Journal of Futures Markets最新文献

Using option order imbalance as a proxy for market makers' inventory pressure, we identify a distinct return reversal pattern between option trading activity and underlying asset returns. Specifically, call (put) order imbalances are contemporaneously positively (negatively) associated with underlying returns, followed by rapid reversals in the subsequent period. This reversal stems primarily from temporary price pressures caused by market makers' delta-hedging activities and remains robust after controlling for multiple factors and across various option classifications. Two empirical scenarios reinforce that option order imbalance reflects market makers' inventory risks rather than informed trading. Additional analyses—including event studies, out-of-sample tests, examinations of dynamic hedging behavior, and panel regressions with expanded SSE-listed exchange traded fund option data—further substantiate these findings. Overall, our results emphasize the critical role of market makers as a noninformational trading channel, significantly shaping the relationship between option trading and underlying asset prices.

This study examines the role of after-hours information in forecasting Chinese commodity futures volatility, exploiting the introduction of a night session that potentially facilitates real-time responses to information originating in overseas markets. We generate timely forecasts on future daytime realized volatility for 10 commodity futures, using heterogeneous autoregressive (HAR) models augmented with and without past nights' realized variance measures. Our results reveal significant predictive power, both in-sample and out-of-sample, associated with the night-time realized volatility across markets. In contrast, the inclusion of daily squared overnight returns as an alternative measure provides limited improvements. Furthermore, we document the empirical merit of separately considering the jump and continuous components in the night-session price variation, with its superior performance being most pronounced over long forecasting horizons. The improved statistical accuracy is also shown to be economically meaningful for a risk-averse investor and remains robust to changes in the identification, estimation, and forecasting procedure.

This article investigates how the “T+1” trading rule affects put-call disparity (PCD) in the Chinese market. The Chinese stock market is subject to the unique “T+1” trading rule, while the options market is not. This provides a valuable opportunity to study the impact of the selling constraint imposed by this rule. We construct synthetic ETFs and measure the levels of PCDs. PCDs of all underlying ETFs are significantly higher at the market's open than at its close and exhibit a downward intraday trend. Moreover, the differences in PCDs between the market's open and close are highly correlated with the underlying ETFs' volatility, speculative trading activity, and limits to arbitrage. We further confirm our results by analyzing overnight and intraday returns of the synthetic and underlying ETFs, as well as the bases of stock index futures. We highlight the significant influence of trading mechanisms on asset price formation.

We examine the performance of trend-based strategies in Chinese commodity futures markets, using a data set of 64 commodity futures from 2003 to 2023. We find that TSMOM strategies generate high returns. The performance is not driven by specific sample periods or individual commodity futures. TSMOM factors also exhibit strong explanatory power for other commodity portfolios, including newly developed trend-based strategies. Decomposition analysis reveals key drivers, including the autocovariance of commodity returns, independent predictive effects, the distribution of volatility-managed weights, and a non-positive risk-return relationship. We develop four enhanced strategies to improve returns and risk control. All strategies remain robustly profitable after accounting for transaction costs.

In the context of China–US trade friction, we use the TVP-VAR, SHAP, DECO, and CDB model to test the spillover volatility, hedging, safe haven, and portfolio returns of cryptocurrencies based on daily data of Bitcoin, Ethereum, Litecoin, Ripple, CSI300 Index, Shanghai Composite Index, S&P500 Index, and Nasdaq Index. The results show a significant short-term time-varying asymmetric volatility spillover effect between cryptocurrencies and the US and Chinese stock markets. Cryptocurrencies can be used as short-term hedging assets for the Chinese stock market. The evidence also shows no long-term correlation between cryptocurrencies and the stock market. Therefore, in periods of volatility caused by trade friction between the two countries, such as the imposition of high tariffs, investors can regard cryptocurrencies as short-term hedging assets and long-term safe haven assets to mitigate losses caused by stock market fluctuations. In addition, adding cryptocurrencies to stock index portfolios can significantly diversify risks and increase returns.

We propose a Markov-switching GARCH framework to describe the VIX series. Unlike previous studies on derivatives pricing, both the conditional mean and conditional variance here are allowed to vary with the market state described by a hidden Markov chain process. This switching framework preserves the good properties, under which we derive the analytical pricing formula for the VIX futures. In addition, to make it feasible in practice, we design a novel analytical algorithm that can efficiently filter out all the unobserved variables in the model. The empirical studies show that adding the Markov-switching terms can significantly improve the fit of the underlying VIX series and the pricing performance of the VIX futures both in- and out-of-sample. Our research highlights the importance of incorporating regime switches that can be viewed as an additional risk factor into the model framework.

To track real economic activity in a timely manner, we construct a Narrative Daily Economic Condition Index. The economic narratives from the full text content of one million Wall Street Journal articles are decomposed into interpretable topic time series. The resulting indicator captures the business cycle and the movement of economic output measures well. The empirical applications show that the resulting indicator captures real-time economic fluctuations and contributes to predicting market volatility and interest rates, which can significantly benefit real-time policy making and investment decisions. Our results highlight the informativeness and timeliness of economic news for assessing the state of the economy.

We examine whether merger announcements convey information relevant to rival firms' return volatility, in addition to that about returns. We find that the changes in option implied volatility of the merger targets around deal announcement are positively and significantly related to the changes in option implied volatility of their industry rivals, consistent with the transfer of volatility information. Our results are obtained after controlling for the transfer of return information, contemporaneous market-wide volatility, as well as other volatility determinants. We also find that the magnitude of volatility information transfer is significantly stronger for those rivals (i) identified ex ante by the market as most likely to be subsequently acquired, (ii) that subsequently receive a takeover bid, or (iii) that have a financial profile that is similar to that of the merger targets. We interpret the evidence to be consistent with the acquisition probability hypothesis.