Premium
Forecasting Daily Variations of Stock Index Returns with a Multifractal Model of Realized Volatility
Author(s) -
Lux Thomas,
MoralesArias Leonardo,
Sattarhoff Cristina
Publication year - 2014
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2307
Subject(s) - multifractal system , econometrics , volatility (finance) , stock market index , autoregressive fractionally integrated moving average , economics , realized variance , stock (firearms) , stock market , implied volatility , computer science , mathematics , long memory , fractal , mechanical engineering , paleontology , horse , biology , engineering , mathematical analysis
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. Copyright © 2014 John Wiley & Sons, Ltd.