baruník +1 more
TLDR
In this article, the conditional quantiles of future returns and volatility of financial assets vary with various measures of ex-post variation in asset prices as well as option-implied volatility.Abstract:
This paper investigates how the conditional quantiles of future returns and volatility of financial assets vary with various measures of ex-post variation in asset prices as well as option-implied volatility. We work in the flexible quantile regression framework and rely on recently developed model-free measures of integrated variance, upside and downside semivariance, and jump variation. Our results for the S&P 500 and WTI Crude Oil futures contracts show that simple linear quantile regressions for returns and heterogenous quantile autoregressions for realized volatility perform very well in capturing the dynamics of the respective conditional distributions, both in absolute terms as well as relative to a couple of well-established benchmark models. The models can therefore serve as useful risk management tools for investors trading the futures contracts themselves or various derivative contracts written on realized volatility.read more
Citations
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Investigating the risk-return trade-off for crude oil futures using high-frequency data
TL;DR: In this paper, the authors comprehensively examined the existence and significance of a contemporaneous/intertemporal risk-return trade-off for crude oil futures using high-frequency transaction data.
Journal ArticleDOI
Realized volatility models and alternative Value-at-Risk prediction strategies
TL;DR: In this paper, the authors assess the value-at-risk (VaR) forecasting performance of recently proposed realized volatility models combined with alternative parametric and semi-parametric quantile estimation methods, and find that statistical accuracy and regulatory compliance is essentially improved when they use quantile methods which account for the fat tails and the asymmetry of the innovations distribution.
Journal ArticleDOI
Optimally Harnessing Inter-Day and Intra-Day Information for Daily Value-at-Risk Prediction
TL;DR: In this article, quantile regression theory is used to obtain a combination of individual potentially-biased VaR forecasts that is optimal because it meets by construction ex post the correct out-of-sample conditional coverage criterion.
Journal ArticleDOI
Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction
Ana-Maria Fuertes,Jose Olmo +1 more
TL;DR: In this article, quantile regression theory is used to obtain a combination of individual potentially-biased VaR forecasts that is optimal because, by construction, it meets the correct out-of-sample conditional coverage criterion ex post.
Journal ArticleDOI
Forecasting the return distribution using high-frequency volatility measures
Jian Hua,Sebastiano Manzan +1 more
TL;DR: In this article, the authors adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a benchmark GARCH model.
References
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Posted Content
The Volatility of Realized Volatility
Fulvio Corsi,Fulvio Corsi,Uta Kretschmer,Stefan Mittnik,Stefan Mittnik,Stefan Mittnik,Christian Pigorsch +6 more
TL;DR: In this paper, the residuals of the commonly used time-series models for realized volatility exhibit non-Gaussianity and volatility clustering, and the authors propose extensions to explicitly account for these properties and assess their relevance when modeling and forecasting realized volatility.
Journal ArticleDOI
Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting
TL;DR: In this paper, the authors consider the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility and introduce the concept of threshold bipower variation, which is based on the joint use of bipower and threshold estimation.
Journal ArticleDOI
Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics
Peter Christoffersen,Peter Christoffersen,Peter Christoffersen,Francis X. Diebold,Francis X. Diebold +4 more
TL;DR: In this article, the authors consider three sets of phenomena that feature prominently and separately in the financial economics literature: conditional mean dependence (or lack thereof) in asset returns, dependence (and hence forecastability) of asset return signs, and dependence in asset return volatilities, and explore the relationships in detail.
Journal ArticleDOI
Jump-Robust Volatility Estimation Using Nearest Neighbor Truncation
TL;DR: In this article, the authors proposed two jump-robust estimators of integrated variance based on high-frequency return observations, MinRV and MedRV, which provide an attractive alternative to the prevailing bipower and multipower variation measures.
Posted Content
Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian
Torben G. Andersen,Torben G. Andersen,Torben G. Andersen,Tim Bollerslev,Tim Bollerslev,Francis X. Diebold,Francis X. Diebold,Paul Labys +7 more
TL;DR: The authors showed that returns standardized by the realized volatilities of Andersen, Bollerslev, Diebold and Labys (2000a) are very nearly Gaussian, and trace the differing effects of different standardizations to differences in information sets.
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