Macro-Driven VAR Forecasts: From Very High to Very Low-Frequency Data
25 Pages Posted: 10 Dec 2015
Date Written: December 9, 2015
Abstract
This paper studies in some details the joint-use of high-frequency data and economic variables to model financial returns and volatility. We extend the Realized LGARCH model by allowing for a timevarying intercept, which responds to changes in macroeconomic variables in a MIDAS framework and allows macroeconomic information to be included directly into the estimation and forecast procedure. Using more than 10 years of high-frequency transactions for 55 U.S. stocks, we argue that the combination of low-frequency exogenous economic indicators with high-frequency financial data improves our ability to forecast the volatility of returns, their full multi-step ahead conditional distribution and the multiperiod Value-at-Risk. We document that nominal corporate profits and term spreads generate accurate risk measures forecasts at horizons beyond two business weeks.
Keywords: Realized LGARCH, Value-at-Risk, density forecasts, realized measures of volatility
JEL Classification: C22, C53, C58, G17
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