The Role of Correlation Dynamics in Sector Allocation

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent AbstractThis paper assesses the economic value of modelling conditional correlations for mean-variance portfolio optimization. Using sector returns in three major markets we show that the predictability of models describing empirical regularities in correlations such as time-variation, asymmetry and structural breaks leads to significant performance gains over the static covariance strategy. Investors would be willing to pay a fee of up to 983 basis points to switch from the static to the dynamic correlation portfolio and about 100 basis points more for capturing asymmetries and shifts in correlations. The gains are robust to the crisis, transaction costs and are most pronounced for monthly rebalancing. C32, C52, C53, F21, G11, G15 JEL Classification:

European equity market integration and joint relationship of conditional volatility and correlations

“…The decreasing DCC effects between Greek and the remaining EU countries during the crisis period are in sharp contrast to the unconditional correlations reported in Table 2. This shows the importance of modelling equity returns dynamically when the static correlations may portray misleading patterns (Kalotychou et al (2014)). It has been shown in Colacito et al (2011) that the efficiency improvements in the estimation of dynamic correlations are even higher when specification allows estimation of slow moving long run component.…”

“…A higher association between the volatility of country X and the bivariate correlation of country X with country Y will stipulate simultaneous discounting of profits under poor market conditions and the exacerbated need to manage the integrated risk or to insure against this spiral risk. Studying this relationship is important given literature has identified that asset allocation strategies which time/benchmark dynamic volatility (Fleming et al (2001) or dynamic correlations (Kalotychou et al (2014)) could yield economically higher profits. Kalotychou et al (2014) reports that risk-averse investor could pay substantially higher fees to reap greater economic benefits of a richer correlation specification such as the DCC model.…”

“…Studying this relationship is important given literature has identified that asset allocation strategies which time/benchmark dynamic volatility (Fleming et al (2001) or dynamic correlations (Kalotychou et al (2014)) could yield economically higher profits. Kalotychou et al (2014) reports that risk-averse investor could pay substantially higher fees to reap greater economic benefits of a richer correlation specification such as the DCC model. Our analysis will make portfolio managers and investors aware of the flip side of this investing: in tandem movement of the two processes (volatility and correlations) can result in increased investing fragilities.…”

Industry momentum: an exchange‐traded funds approach

“…Primary sources of industry momentum returns are uncertain, and proposed explanations relate mainly to either delayed reaction to the traditional Fama‐French factors (Du and Denning, 2005), firm productivity (Chen et al ., 2007), time‐varying serial correlation (Du and Watkins, 2007), industry growth (Safieddine and Sonti, 2007), structural changes (Chui et al ., 2003; Kalotychou et al ., 2014) and herding (Demirer et al ., 2015).…”

Diversification and the benefits of using returns standardized by range‐based volatility estimators

“…In our case, we opt for using the Asymmetric Dynamic Conditional Correlation GARCH model proposed by Cappiello, Engle, and Sheppard (). The choice of this model is based on the results of Gupta and Donleavy (), Kalotychou, Staikouras, and Zhao (), Zhou and Nicholson (), Yuan et al (), and Badshah () who show that modelling covariance asymmetry on the basis of the ADCC model contributes significantly to the economic value of the model due to the fact that conditional volatility, and the correlation of financial returns, tends to rise more after negative return shocks than after positive ones of the same size.…”