Narrow Results

I consider the Black–Scholes–Merton option-pricing model from several angles, including personal, technical and, most importantly, from the perspective of a paradigm-shifting mathematical formula.

Starting from humble beginnings, the use of financial options has substantially increased as an important financial tool for both speculation and hedging over the last 50 years. This chapter discusses both the theoretical and practical applications of financial options and related models. While the content is somewhat technical, we provide illustrations of their applications in simple settings. We address particular stylized features of option pricing models.

Based upon comparative analysis, we first discuss different kinds of Greek letters in terms of Black–Scholes option pricing model, then we show how these Greek letters can be applied to perform hedging and risk management. The relationship between delta, theta, and gamma is also explored in detail.

During the past couple of months, the pandemic situation raised the need for assessment of the impact on derivatives, particularly weather and freight derivatives, as an innovative financial product. There are several issues and challenges faced by weather and freight derivatives in the financial market. This chapter aims to appreciate innovative financial derivatives and also address issues relating to the functioning of weather and freight derivatives. We have also examined the pricing models of weather derivatives across the globe. In addition, we examine the impact of the COVID-19 pandemic situation on weather derivatives.

This research discusses the role of cryptocurrencies in portfolio investment and observes the timing within which the cryptos provide benefit to investors in a traditional financial market. We first use a mean-variance spanning test to check for any improvement that cryptos bring to a well-diversified portfolio and find a significant difference between port-folios with and without cryptos. Second, we analyze the weight dynamics of cryptos in the minimum-variance portfolio and the tangent portfolio to examine if cryptos present a hedging property in the mean-variance viewpoint. The finding shows that the optimal weights of cryptos increase distinctly in a market distress period, which shows their hedging property in a mean-variance view. Finally, we include cryptos in a well-diversified portfolio composed of common assets to check their weight dynamics in both tangent portfolio and minimum-variance portfolio. Consequently, we found that the cryptos take more weights in the tangent portfolio rather than in the minimum-variance portfolio, while the weights of cryptos increased in both portfolios during the COVID-19 pandemic; we thus conclude that cryptocurrencies can bring some hedging effect even in a portfolio with very common traditional assets. We also compare gold and cryptos and find that they have a similar pattern of weight dynamics, although gold has a slightly better effect in eliminating the downside risk of a minimum-variance portfolio.

The objective of this paper is to estimate the hedge ratios of foreign-listed single stock futures (SSFs) and to compare the performance of risk reduction of different methods. The OLS method and a bivariate GJR-GARCH model are employed to estimate constant optimal hedge ratios and the dynamic hedging ratios, respectively. Data of the SSFs listed on the London International Financial Future and Options Exchange (LIFFE) are used in this research. We find that the data series have high estimated constant optimal hedge ratios and high constant correlation in the bivariate GJR-GARCH model, except for three SSFs with their underlying stocks traded in Italy. Our findings provide evidence that distance is a critical factor when explaining investor's trading behavior. Results also show that in general, of the three methods examined (i.e., naïve hedge, conventional OLS method and dynamic hedging) the dynamic hedging performs the best and that naïve hedge is the worst.

The objective of this paper is to estimate the hedge ratios of foreign-listed single stock futures (SSFs) and to compare the performance of risk reduction of different methods. The OLS method and a bivariate GJR-GARCH model are employed to estimate constant optimal hedge ratios and the dynamic hedging ratios, respectively. Data of the SSFs listed on the London International Financial Future and Options Exchange (LIFFE) are used in this research. We find that the data series have high estimated constant optimal hedge ratios and high constant correlation in the bivariate GJR-GARCH model, except for three SSFs with their underlying stocks traded in Italy. Our findings provide evidence that distance is a critical factor when explaining investor's trading behavior. Results also show that in general, of the three methods examined (i.e., naïve hedge, conventional OLS method and dynamic hedging) the dynamic hedging performs the best and that naïve hedge is the worst.