Travail de recherche/Working paper
| Résumé : | 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. |
