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This study constructs a Chinese lottery index (LI) based on six popular lottery preference variables by using the partial least squares method and examines the relationship between the LI and future stock market returns during the period from January 2000 to December 2021. We find that the LI can negatively predict stock market excess returns in‐sample and out‐of‐sample. In addition, the LI can generate a large economic gain for a mean–variance investor. Finally, the predictive sources of the LI stem from a cash flow channel and can be explained by the positive volume–volatility relationship and investor attention. [ABSTRACT FROM AUTHOR]

We propose an industry volatility spillover index and find that this new predictor has an impressive in-sample and out-of-sample predictability of aggregate stock returns. A one-standard-deviation increase in this index leads to a 4.14% increase in the expected excess return over the next month. Furthermore, a mean-variance investor can realize sizable economic gains by using this spillover index in his/her asset allocation. The forecasting power of the spillover index remains significant after controlling for popular economic variables and newly proposed predictors. Due to the absence of learning, the spillover index shows increasing predictive ability over the recent period. The economic origins of the spillover index's success stem from both the cash flow and discount rate channels, as well as significant association with investor sentiment and tail risk. The return predictability of this index is pervasive across characteristic-sorted portfolios and is particularly strong for large-cap or difficult-to-value stocks. [ABSTRACT FROM AUTHOR]

Market skewness is an important indicator of market risk. We decompose market skewness into good and bad skewness and further study the relationship between various skewness and the stock market returns in China. Empirical results show that good skewness can significantly predict stock market returns in- and out-of-sample. Furthermore, compared to macroeconomic variables and variance variables, good skewness can provide complementary or dominant information. We also find that good skewness can provide helpful information in predicting stock market returns beyond what market skewness and bad skewness provide. A mean-variance investor can obtain sizable economic gains by using good skewness. The economic source of predictability is the cash flow channel. [ABSTRACT FROM AUTHOR]

This paper introduces an asymmetric robust weighted least squares (ARLS) approach to improve the forecasting performance of the heterogeneous autoregressive model for realized volatility. The ARLS approach down‐weights extreme observations to limit the bad influence of outliers on the estimated parameters. Compared with existing robust regression methods, our model further takes into account the asymmetry of outliers using a class of kernel functions. Out‐of‐sample results show the ARLS approach can generate more accurate forecasts of the S&P 500 index realized volatility in the statistical and economic senses. The model that considers the asymmetry of outliers gains superior performance among various robust regression competitors. The forecasting improvements also hold in other international stock markets. More importantly, the source of the predictive ability of the ARLS model comes from the less biased and more efficient parameter estimation. [ABSTRACT FROM AUTHOR]

This study investigates the lead–lag effects between product futures and raw material futures. Results show that returns on product futures lead returns on raw material futures: lagged product futures returns can significantly predict raw material futures returns in‐ and out‐of‐sample. This product‐material lead–lag effect is mainly driven by bad news and is a short‐term phenomenon. Moreover, returns on product futures, especially those based on bad news, can provide substantial economic gains to investments in raw material futures. The lead–lag effect is associated with frictions in the information diffusion from product futures markets to raw material futures markets and the slower response of raw material futures markets to common market information. [ABSTRACT FROM AUTHOR]

In this study, we provide the predictive linkage between global terrorist attacks and stock market volatility. We propose the predictive model by extending the prevailing heterogeneous autoregressive model for realized volatility (HAR-RV) with global terrorism and denote it as HAR-RV-GT. According to the Diebold – Mariano test and the model confidence set, we consistently find the superior forecasting performance delivered by the HAR-RV-GT model. For comprehensive empirical results, we extend the key finding to various settings including the consideration of popular jump and leverage effects, the use of more types of forecasting models, and the inclusion of long-horizon global terrorist attacks as well as the domestic terrorist attacks in the US. Additionally, the substantial economic gains based on a mean-variance investor confirm the valuable forecasting role of terrorist attacks. Our results are robust to a wide range of checks. [ABSTRACT FROM AUTHOR]

This study aims to improve the forecasting performance of technical indicators for crude oil prices by imposing economic constraints on the sign of the shrinkage estimators. The out-of-sample results indicate that our constrained methods deliver significantly stronger forecasts than their standard forms and prevalent predictive models. Moreover, the advantages of the constrained methods are stronger during recessions and weaker during expansions. The superior forecasting performance of the constrained methods is not affected by the consideration of long-horizon forecasts and is robust to a large body of alternative specifications. In addition to the statistical tests, we provide evidence that investors who use the new predictive framework can realize sizable economic gains through asset allocations and market timing. [ABSTRACT FROM AUTHOR]

Asset returns, especially negative returns, represent the leverage effect and are found to be informative for forecasting financial market volatility. The purpose of this paper is to dig out more useful information in Bitcoin returns when we predict Bitcoin volatility. We use the threshold regression model to differentiate positive and negative returns. The threshold regression results suggest that not only a decrease in normal returns but also an increase in extremely positive returns would lead to an increase in future Bitcoin volatility. To further capture and address the leverage effect of large positive returns in the Bitcoin market, we propose a model switching method. We empirically demonstrate the significant predictive ability of large positive returns when forecasting Bitcoin volatility both in‐ and out‐of‐sample. And, our findings still hold after considering long‐horizon forecasts, additional leverage effects, jump components, and a wide series of extensions and robustness tests. In an asset allocation exercise, the proposed volatility forecasting models, which highlight the predictive ability of large positive returns, can generate larger economic gains. [ABSTRACT FROM AUTHOR]

In this study, we propose a new family of the heterogeneous autoregressive realized volatility (HAR‐RV) models by considering truncated methods for predicting the RV in China's stock market. By adopting three types of critical values to recognize extremely large values of RV, we show that the modified models are simple but efficient to consistently deliver stronger in‐sample and out‐of‐sample forecasting performances than those of existing methods. Models that take truncated approaches into account can generate substantial economic gains in applications. We further provide evidence that the superiority of our proposed models is derived from the reduced variance of the measurement errors during days including truncated RVs. Additionally, the improved performances of the modified models still hold after considering the effects of jump components and leverage, as well as a wide range of extensions and robustness analyses. [ABSTRACT FROM AUTHOR]