2024-03-29T07:01:38Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/1268062018-11-07T14:27:56Zcom_10234_8643com_10234_9col_10234_8644
Repositori UJI
author
Lux, Thomas
author
Morales Arias, Leonardo
author
Sattarhoff, Cristina
2015-07-08T09:19:15Z
2015-07-08T09:19:15Z
2014-09
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.
http://hdl.handle.net/10234/126806
http://dx.doi.org/10.1002/for.2307
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
eng
Copyright © 2014 John Wiley & Sons, Ltd.
realized volatility
multiplicative volatility models
long memory
international volatility forecasting
Forecasting Daily variations of Stock Index Returns with a Multifractal Model of Realized Volatility
info:eu-repo/semantics/article
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