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We investigate the dynamic properties of the realized volatility of five agricultural commodity futures by employing the high‐frequency data from Chinese markets and find that the realized volatility exhibits both long memory and regime switching. To capture these properties simultaneously, we utilize a Markov switching autoregressive fractionally integrated moving average (MS‐ARFIMA) model to forecast the realized volatility by combining the long memory process with regime switching component, and compare its forecast performances with the competing models at various horizons. The full‐sample estimation results show that the dynamics of the realized volatility of agricultural commodity futures are characterized by two levels of long memory: one associated with the low‐volatility regime and the other with the high‐volatility regime, and the probability to stay in the low‐volatility regime is higher than that in the high‐volatility regime. The out‐of‐sample volatility forecast results show that the combination of long memory with switching regimes improves the performance of realized volatility forecast, and the proposed model represents a superior out‐of‐sample realized volatility forecast to the competing models. Copyright © 2016 John Wiley & Sons, Ltd.

We investigate the realized volatility forecast of stock indices under the structural breaks. We utilize a pure multiple mean break model to identify the possibility of structural breaks in the daily realized volatility series by employing the intraday high-frequency data of the Shanghai Stock Exchange Composite Index and the five sectoral stock indices in Chinese stock markets for the period 4 January 2000 to 30 December 2011. We then conduct both in-sample tests and out-of-sample forecasts to examine the effects of structural breaks on the performance of ARFIMAX-FIGARCH models for the realized volatility forecast by utilizing a variety of estimation window sizes designed to accommodate potential structural breaks. The results of the in-sample tests show that there are multiple breaks in all realized volatility series. The results of the out-of-sample point forecasts indicate that the combination forecasts with time-varying weights across individual forecast models estimated with different estimation windows perform well. In particular, nonlinear combination forecasts with the weights chosen based on a non-parametric kernel regression and linear combination forecasts with the weights chosen based on the non-negative restricted least squares and Schwarz information criterion appear to be the most accurate methods in point forecasting for realized volatility under structural breaks. We also conduct an interval forecast of the realized volatility for the combination approaches, and find that the interval forecast for nonlinear combination approaches with the weights chosen according to a non-parametric kernel regression performs best among the competing models.

This study investigates the relationship between corporate governance and corporate fraud by utilizing logit regression and by employing a sample of 176 firms listed in Chinese stock markets during the period from 2001 to 2005. The results s reveal that: (1) the proportion of independent members in board of directors is lower for firms experiencing corporate fraud than for no-fraud firms; (2) the firms with CEOs being the chairmen of board of directors are more likely to commit corporate fraud than the other firms; (3) the financial incentives to executives are greater for firms experiencing corporate fraud than for no-fraud firms; (4) capital structure has significant and positive effect on corporate fraud in China

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