Time-varying risk aversion and the profitability of momentum trades

We show that time-varying risk aversion serves as a significant predictor of stock market momentum in the U.S. and globally. Risk aversion is found to be a robust predictor of momentum returns even after controlling for various well established stock market predictors and absorbs the predictive power of market volatility. The findings imply that momentum strategies can be enhanced by conditioning trades on the degree of risk aversion in the marketplace

Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram

open access articleThis paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models

Sequential valuation networks for asymmetric decision problems

This paper deals with representation and solution of asymmetric decision problems. We describe a new representation called sequential valuation networks that is a hybrid of Covaliu and Oliver’s sequential decision diagrams and Shenoy’s valuation networks. The solution algorithm is based on the idea of decomposing a large asymmetric problem into smaller sub-problems and then using the fusion algorithm of valuation networks to solve the sub-problems. Sequential valuation networks inherit many of the strengths of sequential decision diagrams and valuation networks while overcoming many of their shortcomings. We illustrate our technique by representing and solving a modified version of Covaliu and Oliver’s [Manage. Sci. 41(12) (1995) 1860] Reactor problem in complete detail

Does liquidity risk explain the time-variation in asset correlations? Evidence from stocks, bonds and commodities

The time-variation in asset correlations have broad implications in asset pricing, portfolio management and hedging. Numerous studies in the literature have found that the change in correlations is mainly related to the magnitude of market movements, hence volatility. However, recent research finds that the size of markets fluctuations is not necessarily the primary driver for the time-variation in correlations, but that the effect of market movements is amplified in times of high financial distress, characterised by low liquidity. This paper seeks to investigate the effect of liquidity on time-varying correlations among different asset classes, namely stocks, corporate bonds and commodities. Our findings show that liquidity indeed has a significant effect on the time-variation in asset correlations, particularly in the case of how bond returns co-move with other asset classes. We observe that higher liquidity risk is associated with lower correlation of bond returns with stocks as well as commodities. Our findings suggest that measures of liquidity risk can improve models of correlations; and potentially help improve the effectiveness of risk management strategies during periods of financial distress.https://ifrnd.org/journal/index.php/jebsam2019Economic

The effect of global crises on stock market correlations : evidence from scalar regressions via functional data analysis

This paper presents a novel, mixed-frequency based regression approach, derived from functional data analysis (FDA), to analyze the effect of global crises on stock market correlations, using a long span of data, dating as far back as early 1800s, thus covering a wide range of global crises that have not yet been examined in the literature in this context. Focusing on the advanced nations in the G7 group, we observe heterogeneous effects of global crises on the convergence patterns across developed stock markets. While the post World War II period experienced a general rise in the level of correlations among developed stock market returns, we find that global crises in general have led to a stronger association of stock market returns in the US, UK and Canada, whereas the opposite holds when it comes to how European and Japanese stock markets co-move with the US. Overall, our results suggest that crises that are global in nature generally contribute to the convergence of global stock markets, while the effect largely depends on the context and nature of the crises that possibly drive the perception of risk and/or contagion in financial markets. From an investment perspective, our findings suggest that, in the wake of global crises, diversification benefits will be limited by moving funds across the US and UK stock markets whereas possible diversification benefits would have been possible during the crises-ridden period of the early twentieth century by holding positions in equities in the remaining G7 nations to supplement positions in the US. However, these diversification benefits seem to have frittered away in the post World War II period, highlighting the role of emerging markets and alternative assets to improve diversification benefits in the modern era.http://www.elsevier.com/locate/sced2020-09-01hj2019Business ManagementEconomic

Global risk exposures and industry diversification with Shariah-compliant equity sectors

This paper examines the risk exposures of ten major Islamic sector indexeswith respect to shocks in global conventional markets. Utilizing a dynamic three-regime, three-factor risk spillover model, we generally observe positive risk exposures of Islamic equity sectors with respect to developed market shocks. Consumer Services, Oil & Gas and Technology, however, are found to exhibit negative risk exposures during crash periods, implying possible safe haven benefits for global investors. Both in- and out-of-sample results suggest that the portfolios supplemented with positions in Islamic equity sectors yield much improved risk adjusted returns, implying significant international diversification benefits. Financials, Healthcare, Telecommunication, and Utilities particularly stand out with relatively higher weights allocated in the optimal portfolios, implying the significance of these Islamic sectors in global diversification strategies.http://www.elsevier.com/locate/pacfin2016-11-30hb201

