Forecasting Daily variations of Stock Index Returns with a Multifractal Model of Realized Volatility
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Otros documentos de la autoría: Lux, Thomas; Morales Arias, Leonardo; Sattarhoff, Cristina
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http://dx.doi.org/10.1002/for.2307 |
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Título
Forecasting Daily variations of Stock Index Returns with a Multifractal Model of Realized VolatilityFecha de publicación
2014-09Editor
John Wiley & SonsCita bibliográfica
LUX, Thomas; MORALES‐ARIAS, Leonardo; SATTARHOFF, Cristina. Forecasting daily variations of stock index returns with a multifractal model of realized volatility. Journal of Forecasting, 2014, 33.7: 532-541.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://onlinelibrary.wiley.com/doi/10.1002/for.2307/fullPalabras clave / Materias
Resumen
Multifractal models have recently been introduced as a new type of data-generating process for asset returns and other financial data. Here we propose an adaptation of this model for realized volatility. We estimate ... [+]
Multifractal models have recently been introduced as a new type of data-generating process for asset returns and other financial data. Here we propose an adaptation of this model for realized volatility. We estimate this new model via generalized method of moments and perform forecasting by means of best linear forecasts derived via the Levinson–Durbin algorithm. Its out-of-sample performance is compared against other popular time series specifications. Using an intra-day dataset for five major international stock market indices, we find that the the multifractal model for realized volatility improves upon forecasts of its earlier counterparts based on daily returns and of many other volatility models. While the more traditional RV-ARFIMA model comes out as the most successful model (in terms of the number of cases in which it has the best forecasts for all combinations of forecast horizons and evaluation criteria), the new model performs often significantly better during the turbulent times of the recent financial crisis [-]
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Journal of Forecasting Volume 33, Issue 7, November 2014Derechos de acceso
Copyright © 2014 John Wiley & Sons, Ltd.
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