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Implied Volatility Forecasting Realized Volatility
2021This chapter conducts an empirical analysis of IV to forecast the RV through testing hypothesis 1–9. The analysis includes three steps. First, estimate the IV for ATM price of currency options with 1-, 2-, and 3-month maturity during opening, midday, and closing period.
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GARCH, Outliers, and Forecasting Volatility
2011The issue of detecting and handling outliers in GARCH processes has received considerable attention recently. In this chapter, we put forwardan iterative outlier detection procedure, which is appropriate given that in practice both the number of outliers as well as their timing is unknown. Our procedure aims to test for the presence of a single outlier
Franses, Philip Hans, van Dijk, Dick
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Volatility forecasts evaluation and comparison
WIREs Computational Statistics, 2011AbstractThis article surveys the most important developments in volatility forecast comparison and model selection. We review a number of evaluation methods and testing procedures for predictive accuracy based on statistical loss functions. We also review recent contributions on the admissible form of loss functions ensuring consistency of the ordering
VIOLANTE, FRANCESCO +1 more
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Volatility Forecasting and Microstructure Noise
SSRN Electronic Journal, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ghysels, Eric, Sinko, Arthur
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Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect
Resources policy, 2019This paper explores the role of investor sentiment and leverage effect on the predictability of crude oil futures market volatility over daily, weekly and monthly horizons.
Cai Yang, Xu Gong, Hongwei Zhang
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Uncertainty and the volatility forecasting power of option‐implied volatility
, 2020This study investigates the impact of uncertainty on the volatility forecasting power of option‐implied volatility. Option‐implied volatility is a powerful predictor of future volatility, particularly during periods of high uncertainty.
Byounghyun Jeon, Sung Won Seo, J. Kim
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Sentiment-aware volatility forecasting
Knowledge-Based Systems, 2019Recent advances in the integration of deep recurrent neural networks and statistical inferences have paved new avenues for joint modeling of moments of random variables, which is highly useful for signal processing, time series analysis, and financial ...
Frank Xing, E. Cambria, Yue Zhang
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Jumps and oil futures volatility forecasting: a new insight
, 2020This study designs the Markov-switching (MS) mixed data sampling (MIDAS) models and then explores the effects of intraday and interday jumps on oil futures price realized volatility.
Feng Ma, Chao Liang, Qing Zeng, Haibo Li
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Practical Issues in Forecasting Volatility
CFA Digest, 2005A comparison is presented of 93 studies that conducted tests of volatility-forecasting methods on a wide range of financial asset returns. The survey found that option-implied volatility provides more accurate forecasts than time-series models.
Poon, Ser Huang, Granger, Clive
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Energy Economics, 2018
This paper aims to investigate whether investor fear gauge (IFG) contains incremental information content for forecasting the volatility of crude oil futures. For this purpose, we use oil volatility index (OVX) to measure the IFG.
Xu Gong, Boqiang Lin
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This paper aims to investigate whether investor fear gauge (IFG) contains incremental information content for forecasting the volatility of crude oil futures. For this purpose, we use oil volatility index (OVX) to measure the IFG.
Xu Gong, Boqiang Lin
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