Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies
University of North Carolina Kenan-Flagler Business School; University of North Carolina (UNC) at Chapel Hill - Department of Economics
New University of Lisbon - Nova School of Business and Economics; National Bureau of Economic Research (NBER); Centre for Economic Policy Research (CEPR)
Rossen I. Valkanov
University of California, San Diego (UCSD) - Rady School of Management
August 17, 2003
Anderson School of Management Working Paper and UNC Department of Economics Working Paper
We use the MIDAS (Mixed Data Sampling) approach to study regressions of future realized volatility at low-frequency horizons (one to four weeks) on lagged daily and intra-daily (1) squared returns, (2) absolute returns, (3) realized volatility, (4) realized power and (5) return ranges. We document first of all that daily realized power and daily range are surprisingly good predictors of future realized volatility and outperform models based on realized volatility. Moreover, MIDAS models with daily data - range, realized power, realized volatility - require a polynomial with at least 30 days. We document that high-frequency absolute returns are also better at forecasting future low frequency realized volatility than high-frequency squared returns. We also discuss many issues that are encountered in practice, such as long memory and seasonality.
All the results are based on a commonly used FX data set.
Number of Pages in PDF File: 46
Keywords: variance estimation, volatility, asset pricing, MIDAS
JEL Classification: G12, G10, C32, C53
Date posted: October 5, 2003
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