On the Economic Sources of Stock Market Volatility

Abstract

We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short run from secular movements. We combine insights from Engle and Rangel (2007) and the recent work on mixed data sampling (MIDAS), as in e.g. Ghysels, Santa-Clara, and Valkanov (2005). The new class of models is called GARCH-MIDAS, since it uses a mean reverting unit daily GARCH process, similar to Engle and Rangel (2007), and a MIDAS polynomial which applies to monthly, quarterly, or bi-annual macroeconomic or financial variables. We study long historical data series of aggregate stock market volatility, starting in the 19th century, as in Schwert (1989). We formulate models with the long term component driven by inflation and industrial production growth that are at par in terms of out-of-sample prediction for horizons of one quarter and out-perform more traditional time series volatility models at longer horizons. Hence, imputing economic fundamentals into volatility models pays off in terms of long horizon forecasting. We also find that at a daily level, inflation and industrial production growth, account for between 10 % and 35 % of oneday ahead volatility prediction. Hence, macroeconomic fundamentals play a significant role even at short horizons. Unfortunately, all the models -purely time series ones as well as those driven by economic variables -feature structural breaks over the entire sample spanning roughly a century and a half of daily data. Consequently, our analysis also focuses on subsamplespre-WWI, the Great Depression era, and post-WWII (also split to examine the so called Great Moderation). Our main findings remain valid across subsamples. * We are most grateful to Bill Schwert for providing us with data used in his 1989 paper and Gonzalo Rangel for help with the Spline-GARCH estimations. We thank

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

“…The inclusion of the volatilities of inflation and production growth follows Schwert (1989) and Engle, Ghysels, and Sohn (2008). These volatilities provide information regarding the extent of uncertainty surrounding macroeconomic prospects, a feature emphasized by the literature linking learning and stock market volatility.…”

“…This paper is among several recent studies that embody a resurgence of interest in connections between macroeconomic conditions and stock return volatility. Related work includes Adrian and Shin (2010), David and Veronesi (2009), Engle, Ghysels, and Sohn (2008, and Ludvigson and Ng (2007). Relative to these studies, the present paper focuses on applying recently developed econometric techniques to evaluate the extent to which macroeconomic and financial variables improve volatility forecasts out-of-sample.…”

Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification

“…By imposing the parameter restriction that Engle et al (2012) suggest that the performance of the GARCH-MIDAS model can be improved by including the future values of the macro variables (i.e. so called two-sided filter) when anticipating the long term volatility.…”

“…Imposing RC θ to be zero and applying the two-sided filter of Engle et al (2012), eq. (15) can be modified as follows:…”

Jump-Diffusion Long-Run Risks Models, Variance Risk Premium, and Volatility Dynamics*

“…The variance and autocovariance moments in VIX help pin down the mean-reverting parameters of the volatility factor(s). In nov (2005) and Engle et al (2008).order to reduce computational cost, I do not include autocovariance moments in realized stock volatility during the calibration process; however I check how calibrated models match the dynamic feature in realized stock volatility.…”