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This paper employs an ARJI-trend model that combines the autoregressive jump intensity (ARJI) and component models to analyze the effects of the U.S. dollar index and oil prices on the dynamic properties of biofuel-related commodity futures. The results show that the ARJI-trend model not only provides a better fit for the data on the volatility dynamics of corn, soybean, and wheat futures, but also performs better in terms of out-of-sample forecasting. The U.S. dollar index and oil prices both generate significant impacts on the returns of the futures. Since the coexistence of permanent component, transitory component, and time-varying jumps are observed in those futures, the ARJI-trend model is beneficial for acquiring a better understanding of the differential attributes among corn, soybean, and wheat futures.

This paper applies the Markov-switching model to analyse the transition probabilities and generalized method of moments (GMM) with Newey–West heteroscedasticity and autocorrelation consistent covar...

Motivated by the recent literature on cryptocurrency volatility dynamics, this paper adopts the ARJI, GARCH, EGARCH, and CGARCH models to explore their capabilities to make out-of-sample volatility...

This research adopts an autoregressive conditional jump intensity (ARJI) model by utilizing intraday data of overlapping trading hours to analyze the global contagion effects of sentimental responses and volatility dynamics through the volatility indices of the U.S. and three major European countries. The results show strong evidence of contagion effects among the volatility indices. For regional effects, compared to the other two European indices (VFTSE and VCAC), the volatility index of Germany (VDAX) generates greater impacts on the other indices, and we also observe bi-directional causalities among these three countries. The findings support the meteor shower hypothesis among the sample countries. Additionally, jump innovations in Germany affect investor sentiments with the fastest convergence speed. Finally, the jump convergence speed in the UK is slower, and the impacts of unexpected news could also last longer for UK investors.

This study applies dynamic generalized method of moments estimation to examine the influences of ownership structure and board characteristics on default risk for a full samples and two subsamples (high-tech and conventional) of publicly listed firms in Taiwan. Our findings reveal that certain characteristics of corporate governance have explanatory power for default probability, but the impact is not straightforward. In particular, the impact of internal and external governance structures on default risk is industry dependent. Accordingly, governance proposals that encourage higher ownership among directors and large block shareholdings in high-tech firms or reduce managerial ownership in conventional companies can have a counterproductive effect on corporate governance and result in higher bankruptcy possibility.

In the aftermath of the sub-prime mortgage crisis, we set out to investigate the spillover effects of returns and volatility in the US stock market on the stock markets of Brazil, Russia, India, China and Vietnam (BRICVs). The results of our application of the ARJI (autoregressive conditional jump intensity) model reveal that the greatest contagious effects of returns and volatility from the US market before the crisis were felt by Russia; however, following the crisis, the most intense spillover effects are found to be on Vietnam. While India, the most efficient of these markets, demonstrates the lowest total long-run risk, an inverse situation is discernible for both China and Brazil. Our results therefore suggest that in the design of their asset allocation strategies, investors with risk aversion should consider investing greater proportions of their funds in India, while being conservative in both Brazil and China to avoid inefficient and risky investment.

This paper employs the autoregressive conditional jump intensity model, incorporating a forecast error, to investigate the relationships between the changes in the implied volatility and the relevant determinants in the Taiwan market. We further apply the orthogonality test to explore forecast error and content of information. The empirical results show that the changes in the implied volatility are affected by the contemporaneous returns, lagged returns, lagged changes in the implied volatility, contemporaneous daily changes in the realized volatility and lagged forecast error. At the same time, these results are consistent with the behavioral explanations of Hibbert et al. [Journal of Banking and Finance (2008) vol. 32, pp. 2254–2266]. We also demonstrate that the implied volatility in Taiwan seems to be an efficient forecast of future realized volatility during the whole of the sample period, except for the financial crisis period.

We propose an ARJI-Trend model—a combination of the ARJI and component models—to capture the distinguishing features of US index returns, with the results indicating that our model has a good fit for the volatility dynamics of spot, floor- traded and E-mini index futures in US markets. Although certain analogous characteristics are discernible amongst the three indices (such as the responses by the transitory components to innovations, the high persistence in the trends, and the relative importance of jump variance), the reaction to news is found to be heterogeneous amongst the S&P 500 indices. Furthermore, the out-of-sample forecasting performances of both the ARJI-Trend model and the GARCH model are found to have general equivalence for the S&P 500 indices. Our analyses further show that the mini-sized index market is the most efficient with regard to the transmission of information in both the short and long run. This suggests that, following the introduction of E-mini futures, these instruments have c...

