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The purpose of this study is to investigate hedging effectiveness of KOSPI200 index futures and options using three measures proposed by Fishburn (1977), Ederington (1979), and Howard and D’Antonio (1987). The comparison of hedging effectiveness is conducted based on the market prices of KOSPI200 index futures and options traded in Korea Exchange (KRX) between January of 2001 and January of 2011, during which bootstrapping method is utilized to make a dataset of 100,000 random samples with holding period of 1, 3, 6, and 12 months, respectively. We examine the hedging performance of hedge portfolios made of short futures, protective puts and covered calls respectively based on three hedging effectiveness measures.

One of our finding is that short futures hedging is better than options in minimizing total volatility risk as well as down-side risk, which is consistent to the previous researches. Also futures hedging is more effective in reducing the VaR than the others. Secondly, the optimal hedge ratios of futures in minimizing total risk and down-side risk are turned out to be 0.97~0.98 and 0.94~0.95 respectively. Third, OTM short call hedge is the best hedging instrument when hedgers would like to maximize the Sharpe ratio. Finally, protective put hedging strategy is in general inferior to the short futures and covered call hedge based on three hedging effectiveness measures.

This paper aims to investigate the safe haven feature of Bitcoin, gold and two gold-backed cryptocurrencies (DGX and PAXG) against energy and agricultural commodities (crude oil, natural gas and wheat) during the COVID-19 pandemic, the Russia–Ukraine conflict and the Silicon Valley Bank (SVB) collapse.

The authors use the threshold GARCH (T-GARCH)-asymmetric dynamic conditional correlation (ADCC) model to evaluate the asymmetric dynamic conditional correlation between the return series and compare the diversifying, hedging and safe-haven ability of Bitcoin, gold and the two gold-backed cryptocurrencies (DGX and PAXG) against financial swings in the commodity market during the COVID-19 outbreak, the Russian–Ukrainian military conflict and SVB collapse. The authors also calculate the hedging ratios (HR) and hedging effectiveness index (HE). The authors finally use the wavelet coherence (WC) approach to check our results’ robustness and further investigate the impact of the three crises on the relationship between Bitcoin, gold gold-backed cryptocurrencies and commodities.

The results show that PAXG serves as a strong hedging instrument while gold, Bitcoin and DGX act as strong diversifiers during normal times. During crises, gold outperforms Bitcoin as a diversifier and a safe haven against commodities. Gold-backed cryptocurrencies also exhibit strong performance as diversifiers and safe havens. HR results indicate that Bitcoin and DGX are more cost-effective for commodities risk mitigation than gold and PAXG. In terms of hedging effectiveness, gold and PAXG emerge as the best hedging instruments for commodities, while DGX is considered the worst one. Bitcoin shows superior hedging against oil compared to wheat and gas risks. Moreover, the results of the WC approach confirm those of the T-GARCH-ADCC results in both the short and long run.

This paper provides a comprehensive analysis of the diversification ability of gold, Bitcoin and gold-backed cryptocurrencies during different crises (the COVID-19 pandemic, the Russia–Ukraine conflict and the SVB collapse). By taking into consideration gold-backed cryptocurrencies, the authors expand the understanding of safe havens beyond conventional assets.

In the past, life insurance companies were mainly exposed to mortality risk, a risk they in principle could diversify by issuing a large number of similar and statistically independent policies. However, as more exotic life insurance policies have been offered, such as unit‐linked life insurance contracts and policies with bonus mechanisms and minimum rate of return guarantees, life insurance companies have also become exposed to financial risk. The financial risk is non‐diver‐sifiable and is likely to affect many, if not most, of the companies outstanding policies in the same direction. Although it is non‐diversifiable, the financial risk is (at least to some extent) hedgeable. Since the accumulated exposure to financial risk over all the policies issued by a life insurance company can be large, it is important that this risk be hedged so that the company is able to meet its obligations to the policyholders.

This study empirically compares the hedging performances of the newly listed Japanese yen (JPY) and European euro (EUR) currency futures in the KRX relative to that of the us dollar (USD) currency futures. For this purpose, assuming the situation of foreign-asset investment the minimum variance hedging models based on OLS and ECM are compared with a simple 1: 1 hedge. The difference between previous studies and this one is in that the latter uses various kinds of hedging performance measures and analyzes the hedging performances by different hedging horizon. According to the empirical results, the USD currency futures outperforms the JPY and EUR currency futures when considering the risk only.

However, the results are reversed wilen incorporating the return as well as the risk. With respect to the comparative advantages among hedging types, the ECM-hedge turns out to be better than the others for evaluating the risk only, and the 1: 1 hedge proves to be superior to the others when considering both of the return and risk aspects. Based on the risk-reduction aspect. the hedging performances are gradually improving as the length of hedging period increases, while they deteriorate for considering both the return and risk aspects.

