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My bibliography Save this paperThe importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades
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- Fantazzini, Dean & Shangina, Tamara, 2019. "The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 55, pages 5-31.
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Cited by:
- Lycheva, Maria & Mironenkov, Alexey & Kurbatskii, Alexey & Fantazzini, Dean, 2022.
"Forecasting oil prices with penalized regressions, variance risk premia and Google data,"
Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 28-49.
- Fantazzini, Dean & Kurbatskii, Alexey & Mironenkov, Alexey & Lycheva, Maria, 2022. "Forecasting oil prices with penalized regressions, variance risk premia and Google data," MPRA Paper 118239, University Library of Munich, Germany.
- Makushkin, Mikhail & Lapshin, Victor, 2020. "Modelling tail dependencies between Russian and foreign stock markets: Application for market risk valuation," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 57, pages 30-52.
- Vladimir Pyrlik & Pavel Elizarov & Aleksandra Leonova, 2021. "Forecasting Realized Volatility Using Machine Learning and Mixed-Frequency Data (the Case of the Russian Stock Market)," CERGE-EI Working Papers wp713, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
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Keywords
Forecasting; Value-at-Risk; Realized Volatility; Google Trends; Implied Volatility; GARCH; ARFIMA; HAR; Realized-GARCH;All these keywords.
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
NEP fields
This paper has been announced in the following NEP Reports:- NEP-CIS-2019-09-30 (Confederation of Independent States)
- NEP-FOR-2019-09-30 (Forecasting)
- NEP-ORE-2019-09-30 (Operations Research)
- NEP-RMG-2019-09-30 (Risk Management)
Statistics
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