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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 paper studies hedging strategies that use the KOSDAQ50 index futures to hedge the price risk of the KOSDAQ50 index spot portfolio. This study uses the minimum variance hedge model and bivariate ECT-GARCH (1,1) model as hedging models, and analyzes their hedging performances. The sample period covers from January 31, 2001 to December 31, 2002. The most important findings may be summarized as follows. First, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios of the risk-minimization tend to be higher than those of GARCH model. Second, for the in-sample data, hedging effectiveness of GARCH model is higher than that of the risk-minimization, while for the out-of-sample data, hedging effectiveness of the risk-minimization with constant hedge ratios is not far behind the GARCH model in its hedging performance. Third, the hedging performance of KOSDAQ50 index futures is lower than that of KOSPI200 index futures, but higher than that of KTB futures. In conclusion, in the KOSDAQ50 index market, investors are encouraged to use the simple risk-minimization model to hedge the price risk of KOSDAQ50 spot portfolios.

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 test three parametric models in pricing and hedging higher-order moment swaps. Using vanilla option prices from the volatility surface of the Euro Stoxx 50 Index, the paper shows that the pricing accuracy of these models is very satisfactory under four different pricing error functions. The result is that taking a position in a third moment swap considerably improves the performance of the standard hedge of a variance swap based on a static position in the log-contract and a dynamic trading strategy. The position in the third moment swap is taken by running a Monte Carlo simulation.

This paper undertook empirical tests of three parametric models. The aim of the paper is twofold: assess the pricing accuracy of these models and show how the classical hedge of the variance swap in terms of a position in a log-contract and a dynamic trading strategy can be significantly enhanced by using third-order moment swaps. The pricing accuracy was measured under four different pricing error functions. A Monte Carlo simulation was run to take a position in the third moment swap.

The results of the paper are twofold: the pricing accuracy of the Heston (1993) model and that of two Levy models with stochastic time and stochastic volatility are satisfactory; taking a position in third-order moment swaps can significantly improve the performance of the standard hedge of a variance swap.

The limitation is that these empirical tests are conducted on existing three parametric models. Maybe more critical insights could have been revealed had these tests been conducted in a brand new derivatives pricing model.

This work is 100 per cent original, and it undertook empirical tests of the pricing and hedging accuracy of existing three parametric models.

Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.

This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.

The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.

The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

This paper examines the inflation hedging performance separated into expected and unexpected inflation in Korean equity funds. In particular, using the bootstrap approach, we identify whether the inflation hedging performance is based on skill or luck. We use the equity funds of the average net asset value (NAV) over 5 billion Korean won and over the 80% stock position. The sample data cover the period from January 2002 to March 2015. The main findings are as follows. First, most equity funds demonstrate a hedging performance against the unexpected inflation shock and this hedging performance seems to come from the fund manager’s skill. Second, our findings are robust across the sieve bootstrap results for the serial dependence and heteroscedasticity. Third, the equity funds have slightly different inflation hedging performances depending on their investment style. Among the investment styles, small-cap, growth, or small and growth style funds demonstrate more hedging performance against unexpected inflation shock. This hedging performance seems to come from the fund manager’s skill. Finally, in the case of the funds separated by winner and loser, the winner funds have more hedging performance for unexpected inflation shock than the loser funds.

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.

This research explores the spillovers and portfolio implications for green bonds and environmental, social and governance (ESG) assets in the context of the rapidly expanding trend in green finance investments and the need for a green recovery in the post-COVID-19 era.

This study utilizes Diebold and Yilmaz’s (2014) spillover method and portfolio strategies (hedge ratio, optimal weights and hedging effectiveness) for the data starting from February 29, 2012, to March 14, 2022.

The study’s findings reveal that the lower volatility spillover is evidenced between the green bonds and ESG stocks during tranquil and turbulent periods (e.g. COVID-19 and Russia-Ukraine War). Furthermore, hedging costs are lower both in normal times and during economic slumps. Investing the bulk of the funds in green bonds makes it possible to achieve maximum hedging effectiveness between the S&P green bond (GB) and the S&P 500 ESG.

Both investors and policymakers may use these findings to make wise investment and policy choices to achieve post-COVID environmental sustainability.

Unlike previous research, this is the first to explore the interconnectedness among the major global and country-specific green bonds and ESG assets. The major findings of this study about the lower volatility spillovers and hedging costs between green bonds and ESG assets during the tranquil and turbulent periods may contribute to the post-COVID investment portfolio for environmental sustainability.

This article explores the hedging effectiveness of KIKO options by using the mean-variance analysis of Markowitz and by comparing three hedge measures such as Sharpe hedging effectiveness measure proposed by Howard and D’Antonio (1987), Fishburn (1977)’s measure, and Ederington (1979)’s. which calculates the degree to which the rate of return per unit of risk increases and total volatilty and down-side risk of hedged portfolio diminishes respectively. This paper differs from the previous researches in that this research first assumes that the firms hold the same value of dollar amount as that of short calls at each of settlement dates, and secondly this article performs multiple period of analysis instead of single period.

This paper finds first that the hedging effectiveness of KIKO options is not better than that of currency forward contract in making a reduction of the total volatility and down-side risks of hedged portfolio. Secondly the hedge effectiveness is the highest at the first settlement date but it plunges when the time passes by, which is mainly due to the fact that the value of in-the-money put decreases, but that of out-of-the-money call increases as the time to maturity increases. Thirdly, it is found that another KIKO option with the equal premium shows even better hedging performance than the original KIKO in three aspects of hedging effectiveness. In conclusion, the KIKO turns out to be a lemon.

This study is to examine the unblasedness hypothesis and hedging effectiveness in KOSPI20() futures market. The unbiasedness and efficiency hypothesis is carried out using a cointegration methodology. And hedging effectiveness is measured by comparing hedging performance of the naive hedge model, OLS hedge model. and constant correlation bivariate GARCH (1. 1) hedge model based on rolling windows. The sample period covers from May. 3. 1996 to December. 8, 2005.

The empirical results are summarized as follows: First, there exists the cOintegrating relationship between realized spot prices and futures prices of the 10 day. 22 day. 44 day. and 59 day prior to maturity. Second. futures prices of backward the 10 day. 22 day. 44 day from maturity provide unbiased forecasts of the realized spot prices. The KOSPI200 futures price is likely to predict accurately future KOSPI200 spot prices without the trader having to pay a risk premium for the privilege of trading the contract. Third. for shorter maturity. the futures price appears to be the best forecaster of spot price. Forth, bivariate GARCH hedging effectiveness outperforms the naive and OLS hedging effectiveness.

The implications of these findings show that KOSPI200 futures market behaves as unbiased predictor of future spot price and risk management instrument of KOSPI200 spot portfolio.