Extreme observations and diversification in Latin American emerging equity markets

In this paper, we focus on the tails of the unconditional distribution of Latin American emerging markets stock returns. We explore their implications for portfolio diversification according to the safety first principle, first proposed by Roy [Econometrica (1952) 20, 431]. We find that the Latin American emerging markets have significantly fatter tails than industrial markets, especially, the lower tail of the distribution. We consider the implication of the safety first principle for a US investor who creates a diversified portfolio using Latin American stock markets. We find that a US investor gains by adding Latin American equity markets to her purely domestic portfolio. For different parameter specifications, we find a more realistic asset allocation than the one suggested by the literature based on the traditional mean-variance framework.     

An economic evaluation of stock–bond return comovements with copula-based GARCH models

Owing to their importance in asset allocation strategies, the comovements between the stock and bond markets have become an increasingly popular issue in financial economics. Moreover, the copula theory can be utilized to construct a flexible joint distribution that allows for skewness in the distribution of asset returns as well as asymmetry in the dependence structure between asset returns. Therefore, this paper proposes three classes of copula-based GARCH models to describe the time-varying dependence structure of stock–bond returns, and then examines the economic value of copula-based GARCH models in the asset allocation strategy. We compare their out-of-sample performance with other models, including the passive, the constant conditional correlation (CCC) GARCH and the dynamic conditional correlation (DCC) GARCH models. From the empirical results, we find that a dynamic strategy based on the GJR-GARCH model with Student-t copula yields larger economic gains than passive and other dynamic strategies. Moreover, a less risk-averse investor will pay higher performance fees to switch from a passive strategy to a dynamic strategy based on copula-based GARCH models.     

Predicting bear and bull stock markets with dynamic binary time series models

Despite the voluminous empirical research on the potential predictability of stock returns, much less attention has been paid to the predictability of bear and bull stock markets. In this study, the aim is to predict U.S. bear and bull stock markets with dynamic binary time series models. Based on the analysis of the monthly U.S. data set, bear and bull markets are predictable in and out of sample. In particular, substantial additional predictive power can be obtained by allowing for a dynamic structure in the binary response model. Probability forecasts of the state of the stock market can also be utilized to obtain optimal asset allocation decisions between stocks and bonds. It turns out that the dynamic probit models yield much higher portfolio returns than the buy-and-hold trading strategy in a small-scale market timing experiment.     

Spillovers among sovereign CDS,stock and commodity markets: A correlation network perspective

In the wake of the globalization of financial markets, studying spillovers among different asset markets, especially spillovers that include sovereign CDS markets, is of vital importance. This paper attempts to build a spillover network to investigate the complex interactions within the system of sovereign CDS, stock and commodity markets by adopting the spillover index based on forecast error variance (FEV) decomposition. The results reveal that emerging countries have larger average spillovers than developed countries with regard to sovereign CDS-to-stock returns spillovers, while the developed countries contribute more average spillovers than the emerging countries in the opposite direction. Moreover, the sovereign CDS market and the commodity market still demonstrate a relatively important role during certain periods although stock markets always occupy the dominant position during every phase. Our findings provide new insights into spillovers among the major global asset markets using a network perspective, which is valuable for regulation of financial markets, asset allocation and portfolio risk management.     

Dynamic correlations and volatility effects in the Balkan equity markets

This paper investigates the presence of time-varying comovements, volatility implications and dynamic correlations in major Balkan and leading mature equity markets, in order to provide quantified responses to international asset allocation decisions. Since asset returns and correlation dynamics are critical inputs in asset pricing, portfolio management and risk hedging, emphasis is placed on the respective (constant and dynamic) equity market correlations produced by alternative multivariate GARCH forms, the Constant Conditional Correlation and the Asymmetric Dynamic Conditional Correlation models. The Balkan stock markets are seen to exhibit time-varying correlations as a peer group, although correlations with the mature markets remain relatively modest. In conjunction with sensitivity analysis on the asymmetric variance–covariance matrix, active portfolio diversification to the Balkan equity markets indicates to potentially improve investors’ risk-return trade-off.     

