Equilibrium-Based Volatility Models of the Market Portfolio Rate of Return (Peacock Tails or Stotting Gazelles)

39 Pages Posted: 31 Aug 2012 Last revised: 3 Sep 2015

See all articles by David Feldman

David Feldman

Banking and Finance, UNSW Business School, UNSW Sydney; Financial Research Network (FIRN)

Xin Xu

Unisys Machine Learning and Advanced Analytics Services

Date Written: August 18, 2015

Abstract

Volatility models of the market portfolio’s return are central to financial risk management. Within an equilibrium framework, we introduce an implementation method and study two families of such models. One is deterministic volatility, represented by current popular models. Another is in the “constant elasticity of variance” family, in which we propose new models. Theoretically, we show that, together with constant expected returns, the latter family tends to have better ability to forecast. Empirically, our proposed models, while as easy to implement as the popular ones, outperform them in three out-of-sample forecast evaluations of different time periods, by standard predictability criteria. This is true particularly during high-volatility periods, whether the market rises or falls.

Keywords: Market Risk, Volatility Model, Systematic Risk, Market Portfolio, Predictive Power, Equilibrium, GARCH, RiskMetrics, Piecewise Constant Volatility, Constant Elasticity of Variance

JEL Classification: G17, G12, C58

Suggested Citation

Feldman, David and Xu, Xin, Equilibrium-Based Volatility Models of the Market Portfolio Rate of Return (Peacock Tails or Stotting Gazelles) (August 18, 2015). Available at SSRN: https://ssrn.com/abstract=2138653 or http://dx.doi.org/10.2139/ssrn.2138653

David Feldman (Contact Author)

Banking and Finance, UNSW Business School, UNSW Sydney ( email )

UNSW Sydney, NSW 2052
Australia
+61 2 9385 5748 (Phone)
+61 2 9385 6347 (Fax)

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

Xin Xu

Unisys Machine Learning and Advanced Analytics Services ( email )

PO Box 288
Concord West, NSW 2138
Australia

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