The predictive power of industrial electricity usage revisited : evidence from non‐parametric causality tests

Recent research shows that the industrial electricity usage growth rate carries predictive ability over stock market returns up to 1 year. Using the recently developed non‐parametric causality tests we show that the predictive power of industrial electricity usage can be explained by an ‘industry effect’ that is transmitted via the volatility channel. We argue that the countercyclical premium associated with industrial electricity usage growth is driven by the industry components that drive stock reversals, thus resulting in the negative relationship between today's industrial electricity usage and stock market returns in the future. The findings are in line with the notion that the returns on industry portfolios are informative about macroeconomic fundamentals and suggest that the informational value of industrial electricity usage as a business cycle variable may be an artefact of return reversals driven by past industry performance.https://onlinelibrary.wiley.com/journal/175302372020-06-01hj2019Economic

Does liquidity risk explain the time-variation in asset correlations? Evidence from stocks, bonds and commodities

The time-variation in asset correlations have broad implications in asset pricing, portfolio management and hedging. Numerous studies in the literature have found that the change in correlations is mainly related to the magnitude of market movements, hence volatility. However, recent research finds that the size of markets fluctuations is not necessarily the primary driver for the time-variation in correlations, but that the effect of market movements is amplified in times of high financial distress, characterised by low liquidity. This paper seeks to investigate the effect of liquidity on time-varying correlations among different asset classes, namely stocks, corporate bonds and commodities. Our findings show that liquidity indeed has a significant effect on the time-variation in asset correlations, particularly in the case of how bond returns co-move with other asset classes. We observe that higher liquidity risk is associated with lower correlation of bond returns with stocks as well as commodities. Our findings suggest that measures of liquidity risk can improve models of correlations; and potentially help improve the effectiveness of risk management strategies during periods of financial distress.https://ifrnd.org/journal/index.php/jebsam2019Economic

A note on uncertainty due to infectious diseases and output growth of the United States : a mixed-frequency forecasting experiment

Utilizing a mixed data sampling (MIDAS) approach, we show that a daily newspaper-based index of uncertainty associated with infectious diseases can be used to predict, both in- and out-of-samples, low-frequency movements of output growth for the United States (US). The predictability of monthly industrial production growth and quarterly real Gross Domestic Product (GDP) growth during the current period of heightened economic uncertainty due to the COVID-19 pandemic is likely to be of tremendous value to policymakers.http://www.worldscientific.com/worldscinet/afehj2023Economic

Oil price uncertainty shocks and global equity markets : evidence from a GVAR model

This paper examines the propagation of oil price uncertainty shocks to real equity prices using a large-scale Global Vector Autoregressive (GVAR) model of 26 advanced and emerging stock markets. The GVAR framework allows us to capture the transmission of local and global shocks, while simultaneously accounting for individual-country peculiarities. Utilising a recently developed model-free, robust estimate of oil price uncertainty, we document a statistically significant and negative effect of uncertainty shocks emanating from oil prices on the large majority of global stock markets, with the adverse effect of oil price uncertainty shocks found to be stronger for emerging economies as well as net oil-exporting nations. Interestingly, however, global stock markets exhibit a great deal of heterogeneity in their recovery following oil uncertainty shocks as some experience rapid corrections in stock valuations while others suffer from extended slumps. While the results are sensitive to the oil uncertainty measure utilised, they suggest that country diversification in the face of rising oil market uncertainty can still be beneficial for global investors as global stock markets exhibit a rather heterogeneous pattern in their recovery rates against oil market shocks.DATA AVAILABILITY STATEMENT : The GVAR data used for this study can be obtained from https://sites. google.com/site/gvarmodelling/data, accessed on 17 July 2022. Data for the oil uncertainty index which is not captured in the GVAR dataset can be obtained from https://sites.google.com/site/ nguyenhoaibao/oil-market-uncertainty?authuser=0, accessed on 17 July 2022.https://www.mdpi.com/journal/jrfmEconomic