Economic valuation of liquidity timing

This paper conducts a horse-race of different liquidity proxies using dynamic asset allocation strategies to evaluate the short-horizon predictive ability of liquidity on monthly stock returns. We assess the economic value of the out-of-sample power of empirical models based on different liquidity measures and find three key results: liquidity timing leads to tangible economic gains; a risk-averse investor will pay a high performance fee to switch from a dynamic portfolio strategy based on various liquidity measures to one that conditions on the Zeros measure (Lesmond et al., 1999); the Zeros measure outperforms other liquidity measures because of its robustness in extreme market conditions. These findings are stable over time and robust to controlling for existing market return predictors or considering risk-adjusted returns.     

Including commodity futures in asset allocation in China

In this paper, we investigate the role of eight commodity futures in asset allocation in China during the period January 2004–December 2015. The Chinese commodities and stocks are moderately correlated. We use quantile regressions based on a value-at-risk model to examine the relation between these two markets. We find no risk spillovers between the markets, suggesting that stocks and commodities in China are exposed to different risks. Using different asset allocation strategies, we show that including soymeal and soybeans in the Chinese stock index can offer some diversification gains. However, other Chinese commodities may not be useful for portfolio diversification.     

Timing and diversification: A state-dependent asset allocation approach

Abstract

The influence of changing economic environment leads the distribution of stock market returns to be time-varying. A conditionally optimal investment hence requires a dynamic adjustment of asset allocation. In this context, this paper examines the improvement in portfolio performance by simulating portfolio strategies that are conditioned on the Markov regime switching behaviour of stock market returns. Including a memory effect eliminates the empirical shortcoming of discrete state models, namely that they produce a standard and an extreme state in stock returns. So far, this has prevented the regimes from being used as a valuable conditioning variable. Based on a discrete state indicator variable, is presented evidence of considerable performance improvement relative to the static model due to optimal shifting between aggressive and well diversified portfolio structures.     

Constructing inverse factor volatility portfolios: A risk-based asset allocation for factor investing

In this study, we investigate risk-based asset allocation approaches for factor investing strategies by constructing a multifactor portfolio based on the inverse weighting method. We propose the inverse factor volatility (IFV) strategy, which is the simplified variant of a factor risk parity, assuming constant factor correlation. In IFV portfolio construction, the portfolio's weights are determined by using scaled inverse factor volatility treated as a proxy for a targeted exposure in the optimization. Based on daily stock and index returns on global markets from 2002 to the end of 2017, we implemented the empirical analysis of IFV portfolios among three stock markets: Japan, Euro, and the US. The results obtained reveal that the IFV portfolios significantly outperformed market capitalization weighted portfolios by successfully acquiring factor risk premiums.     

Portfolio Selection and Optimization with Higher Moments: Evidence from the Indian Stock Market

The Markowitz portfolio optimization model, popularly known as the Mean-Variance model, assumes that stockreturns follow normal distribution. But when stock returns do not follow normal distribution, this model wouldbe inadequate as it would prescribe sub-optimal portfolios. Stock market literature often deliberates that stock returns are non-normal. In such context the Markowitz model would not be sufficient to estimate the portfolio risks. The purpose of this paper is to expand the original Markowitz portfolio theory (mean-variance) via adding the higher order moments like skewness (third moment about the mean) and kurtosis (fourth moment about the mean) in the return characteristics. The research paper investigates the impact of including higher moments using multi-objective programming model for portfolio stock selection and optimization. The empirical results indicate that the inclusion of higher moments had a considerable impact in estimating the returns behavior of portfolios. The portfolios optimized using all the four moments, generated higher returns for the given level of risk in comparison to the returns of the Markowitz model during the study period 2000–2011. The results of this study would be immensely useful to fund managers, portfolio managers and investors as it would help them in understanding the Indian stock market behavior better and also in selecting alternative portfolio selection models.