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Optimal Pairs Trading with CEV Time-Varying Volatility

Article arXiv papers · Author: T. N. Li et al.
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Summary

This document presents a pairs trading model for two cointegrated assets that accounts for time-varying volatility using a Constant Elasticity of Variance specification. It frames portfolio selection as a stochastic control problem over a fixed horizon: portfolio weights are chosen to maximize expected power utility of terminal wealth. A finite difference method is used to compute the optimal strategy, while model parameters are estimated with the Generalized Method of Moments.

The authors illustrate the approach with tests on historical daily market data. The excerpt describes the model and estimation and solution methods, but gives no specific performance results, asset identities, sample period, transaction-cost treatment, or comparison with alternative strategies. Its usefulness therefore lies mainly in the modeling framework; the brief description is insufficient to judge practical profitability or robustness.

Key ideas

  • The model applies a Constant Elasticity of Variance process to time-varying volatility in pairs trading.
  • It assumes a fixed horizon and two cointegrated assets.
  • Optimal portfolio weights maximize expected power utility of terminal wealth.
  • A finite difference method computes strategies, and the Generalized Method of Moments estimates parameters.
  • Historical daily data illustrate the method, but the excerpt provides no performance figures or cost analysis.

Tags

Pairs tradingVolatilityPortfolio constructionStatisticsBacktesting
Full text
# Optimal Pairs Trading with Time-Varying Volatility


# Optimal Pairs Trading with Time-Varying Volatility









We propose a pairs trading model that incorporates a time-varying volatility of the Constant Elasticity of Variance type. Our approach is based on stochastic control techniques; given a fixed time horizon and a portfolio of two co-integrated assets, we define the trading strategies as the portfolio weights maximizing the expected power utility from terminal wealth. We compute the optimal pairs strategies by using a Finite Difference method. Finally, we illustrate our results by conducting tests on historical market data at daily frequency. The parameters are estimated by the Generalized Method of Moments.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.