Cross‐region and cross‐sector asset allocation with regimes

Abstract

Cross-region and cross-sector asset allocation decisions are one of the most fundamental issues in international equity portfolio management. Equity returns exhibit higher volatilities and correlations, and lower expected returns, in bear markets compared to bull markets. However, static mean-variance analysis fails to capture this salient feature of equity returns. We accommodate the nonlinearity of returns using a regime switching model across both regions and sectors. The regime-dependent asset allocation potentially adds value to the traditional static mean-variance allocation. In addition, optimal allocation across sectors provide greater benefits compared to international diversification, which is characterized by higher returns, lower risks, lower correlations with the world market and a higher Sharpe ratio.

US sector rotation with five-factor Fama–French alphas

“…Further, Chong and Philips (2015) find that a portfolio of sector ETFs constructed as a response of sectors to economic factors performs well relative to S&P 500 index. Outperformance of sector rotation strategy is also documented in the study of Baca, Garbe and Weiss (2000); Conover et al (2005); Shynkevich (2013) and Dou et al (2014). Sector rotation studies differ, among other things, in indicators used to signal a switch from one sector to another.…”

“…They argue that, fund managers may deviate from the passive market portfolio by having their portfolio with specific industry concentration, and prove that funds that deviate more from the overall market by focusing on particular industries tend to perform better. Dou et al (2014) study asset allocation in different economic regimes across sectors in the developed countries (North America, UK, Japan, and Europe). They report positive alpha of Energy, HiTech, and Health sectors; and negative alphas of Durable, Telecom, and Manufacturing sectors both in the bull market and the bear market.…”

The Impact of Disruptive Technology on Banking Under Switching Volatility Regimes

“…Particularly, Markov regime-switching (MRS) models, which are widely applied in finance and macroeconomics, suppose that an observed process is triggered by an unobserved state process. Evidence supports the statement that MRS modelling outperforms static mean-variance strategies (e.g., Ang & Bekaert, 2004;Kritzman et al, 2012;Dou et al, 2014). Quandt (1960) introduced the methodology to estimate a single switching point position for a linear regression system and the MRS model was presented by Goldfeld and Quandt (1973).…”