Why Do Big Data and Machine Learning Entail the Fractional Dynamics? [PDF]
Entropy (Basel), 2021 Niu H, Chen Y, West BJ.
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VOLATILITY AND VAR FORECASTING FOR THE IBEX-35 STOCK-RETURN INDEX USING FIGARCH-TYPE PROCESSES AND DIFFERENT EVALUATION CRITERIA [PDF]
In this paper I analyze the relative performance of Gaussian and Student-t GARCH and FIGARCH type models for volatility and Value-at-Risk forecasting of daily stock-returns using data from the Spanish equity index IBEX-35.
Trino-Manuel Ñíguez
core
High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions. [PDF]
PLoS OneKuang W.
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Comparing COVID-19 with the GFC: A shockwave analysis of currency markets. [PDF]
Res Int Bus Finance, 2021 Gunay S.
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Modelling stock market data in China: Crisis and Coronavirus. [PDF]
Financ Res Lett, 2021 Cristofaro L +3 more
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Long Memory in LME Volatility through the ARFIMA and FIGARCH Model
Korean Journal of Financial Engineering, 2016 null Jaehwan Park, null 김현숙
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Value at Risk long memory volatility models with heavy-tailed distributions for cryptocurrencies. [PDF]
Front Appl Math StatSubramoney SD, Chinhamu K, Chifurira R.
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Stock Market Volatility and Return Analysis: A Systematic Literature Review. [PDF]
Entropy (Basel), 2020 Bhowmik R, Wang S.
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Modeling Long Memory and Structural Breaks in Conditional Variances: an Adaptive FIGARCH Approach [PDF]
This paper introduces a new long memory volatility process, denoted by Adaptive FIGARCH, or A-FIGARCH, which is designed to account for both long memory and structural change in the conditional variance process.
Claudio Morana, Richard T. Baillie
core
Market-crash forecasting based on the dynamics of the alpha-stable distribution. [PDF]
Physica A, 2020 Molina-Muñoz J +2 more
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