Dynamic Conditional Bias-Adjusted Carry Cost Rate Futures Hedge Ratios

This paper proposes new dynamic conditional futures hedge ratios and compares their hedging performances along with those of common benchmark hedge ratios across three broad asset classes. Three of the hedge ratios are based on the upward-biased carry cost rate hedge ratio, where each is augmented in a different bias-mitigating way. The carry cost rate hedge ratio augmented with the dynamic conditional correlation between spot and futures price changes generally: (1) provides the highest hedging effectiveness and (2) has a statistically significantly higher hedging effectiveness than the other hedge ratios across assets, sub-periods, and rolling window sizes

Carry Cost Rate Regimes and Futures Hedge Ratio Variation

This paper tests whether the traditional futures hedge ratio (hT) and the carry cost rate futures hedge ratio (hc) vary in accordance with the Sercu and Wu (2000) and Leistikow et al. (2019) “hc” theory. It does so, both within and across high and low spot asset carry cost rate (c) regimes. The high and low c regimes are specified by asset across time and across currency denominations. The findings are consistent with the theory. Within and across c regimes, hT is inefficient and hc is biased. Across c regimes, hc’s Bias Adjustment Multiplier (BAM) does not vary significantly. Even though hc’s bias-adjusted variant’s BAM is restricted to old data that is from a different c regime, the hedging performance of hc and its bias-adjusted variant (=hc × BAM), are superior to that for hT. Variation in c may account for the hT variation noted in the literature and variation in c should be incorporated into ex ante hedge ratios

Abnormal Stock Returns, for the Event Firm and Its Rivals, Following the Event Firm\u27s Large One-Day Stock Price Drop

Purpose – The purpose of this paper is to examine intra-industry contagion and the following apparent violations of the efficient market hypothesis around large one-day price decline events in individual stocks. Design/methodology/approach – The paper examines daily stock returns around one-day price declines of 10 percent or more for event stocks and their rivals. Using techniques similar to those used in Bremer and Sweeney and Cox and Peterson, the paper includes event stocks whose prices are at least $10 per share prior to the event to reduce the possible price reversal induced by bid-ask price bounce. As is typical for the literature, the stock daily abnormal return (AR) is calculated as the difference between the actual daily stock return and the estimated stock return based on the market model estimated over a 200-trading-day pre-event period lsqb-220, -21]. Cumulative abnormal returns (CARs) for each stock are formed by aggregating the individual daily stock ARs. Denoting the large price decline event day as day 0, we examine the ARs of 41 trading days lsqb-20,+20], the CARs for the lsqb+1,+3] period, and the CARs for the lsqb+4,+20] period. Cross-sectional average ARs and CARs are calculated and tested for statistical significance. Furthermore, the paper examines whether the post-event abnormal stock returns for the event firm and its rivals can be explained by prior event firm and industry variables. Findings – On average, after an event, the event stock experiences a positive three-day AR (S&P 600 stocks) followed by a 17-day negative AR (both S&P 500 and 600 stocks). Moreover, for that 17-day period: the rivals\u27 stocks outperform the event firms\u27 stocks and the event firms\u27 returns are statistically significantly related to prior variables. The paper also finds statistically significant relationships between the prior variables and the rivals\u27 post-event stock returns. It provides an intra-industry effects explanation for these results. Originality/value – The paper offers insights into abnormal stock returns, for the event firm and its rivals, following the event firm\u27s large one-day stock price drop

Abnormal stock returns, for the event firm and its rivals, following the event firm's large one-day stock price drop

Purpose – The purpose of this paper is to examine intra-industry contagion and the following apparent violations of the efficient market hypothesis around large one-day price decline events in individual stocks. Design/methodology/approach – The paper examines daily stock returns around one-day price declines of 10 percent or more for event stocks and their rivals. Using techniques similar to those used in Bremer and Sweeney and Cox and Peterson, the paper includes event stocks whose prices are at least $10 per share prior to the event to reduce the possible price reversal induced by bid-ask price bounce. As is typical for the literature, the stock daily abnormal return (AR) is calculated as the difference between the actual daily stock return and the estimated stock return based on the market model estimated over a 200-trading-day pre-event period [-220, -21]. Cumulative abnormal returns (CARs) for each stock are formed by aggregating the individual daily stock ARs. Denoting the large price decline event day as day 0, we examine the ARs of 41 trading days [-20,+20], the CARs for the [+1,+3] period, and the CARs for the [+4,+20] period. Cross-sectional average ARs and CARs are calculated and tested for statistical significance. Furthermore, the paper examines whether the post-event abnormal stock returns for the event firm and its rivals can be explained by prior event firm and industry variables. Findings – On average, after an event, the event stock experiences a positive three-day AR (S&P 600 stocks) followed by a 17-day negative AR (both S&P 500 and 600 stocks). Moreover, for that 17-day period: the rivals' stocks outperform the event firms' stocks and the event firms' returns are statistically significantly related to prior variables. The paper also finds statistically significant relationships between the prior variables and the rivals' post-event stock returns. It provides an intra-industry effects explanation for these results. Originality/value – The paper offers insights into abnormal stock returns, for the event firm and its rivals, following the event firm's large one-day stock price drop.Finance, Financial markets, Financial performance, Stock returns

Carry Cost Rate Regimes and Futures Hedge Ratio Variation

This paper tests whether the traditional futures hedge ratio (hT) and the carry cost rate futures hedge ratio (hc) vary in accordance with the Sercu and Wu (2000) and Leistikow et al. (2019) “hc” theory. It does so, both within and across high and low spot asset carry cost rate (c) regimes. The high and low c regimes are specified by asset across time and across currency denominations. The findings are consistent with the theory. Within and across c regimes, hT is inefficient and hc is biased. Across c regimes, hc’s Bias Adjustment Multiplier (BAM) does not vary significantly. Even though hc’s bias-adjusted variant’s BAM is restricted to old data that is from a different c regime, the hedging performance of hc and its bias-adjusted variant (=hc × BAM), are superior to that for hT. Variation in c may account for the hT variation noted in the literature and variation in c should be incorporated into ex ante hedge ratios