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Macro-Driven VaR Forecasts: From Very High to Very Low Frequency Data

Yves Dominicy and Harry Vander Elst ()

Working Papers ECARES from ULB -- Universite Libre de Bruxelles

Abstract: This paper studies in some details the joint-use of high-frequency data and economic variables tomodel financial returns and volatility. We extend the Realized LGARCH model by allowing for a timevaryingintercept, which responds to changes in macroeconomic variables in a MIDAS framework andallows 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 combinationof low-frequency exogenous economic indicators with high-frequency financial data improves our abilityto forecast the volatility of returns, their full multi-step ahead conditional distribution and the multiperiodValue-at-Risk. We document that nominal corporate profits and term spreads generate accuraterisk measures forecasts at horizons beyond two business weeks.

Keywords: realized LGARCH; value-at-risk; density forecasts; realized measures of volatility (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
Pages: 25 p.
Date: 2015-11
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mst and nep-rmg
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