A Comprehensive Look at Financial Volatility Prediction by Economic Variables

Journal of Applied Econometrics (Impact Factor: 1.76). 01/2010; 27(2010-58). DOI: 10.2139/ssrn.1737433
Source: RePEc


What drives volatility on financial markets? This paper takes a comprehensive look at the predictability of financial market volatility by macroeconomic and financial variables. We go beyond forecasting stock market volatility (by large the focus in previous studies) and additionally investigate the predictability of foreign exchange, bond, and commodity volatility by means of a data-rich modeling methodology which is able to handle a potentially large number of predictor variables. In line with previous research, we find relatively little economically meaningful predictability of stock market volatility. By contrast, volatility in foreign exchange, bond, and commodity markets appears predictable by macro and financial predictors both in-sample and out-of-sample.

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    • "default spreads), TED spreads for LIBOR rate and T-Bill rates discussed in funding liquidity in Brunnermeier and Pedersen (2009) as well as aggregate measures of bid-ask spreads in foreign exchange markets such as those discussed in Menkhoff et al. (2012); and macro-economic variables. In this study of Christiansen et al. (2012) they concentrate on explaining carefully individual exchange rates through Bayesian model averaging, however they neglect to study the joint relationships between multiple exchange rates and the influence of these factors proposed. In this analysis, we intend to generalize these types of studies to focus on joint behaviours in multiple exchange rates and we focus on a few important factors principally related to the exchange rate market dynamics. "
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    • "Many studies that focus on volatility forecasting have identified through several approaches key macroeconomic and financial variables as important drivers of volatility, highlighting their power in improving forecast performances. For instance, Christiansen et al. (2012) predicted the asset return volatility by means of macroeconomic and financial variables in a Bayesian Model Averaging framework. They considered several asset classes, such as equities, foreign exchange, bonds, and commodities, over long time spans and found that economic variables provide information about future volatility from both an in-sample and an out-of-sample perspective . "
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