Modeling and forecasting exchange rate volatility in time-frequency domain

Abstract

This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of volatility into several timescales approximates the behaviour of traders at corresponding investment horizons. The proposed methodology is moreover able to account for impact of jumps due to a recently proposed jump wavelet two scale realized volatility estimator. We propose a realized Jump-GARCH models estimated in two versions using maximum likelihood as well as observation-driven estimation framework of generalized autoregressive score. We compare forecasts using several popular realized volatility measures on foreign exchange rate futures data covering the recent financial crisis. Our results indicate that disentangling jump variation from the integrated variation is important for forecasting performance. An interesting insight into the volatility process is also provided by its multiscale decomposition. We find that most of the information for future volatility comes from high frequency part of the spectra representing very short investment horizons. Our newly proposed models outperform statistically the popular as well conventional models in both one-day and multi-period-ahead forecasting.

Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model

“…Therefore, the information reflecting the economic fundamentals of global stock markets diffuses from the US to other countries' markets, creating the volatility spillover. This paper is related to a growing number of papers on volatility spillover in stock markets (see, e.g., Barunik, Krehlik, & Vacha, 2016;Bonato, Caporin, & Ranaldo, 2013;Buncic & Gisler, 2016;Dean, Faff, & Loudon, 2010;Eun & Shim, 1989;Fengler & Gisler, 2015;Hamao, Masulis, & Ng, 1990;Lin, Engle, & Ito, 1994). Most of these studies, except for Bonato et al (2013) and Buncic and Gisler (2016), are limited to in-sample evidence concerning the presence or absence of volatility spillover but do not give out-of-sample results.…”

Do Co-Jumps Impact Correlations in Currency Markets?

“…The main reason why we focus on wavelet analysis is its remarkable ability to detect jumps and sharp cusps even if covered by noise (Donoho and Johnstone, 1994;Wang, 1995). Several authors have used these results to improve the jump estimation (Fan and Wang, 2007;Barunik and Vacha, 2015;Barunik et al, 2016;Xue et al, 2014). The reported improvements originate from the fact that wavelets are able to decompose noisy time series into separate time-scale components.…”

Heterogeneous autoregressive model with structural break using nearest neighbor truncation volatility estimators for DAX

“…However, the RV estimation becomes inconsistent (Barndorff-Nielsen and Shephard 2004) for integrated volatility under the presence of abrupt jumps (structural breaks). There is ample empirical evidence on this phenomenon in financial markets (Duonga and Swanson 2015; Ewing and Malik 2016; Barunika et al 2016; Dendramis et al 2015). The structural break may cause by voluminous drastic feedbacks from market participants due to new inflow market information.…”