Deja Vol: Predictive Regressions for Aggregate Stock Market Volatility Using Macroeconomic Variables

Abstract

Aggregate stock return volatility is both persistent and countercyclical. This paper tests whether it is possible to improve volatility forecasts at monthly and quarterly horizons by conditioning on additional macroeconomic variables. I find that several variables related to macroeconomic uncertainty, time-varying expected stock returns, and credit conditions Granger cause volatility. It is more difficult to find evidence that forecasts exploiting macroeconomic variables outperform a univariate benchmark out-of-sample. The most successful approaches involve simple combinations of individual forecasts. Predictive power associated with macroeconomic variables appears to concentrate around the onset of recessions.JEL classification: G12; C22

On the influence of US monetary policy on crude oil price volatility

“…Third, to the best of our knowledge, we are the first to verify the joint influence of the macroeconomic determinants together with the (proxy of) monetary policy decisions over the COFP volatility. This is in line with Paye (2012), who argues that the macrovariables are able to improve volatility forecasts only if they are not considered separately. Since we are interested in the daily COFP volatility, and the macrovariables are observed monthly, we use a model which allows for mixed data sampling in a GARCH framework, that is, the GARCH-MIDAS model recently proposed by Engle et al (2013).…”

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“…W kolejnych krokach zostały wykorzystane do opracowania udoskonalonych modeli prognozowania zmienności, szcze-gólnie w długich okresach (Engle i Rangel, 2008;Engle i in., 2013). Paye (2012) i Christiansen i in. (2012) analizują związek między niestabilnością finansową a warunkami makroekonomicznymi.…”

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“…In our numerical example (Section 5.1) we follow Boudoukh et al (2007) and Paye (2012) and use the net payout yield (dividend plus equity repurchases less equity issuances) as an observed predictor for risk premium and stock return volatility. Since each predictor can have different predictive power on the return distribution, we weight the impact of the observed and not observed variables on the risk premium.…”

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