Predictive ability and economic gains from volatility forecast combinations

The availability of numerous modeling approaches for volatility forecasting leads to model uncertainty for both researchers and practitioners. A large number of studies provide evidence in favor of combination methods for forecasting a variety of financial variables, but most of them are implemented on returns forecasting and evaluate their performance based solely on statistical evaluation criteria. In this paper, we combine various volatility forecasts based on different combination schemes and evaluate their performance in forecasting the volatility of the S&P 500 index. We use an exhaustive variety of combination methods to forecast volatility, ranging from simple techniques to time-varying techniques based on the past performance of the single models and regression techniques. We then evaluate the forecasting performance of single and combination volatility forecasts based on both statistical and economic loss functions. The empirical analysis in this paper yields an important conclusion. Although combination forecasts based on more complex methods perform better than the simple combinations and single models, there is no dominant combination technique that outperforms the rest in both statistical and economic terms.     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.     

Forecasting of dependence,market, and investment risks of a global index portfolio

This paper undertakes an in-sample and rolling-window comparative analysis of dependence, market, and portfolio investment risks on a 10-year global index portfolio of developed, emerging, and commodity markets. We draw our empirical results by fitting vine copulas (e.g., r-vines, c-vines, d-vines), IGARCH(1,1) RiskMetrics value-at-risk (VaR), and portfolio optimization methods based on risk measures such as the variance, conditional value-at-risk, conditional drawdown-at-risk, minimizing regret (Minimax), and mean absolute deviation. The empirical results indicate that all international indices tend to correlate strongly in the negative tail of the return distribution; however, emerging markets, relative to developed and commodity markets, exhibit greater dependence, market, and portfolio investment risks. The portfolio optimization shows a clear preference towards the gold commodity for investment, while Japan and Canada are found to have the highest and lowest market risk, respectively. The vine copula analysis identifies symmetry in the dependence dynamics of the global index portfolio modeled. Large VaR diversification benefits are produced at the 95% and 99% confidence levels by the modeled international index portfolio. The empirical results may appeal to international portfolio investors and risk managers for advanced portfolio management, hedging, and risk forecasting.     

Can online search data improve the forecast accuracy of pork price in China?

Online search data provide us with a new perspective for quantifying public concern about animal diseases, which can be regarded as a major external shock to price fluctuations. We propose a modeling framework for pork price forecasting that incorporates online search data with support vector regression model. This novel framework involves three main steps: that is, formulation of the animal diseases composite indexes (ADCIs) based on online search data; forecast with the original ADCIs; and forecast improvement with the decomposed ADCIs. Considering that there are some noises within the online search data, four decomposition techniques are introduced: that is, wavelet decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and singular spectrum analysis. The experimental study confirms the superiority of the proposed framework, which improves both the level and directional prediction accuracy. With the SSA method, the noise within the online search data can be removed and the performance of the optimal model is further enhanced. Owing to the long-term effect of diseases outbreak on price volatility, these improvements are more prominent in the mid- and long-term forecast horizons.     

Priority and privilege in scientific discovery

The priority rule in science has been interpreted as a behavior regulator for the scientific community, which benefits society by adequately structuring the distribution of intellectual labor across pre-existing research programs. Further, it has been lauded as an intuitively fair way to reward scientists for their contributions, as a special case of society’s “grand reward scheme”. However, we will argue that the current formal framework utilized to model the priority rule idealizes away important aspects of credit attribution, and does so in a way that impacts the conclusions drawn regarding its function in scientific communities. In particular, we consider the social dynamics of credit attribution in order to show that the priority rule can foster structural disadvantages in socially diverse science, as well as drive the distribution of intellectual labor away from optimal.     

Processing and characterization of the microstructure and mechanical properties of Al6061–TiB2 composite

In the present research, aluminum metal matrix composites were processed by the stir casting technique. The effects of TiB2 rein-forcement particles, severe plastic deformation through accumulative roll bonding (ARB), and aging treatment on the microstructural charac-teristics and mechanical properties were also evaluated. Uniaxial tensile tests and microhardness measurements were conducted, and the micro-structural characteristics were investigated. Notably, the important problems associated with cast samples, including nonuniformity of the rein-forcement particles and high porosity content, were solved through the ARB process. At the initial stage, particle-free zones, as well as particle clusters, were observed on the microstructure of the composite. However, after the ARB process, fracturing phenomena occurred in brittle ceramic particles, followed by breaking down of the fragments into fine particles as the number of rolling cycles increased. Subsequently, com-posites with a uniform distribution of particles were produced. Moreover, the tensile strength and microhardness of the ARB-processed com-posites increased with the increase in the reinforcement mass fraction. However, their ductility exhibited a different trend. With post-deforma-tion aging treatment (T6), the mechanical properties of composites were improved because of the formation of fine Mg2Si precipitates.