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Abstract: The classical volatility models, such as GARCH, are return-based models, which are constructed with the data of closing prices. It might neglect the important intraday information of the price movement, and will lead to loss of information and efficiency. This study introduces and extends the range-based autoregressive volatility model to make up for these weaknesses. The empirical results consistently show that the new model successfully captures the dynamics of the volatility and gains good performance relative to GARCH model. [Copyright &y& Elsevier]

• Site dependency and distribution heterogeneity of wind speed data is considered. • Generalized Gaussian process models with positive domain likelihoods are proposed. • A statistical model is selected to extract the linear feature from the wind speed. • A pseudo-likelihood with cross validation is applied to improve models' robustness. • Empirical results statistically verify the performance of our proposed models. As the proportion of the total installed wind capacity continues to increase, precise probabilistic and deterministic wind speed forecasting becoming increasingly significant for wind turbine stable function and operation management. It is worth noting that except for the intermittent nature of wind speed itself, the site dependence of wind energy and the heterogeneous nature of the distribution at different locations can also affect the results of wind speed forecasting. Considering this has often been overlooked in previous studies, this study attempts to introduce characteristic information of wind from wind farms into wind speed probabilistic forecasting. For the sake of acquiring a more rational descriptive statistical interpretation of wind speed, a variety of probability density functions for actual wind speed data are evaluated in this study. And then, Generalized Gaussian Process models are constructed via observation likelihood functions which are determined by obtained probability density functions. Beyond that, to enhance the robustness against model misspecification, a pseudo-likelihood for the Leave-One-Out cross-validation methodology is proposed for approximating the posterior in the Bayesian inference stage. Wind speed forecasts on 6-hour-ahead horizons from two wind farms in China illustrated that the appropriate mining of wind prior statistical characteristic information can contribute to improving the precision of probabilistic wind speed forecasting. [ABSTRACT FROM AUTHOR]

Abstract: This study employs a parametric approach based on TGARCH and GARCH models to estimate the VaR of the copper futures market and spot market in China. Considering the short selling mechanism in the futures market, the paper introduces two new notions: upside VaR and extreme upside risk spillover. And downside VaR and upside VaR are examined by using the above approach. Also, we use Kupiec’s [P.H. Kupiec, Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives 3 (1995) 73–84] backtest to test the power of our approaches. In addition, we investigate information spillover effects between the futures market and the spot market by employing a linear Granger causality test, and Granger causality tests in mean, volatility and risk respectively. Moreover, we also investigate the relationship between the futures market and the spot market by using a test based on a kernel function. Empirical results indicate that there exist significant two-way spillovers between the futures market and the spot market, and the spillovers from the futures market to the spot market are much more striking. [Copyright &y& Elsevier]