Volatility Forecasting for Low-Volatility Investing

38 Pages Posted: 1 Aug 2022

See all articles by Christian Conrad

Christian Conrad

Heidelberg University - Alfred Weber Institute for Economics; ETH Zürich - KOF Swiss Economic Institute

Onno Kleen

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE); Tinbergen Institute

Date Written: July 10, 2022

Abstract

Low-volatility investing is typically implemented by sorting stocks based on simple risk measures; for example, the empirical standard deviation of last year's daily returns. In contrast, we understand identifying next-month's ranking of volatilities as a forecasting problem aimed at the ex-post optimal sorting. We show that time series models based on intraday data outperform simple risk measures in anticipating the cross-sectional ranking in real time. The corresponding portfolios are more similar to the ex-ante infeasible optimal portfolio in multiple dimensions. Moreover, the increased signal in our improved volatility sorts survives portfolio weight smoothing for mitigating transaction costs.

Keywords: factor investing, low-volatility anomaly, volatility forecasts, forecast evaluation

JEL Classification: C22, C55, C58, G11

Suggested Citation

Conrad, Christian and Kleen, Onno, Volatility Forecasting for Low-Volatility Investing (July 10, 2022). Available at SSRN: https://ssrn.com/abstract=4158925 or http://dx.doi.org/10.2139/ssrn.4158925

Christian Conrad

Heidelberg University - Alfred Weber Institute for Economics ( email )

Grabengasse 14
Heidelberg, D-69117
Germany
+49 (06)221 543173 (Phone)

HOME PAGE: http://www.uni-heidelberg.de/conrad

ETH Zürich - KOF Swiss Economic Institute ( email )

Zurich
Switzerland

Onno Kleen (Contact Author)

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE) ( email )

P.O. Box 1738
3000 DR Rotterdam, NL 3062 PA
Netherlands

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 3062 PA
Netherlands

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