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Using Machine Learning to Predict Realized Variance [PDF]
, 2019 In this paper we formulate a regression problem to predict realized volatility by using option price data and enhance VIX-styled volatility indices' predictability and liquidity. We test algorithms including regularized regression and machine learning methods such as Feedforward Neural Networks (FNN) on S&P 500 Index and its option data.
Peter Carr, Liuren Wu, Zhibai Zhang
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Uncertainty index and stock volatility prediction: evidence from international markets
Financial Innovation, 2022 This study investigates the predictability of a fixed uncertainty index (UI) for realized variances (volatility) in the international stock markets from a high-frequency perspective.
Xue Gong +3 more
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Development of high-frequency volatility estimators in pricing and trading stock options
π-Economy, 2022 Asset return volatility plays a key role in derivative pricing and hedging, risk management and portfolio allocation decisions. This study examined the economic benefit of high-frequency volatility estimators (measures realized) in option pricing and ...
Gayomey John, Zaytsev Andrey
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Modelling temporal dependence of realized variances with vines [PDF]
Econometrics and Statistics, 2019 Abstract Models for realized volatility that take the specific form of temporal dependence into account are proposed. Current popular methods use the idea of mixed frequencies for forecasting realized volatility, but neglect the potential non-linear and non-monotonic temporal dependence.
Claudia Czado +2 more
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Comparing GARCH Models by Introducing Fuzzy Asymmetric Realized GARCH [PDF]
مدلسازی اقتصادسنجی, 2018 Estimation of conditional variance has lots of application reflecting economic, especially financial economics, social economics and political economics’ risk and volatility research.
Esmaiel Abounoori, Mohammad Amin Zabol
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Estimating quadratic variation using realized variance [PDF]
Journal of Applied Econometrics, 2002 AbstractThis paper looks at some recent work on estimating quadratic variation using realized variance (RV)—that is, sums ofMsquared returns. This econometrics has been motivated by the advent of the common availability of high‐frequency financial return data. When the underlying process is a semimartingale we recall the fundamental result that RV is a
Barndorff-Nielsen, Ole Eiler +1 more
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Energies, 2021 We examine the predictive value of gold-to-silver and gold-to-platinum price ratios, as proxies for global risks affecting the realized variance (RV) of oil-price movements, using monthly data over the longest available periods of 1915:01–2021:03 and ...
Rangan Gupta +2 more
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Lasso-Based Forecast Combinations for Forecasting Realized Variances [PDF]
SSRN Electronic Journal, 2016 Volatility forecasts are key inputs in financial analysis. While lasso based forecasts have shown to perform well in many applications, their use to obtain volatility forecasts has not yet received much attention in the literature. Lasso estimators produce parsimonious forecast models.
Wilms, Ines +2 more
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Options on realized variance and convex orders [PDF]
, 2010 Realized variance option and options on quadratic variation normalized to unit expectation are analysed for the property of monotonicity in maturity for call options at a fixed strike.
Carr, P. +3 more
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Properties of Bias-Corrected Realized Variance Under Alternative Sampling Schemes [PDF]
Journal of Financial Econometrics, 2005 In this article I study the statistical properties of a bias-corrected realized variance measure when high-frequency asset prices are contaminated with market microstructure noise. The analysis is based on a pure jump process for asset prices and explicitly distinguishes among different sampling schemes, including calendar time, business time, and ...
Roel C. A. Oomen
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