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 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 pseudo out-of-sample prediction for horizons of one quarter and are at par or 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 one-day 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 subsamples -pre-WWI, the Great Depression era, and post-WWII (also split to examine the so-called Great Moderation). Our main findings remain valid across subsamples.
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