Volatility forecasting for low-volatility investing

54 Pages Posted: 1 Aug 2022 Last revised: 30 Nov 2023

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

Rasmus Lönn

Erasmus University Rotterdam (EUR) - Department of Econometrics

Date Written: July 10, 2022

Abstract

Low-volatility investing often involves sorting and selecting stocks based on retrospective risk measures, for example, the historical standard deviation of returns. In this paper, we use the volatility forecasts from a wide spectrum of volatility models to sort and select stocks and estimate portfolio weights. Our portfolios are more closely aligned with the ex-post optimal portfolio and deliver large, significant economic gains compared to traditional benchmarks after transaction costs. Importantly, we find that choosing portfolio weights by optimally combining the volatility forecasts from the different models delivers the strongest forecast and financial performance in real-time.

Keywords: Factor investing, low-volatility allocations, volatility forecasts

JEL Classification: C22, C55, C58, G11

Suggested Citation

Conrad, Christian and Kleen, Onno and Lönn, Rasmus, 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

Rasmus Lönn

Erasmus University Rotterdam (EUR) - Department of Econometrics ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

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