We employ four various GARCH-type models, incorporating the skewed generalized t (SGT) errors into those returns innovations exhibiting fat-tails, leptokurtosis and skewness to forecast both volatility and value-at-risk (VaR) for Standard & Poor's Depositary Receipts (SPDRs) from 2002 to 2008. Empirical results indicate that the asymmetric EGARCH model is the most preferable according to purely statistical loss functions. However, the mean mixed error criterion suggests that the EGARCH model facilitates option buyers for improving their trading position performance, while option sellers tend to favor the IGARCH/EGARCH model at shorter/longer trading horizon. For VaR calculations, although these GARCH-type models are likely to over-predict SPDRs' volatility, they are, nevertheless, capable of providing adequate VaR forecasts. Thus, a GARCH genre of model with SGT errors remains a useful technique for measuring and managing potential losses on SPDRs under a turbulent market scenario.

This paper uses the traditional variance ratio test of Lo and MacKinlay (1988, 1989), the non-parametric-based variance ratio test of Wright (2000) and the multiple-variance ratio test of Chow and Denning (1993), to re-examine the validity of the weak form efficient market hypothesis for foreign exchange markets in four floating-rate markets in neighboring Asian economies (Japan, South Korea, Taiwan and the Philippines). The results show that the random walk patterns of the exchange rate return series cannot be rejected, with the one exception of Taiwan, where inefficiency is shown to be most prominent. We therefore conclude that the foreign exchange markets of Japan, South Korea and the Philippines are weak form efficient, while the foreign exchange market of Taiwan is inefficient.

This study applies the ARJI-trend model in conjunction with the procedure proposed by Bai and Perron (2003) to investigate the coexistence of permanent and transitory components and time-varying jumps in the A and B stock market indices of the Shanghai and Shenzhen Stock Exchanges. Although the response to outside innovations is greater within the transitory component, it is short-lived; conversely, though there is a high level of persistence in the trend, new information has only a lesser effect on the permanent component. Jump variance can also affect total variance, though the effect is far lower than the variance for generalized autoregressive conditional heteroskedasticity. Accordingly, the market risk appears small. The reaction to news is heterogeneous within the Shanghai and Shenzhen indices; this may be the result of various market characteristics. During event periods, the permanent component, transitory components, and jump intensity are larger than their averages. After an upward trend, market...

This study employs a bivariate GARCH model to examine the dynamic relationships between two gold futures markets (COMEX and TOCOM) before and during gold's recent uptrend of the past few years. Results show that the performance of COMEX is better than TOCOM. However, TOCOM leads COMEX in the mean return. Volatility transmission effects exist in both COMEX and TOCOM. While the responses to good news and bad news are symmetrical in TOCOM, they are asymmetric in COMEX.

This article sets out to investigate if the TAIFEX has adequate clearing margin adjustment system via unconditional coverage, conditional coverage test and mean relative scaled bias to assess the performance of three value-at-risk (VaR) models (i.e., the TAIFEX, RiskMetrics and GARCH-t). For the same model, original and absolute returns are compared to explore which can accurately capture the true risk. For the same return, daily and tiered adjustment methods are examined to evaluate which corresponds to risk best. The results indicate that the clearing margin adjustment of the TAIFEX cannot reflect true risks. The adjustment rules, including the use of absolute return and tiered adjustment of the clearing margin, have distorted VaR-based margin requirements. Besides, the results suggest that the TAIFEX should use original return to compute VaR and daily adjustment system to set clearing margin. This approach would improve the funds operation efficiency and the liquidity of the futures markets.

This paper applies the GARJI model to investigate the impact of news on the S&P 500 spot and index futures. We show their reactions are dissimilar to good or bad news. Hence, though they are like brothers, they are cousins. Besides, the persistence and sensitivity parameters for the arrival of jump events are quite high and significant. It means a high probability of many jumps today seems to be followed by a high probability of many jumps tomorrow. We suggest it is necessary to consider the time series dynamics in the jump size distribution when studying the impact of news on financial markets.

This article adopt bivariate GARCH model with TAR to investigate the extent of volatility as well as return transmission between S&P 500 (NASDAQ 100) and VIX (VXN) since their introduction of VIX and VXN. Results show that the performance of VIX index is the best among the four indices during the whole sample period. But the volatility of VIX is also higher than other index. Further, only lagged negative return (change) has a bidirectional casual effect in the low-fear regime for the SP500/VIX series. The results also indicate that VIX index market has a stronger pricing effect on SP500. However, there is no obvious lead-lag relationship between NASDAQ100 and VXN index. Moreover, the return and volatility responses to high-fear and low-fear gauge are asymmetrical.