Direct Versus Iterated Multi-Period Volatility Forecasts

Eric Ghysels, Alberto Plazzi, Rossen Valkanov, Antonio Rubia Serrano, Asad Dossani
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引用次数: 3

Abstract

Multi-period-ahead forecasts of returns’ variance are used in most areas of applied finance where long horizon measures of risk are necessary. Yet, the major focus in the variance forecasting literature has been on one-period-ahead forecasts. In this paper, we compare several approaches of producing multi-period-ahead forecasts within the GARCH and RV families – iterated, direct, and scaled short-horizon forecasts. We also consider the newer class of mixed data sampling (MIDAS) methods. We carry the comparison on 30 assets, comprising of equity, Treasury, currency, and commodity indices. While the underlying data is available at high-frequency (5-minutes), we are interested at forecasting variances 5, 10, 22, 44, and 66 days ahead. The empirical analysis, which is carried in-sample and out-of-sample with data from 2005 to 2018, yields the following results. For GARCH, iterated GARCH dominates the direct GARCH approach. In the case of RV, the direct RV is preferred to the iterated RV. This dichotomy of results emphasizes the need for an approach that uses the richness of high-frequency data and, at the same time produces a direct forecast of the variance at the desired horizon, without iterating. The MIDAS is such an approach and, unsurprisingly, it yields the most precise forecasts of the variance, in and out-of-sample. More broadly, our study dispels the notion that volatility is not forecastable at long horizons and offers an approach that delivers accurate out-of-sample predictions.
直接与迭代的多周期波动预测
在应用金融的大多数领域,对收益方差进行多期预测是必要的,因为这些领域需要对风险进行长期衡量。然而,方差预测文献的主要焦点是对一个时期的预测。在本文中,我们比较了几种在GARCH和RV家族中产生多周期提前预测的方法-迭代,直接和缩放短期预测。我们还考虑了较新的混合数据采样(MIDAS)方法。我们对30种资产进行比较,包括股票、国债、货币和商品指数。虽然基础数据在高频(5分钟)可用,但我们对预测5天、10天、22天、44天和66天的方差感兴趣。对2005年至2018年的样本内和样本外数据进行实证分析,得出以下结果:对于GARCH,迭代GARCH优于直接GARCH方法。在RV的情况下,直接RV优于迭代RV。这种结果的二分法强调了一种方法的必要性,这种方法既要利用丰富的高频数据,同时又要直接预测期望水平上的方差,而不需要迭代。MIDAS就是这样一种方法,不出所料,它对样本内和样本外的方差做出了最精确的预测。更广泛地说,我们的研究消除了长期波动不可预测的观念,并提供了一种提供准确样本外预测的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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