This study explores hedging strategies that use the KTB futures to hedge the price risk of the KTB spot portfolio. The study establishes the price sensitivity, risk-minimization, bivariate GARCH (1,1) models as hedging models, and analyzes their hedging performances. The sample period covers from September 29, 1999 to September 18, 2001. Time-matched prices at 11:00 (11:30) of the KTB futures and spot were used in the analysis. The most important findings may be summarized as follows. First, while the average hedge ration of the price sensitivity model is close to one, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios tend to be greater for daily data than for weekly data. Second, for the daily in-sample data, hedging effectiveness is the highest for the GARCH model with time-varying hedge ratios, but the risk-minimization model with constant hedge ratios is not far behind the GARCH model in its hedging performance. In the case of out-of-sample hedging effectiveness, the GARCH model is the best for the KTB spot portfolio, and the risk-minimization model is the best for the corporate bond portfolio. Third, for daily data, the in-sample hedge shows a better performance than the out-of-sample hedge, except for the risk-minimization hedge against the corporate bond portfolio. Fourth, for the weekly in-sample hedges, the price sensitivity model is the worst and the risk-minimization model is the best in hedging the KTB spot portfolio. While the GARCH model is the best against the KTB +corporate bond portfolio, the risk-minimization model is generally as good as the GARCH model. The risk-minimization model performs the best for the weekly out-of-sample data, and the out-of-sample hedges are better than the in-sample hedges. Fifth, while the hedging performance of the risk-minimization model with daily moving window seems somewhat superior to the traditional risk-minimization model when the trading volume increased one year after the inception of the KTB futures, on the average the traditional model is better than the moving-window model. For weekly data, the traditional model exhibits a better performance. Overall, in the Korean bond markets, investors are encouraged to use the simple risk-minimization model to hedge the price risk of the KTB spot and corporate bond portfolios.

Our study explores friction costs in terms of competition and market structure, considering factors such as market share, industry leverage levels, industry hedging levels, number of peers, and the geographic concentration that influences reinsurance purchase in the Property and Casualty insurance industry in China. Financial factors that influence the hedging level are also included. The data are hand collected from 2008 to 2015 from the Chinese Insurance Yearbook. Using panel data analysis techniques, the results are interesting. The capital structure shows a significant negative relationship with the hedging level. Group has a negative relationship with reinsurance purchases. Assets exhibit a negative relationship with hedging levels. The hedging level has a negative relation with the individual hedging level. Insurers have less incentive to hedge because it provides less resource than leverage. The study also robustly investigates the strategic risk management separately by the financial crises.

A futures contract may rely upon physical delivery or cash settlement to liquidate open positions at the maturity date. Contract settlement specification has direct impacts on the behavior of the futures price, leading to different effects of liquidity risk on futures hedging. This chapter compares such effects under alternative settlement specifications with a simple analytical model of daily price change. Numerical simulation results demonstrate that capital constraint reduces hedging effectiveness and tends to produce a lower optimal hedge ratio. As the futures contract proceeds toward the maturity date, hedgers will take larger hedge position in order to achieve better hedging effectiveness. Finally, optimal hedge ratios are higher (resp. lower) under cash settlement for the bivariate normal (resp. lognormal) assumptions, whereas hedging effectiveness is almost always greater under cash settlement.

We examine whether the hedging effectiveness of crude oil futures is affected by asymmetry in the return distribution by applying tail-specific metrics to compare the hedging effectiveness of both short and long hedgers. The hedging effectiveness metrics we use are based on lower partial moments (LPM), value at risk (VaR) and conditional value at risk (CVaR). Comparisons are applied to a number of hedging strategies including ordinary least square (OLS), and both symmetric and asymmetric GARCH models. We find that OLS provides consistently better performance across different measures of hedging effectiveness as compared with GARCH models, irrespective of the characteristics of the underlying distribution.

Stock ownership and incentive options are used by companies to retain and motivate employees and managers. These grants usually come with vesting features which require grantees to hold the assets for certain periods. This vesting requirement makes the grantee's total wealth highly undiversified. As a result, as shown by previous researchers, grantees tend to value these incentive securities below market. In this case, grantees will have a strong desire to hedge away the firm-specific risk. Facing the restrictions of direct hedges such as shorting the firm's stock, employees may implement a partial hedge by taking positions in an asset highly correlated with the firm's stock, such as an industry index. In this chapter, we investigate the effects of such a partial hedge. Using the continuous-time, consumption-portfolio framework as a backdrop, we demonstrate that the hedging index can enhance the employee's optimal portfolio holding and increase his intertemporal utility. Consequently, his private valuations of these grants are higher than that without the partial hedging. However, because the partial hedge makes the employee's total wealth less sensitive to the firm's stock price, it will also undermine the incentive effects. Therefore, the presumed incentive effects of these restricted assets should not be taken for granted.

Using a sample of oil and gas firms in the USA, the study examines the relation between the presence of hedging and annual report readability.

The authors use regression analysis to examine the relation between the presence of hedging and annual report readability.

The authors find that annual reports of firms with the use of hedging are less readable (i.e. difficult to read and understand). The authors also find that the primary results are more pronounced for firms with a higher level of business volatility.

The study contributes to the finance literature on the use and value of hedging and to the accounting literature on the determinants of annual report readability. The Securities and Exchange Commission (SEC) has persistently asked companies to improve the readability of their disclosures to stakeholders (SEC, 1998; 2013, 2014). Hence, the study not only identifies a potential determinant (i.e. hedging) that may influence the level of readability but also supports the current regulatory policy by the SEC, which is encouraging companies to improve readability.