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Previous studies have shown that stock returns bear a premium for downside risk versus upside potential. We develop a new risk measure which scales the traditional CAPM beta by the ratio of the upside beta to the downside beta, thereby incorporating the effects of both upside potential and downside risk. This “modified” beta has substantial explanatory power in standard asset pricing tests, outperforming existing measures, and it is robust to various alternative modeling and estimation techniques.

From the early 1980s until the late 1990s the term structure of interest rates in Chile was usually downward sloping, particularly for long maturities. We postulate that the explanation is behind liquidity premium of the term structure of interest rates. Based upon a parsimonious theoretical model, we show that the sign of liquidity premium depends on both expected return and risk.

For our sample period 1983–1999, investors were willing to hold long-term assets even though their return was relatively lower. This appears to be a consequence of indexation, which reduced risk of long-term bonds as their return was linked to past inflation.

This study aims to investigate whether the zero-investment portfolio strategy generates higher excess returns for all listed companies in the Stock Exchange of Thailand (SET) or ESG100 stocks. The study period is from January 2016 to December 2020, a total of 60 months. The dividend yield is employed for categorizing the stock into value and growth stocks. The strategy of buying value stocks and short-selling growth stocks is then applied. The results show that investing using the zero-investment portfolio strategy can generate higher returns in an investment portfolio that consists of ESG100 stocks than in an investment portfolio that consists of all stocks in the SET. The optimal holding periods for investing in portfolios that consist of stocks in the SET are 6 months, 9 months, and 12 months, and the optimal holding periods for a portfolio that consists of ESG100 stocks is 6 months. To explain excess returns of stocks in the SET, the Fama and French (2015) five-factor model is employed. There is no relation between risk factors and excess returns for the holding period of 6 months and 12 months. However, excess return is found to have a negative relation with the market risk premium factor for a 9-month holding period. The excess returns of ESG100 stocks are also inversely correlated with investment factors for a holding period of 6 months.

We address an interesting case – the predictability of excess US asset returns from macroeconomic factors within a flexible regime-switching VAR framework – in which the presence of regimes may lead to superior forecasting performance from forecast combinations. After documenting that forecast combinations provide gains in predictive accuracy and that these gains are statistically significant, we show that forecast combinations may substantially improve portfolio selection. We find that the best-performing forecast combinations are those that either avoid estimating the pooling weights or that minimize the need for estimation. In practice, we report that the best-performing combination schemes are based on the principle of relative past forecasting performance. The economic gains from combining forecasts in portfolio management applications appear to be large, stable over time, and robust to the introduction of realistic transaction costs.

American depository receipts (ADRs) listed on the New York Stock Exchange during the 1990s and 2000s are compared to determine how well they performed versus the US index and respective regional indexes utilizing three-year holding period excess returns. Results suggest that ADRs listed in the 2000s perform better than those in the 1990s. Also, seasoned equity offerings performed better than initial public offerings. Regression analysis indicated the best predictors of ADR performance are the returns of the respective regional index where the ADR-listing firm is headquartered, the date of issue (2000s vs 1990s), and whether the ADR was from an emerging economy.

We evaluate the performance of fixed income mutual funds using stochastic discount factors motivated by continuous-time term structure models. Time-aggregation of these models for discrete returns generates new empirical “factors,” and these factors contribute significant explanatory power to the models. We provide a conditional performance evaluation for US fixed income mutual funds, conditioning on a variety of discrete ex-ante characterizations of the states of the economy. During 1985–1999 we find that fixed income funds return less on average than passive benchmarks that do not pay expenses, but not in all economic states. Fixed income funds typically do poorly when short-term interest rates or industrial capacity utilization rates are high, and offer higher returns when quality-related credit spreads are high. We find more heterogeneity across fund styles than across characteristics-based fund groups. Mortgage funds underperform a GNMA index in all economic states. These excess returns are reduced, and typically become insignificant, when we adjust for risk using the models.

We examine the relationship between how mutual fund sponsors configure their board(s) of directors and the performance of the funds under a particular board's purview. Fund sponsors utilize either one board to oversee all the funds within a fund family or multiple boards that oversee one fund or a subset of the family's funds. Our results suggest that fund families – that is, sponsors – that use multiple boards have significantly higher objective-adjusted board-level weighted excess returns. But, there are no significant differences in the objective-adjusted board-level weighted excess expenses. These results are consistent with the argument that multiple boards provide superior monitoring.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

This chapter tests the market risk and economic policy uncertainty (EPU) of five Asian stock market returns and finds positive and significant intertemporal relations between excess stock returns and conditional volatility/downside risk. The results support positive risk-return relations across five Asian markets after controlling for the lagged dividend yield and the change in EPU ( Δ EPU). The evidence strongly indicates that excess stock returns are negatively correlated with the Δ EPUs. This finding holds true not only for the domestic market but also for external sources. The negative effect of Δ EPU is more profound from the US and global markets as compared with those from the Europe, Japanese, and domestic markets and suggests that a pathway to forming an optimal strategy for portfolio risk management depends on developing an effective hedging strategy against the impact of Δ EPUs from US/global markets.

This chapter develops a return forecasting methodology that allows for instability in the relationship between stock returns and predictor variables, model uncertainty, and parameter estimation uncertainty. The predictive regression specification that is put forward allows for occasional structural breaks of random magnitude in the regression parameters, uncertainty about the inclusion of forecasting variables, and uncertainty about parameter values by employing Bayesian model averaging. The implications of these three sources of uncertainty and their relative importance are investigated from an active investment management perspective. It is found that the economic value of incorporating all three sources of uncertainty is considerable. A typical investor would be willing to pay up to several hundreds of basis points annually to switch from a passive buy-and-hold strategy to an active strategy based on a return forecasting model that allows for model and parameter uncertainty as well as structural breaks in the regression parameters.