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Empirical evidence of the hedging pressure risk premium exists only in the futures contracts with delivery-related options. Since hedging pressure is supposed to exist for all futures contracts, the empirical evidence raises an interesting empirical question: whether the hedging pressure risk premium is in fact the risk premium associated with the delivery-related options. This chapter contains an empirical test of the non-redundancy between the two related but alternative sources of non-market risks. For the test, we employs a futures risk premia model in which the expected futures returns contain the market risk premium (proxied by the equity market risk premium) and two non-market risk premia (proxied by the hedging pressure effect and by the delivery risk premium reflected in the returns of futures options, respectively). Our main finding is that both the hedging pressure and the delivery risk premia are non-redundant and statistically significant for futures contracts with delivery-related options. This finding implies a substantial degree of segmentations between these futures markets and the underlying asset markets.

This research applies a static hedging portfolio method derived from Derman, Ergener, and Kani (1995) (henceforth Derman's SHP method) and a new SHP method with European cash-or-nothing binary options developed by Chung, Shih, and Tsai (2013) to price European continuous double barrier (ECDB) options and the rebates of the ECDB options. Our numerical results indicate that the new SHP method outperforms Derman's SHP method in terms of efficiency and effectiveness under all circumstances.

This paper provides a fuller characterization of the analytical upper bounds for American options than has been available to date. We establish properties required of analytical upper bounds without any direct reliance on the exercise boundary. A class of generalized European claims on the same underlying asset is then proposed as upper bounds. This set contains the existing closed form bounds of Margrabe, (1978) and Chen and Yeh (2002) as special cases and allows randomization of the maturity payoff. Owing to the European nature of the bounds, across-strike arbitrage conditions on option prices seem to carry over to the bounds. Among other things, European option spreads may be viewed as ratio positions on the early exercise option. To tighten the upper bound, we propose a quasi-bound that holds as an upper bound for most situations of interest and seems to offer considerable improvement over the currently available closed form bounds. As an approximation, the discounted value of Chen and Yeh's (2002) bound holds some promise. We also discuss implications for parametric and nonparametric empirical option pricing. Sample option quotes for the European (XEO) and the American (OEX) options on the S&P 100 Index appear well behaved with respect to the upper bound properties but the bid–ask spreads are too wide to permit a synthetic short position in the early exercise option.

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.

This chapter aims to find an optimal way to hedge foreign exchange exposures on three main currency pairs being the EURUSD, EURGBP and EURJPY. Furthermore, it analyses the risk level of each portfolio together with its kurtosis level. This chapter also looks into the relationship between the EURUSD portfolios and the VIX level.

This study is based on a back-testing analysis over a period of seven years starting in January 2007 and ending in December 2014. Two main Foreign Exchange Premium-Free strategies were structured using the Bloomberg Terminal. These were the ‘At-Expiry Forward Extra’ and the ‘Window Forward Extra’. Portfolios were created using FX options strategies, FX spot and FX forwards. The EURUSD portfolios were also analysed and compared with the VIX level in order to see whether volatility has a direct effect on the outcome of the strategies. The statistical significance of the difference between returns of portfolios was analysed using a paired sample t-test. Finally, the histogram and distribution curve of each portfolio were created and plotted in order to provide a more visual analysis of returns.

It was found that the optimal strategies in all cases were the FX option strategies. The portfolios’ risk was analysed and indicated that optimal portfolios do not necessarily derive the lowest risk. It was also found that with a high VIX level, the forward contract was the most beneficial whilst the option strategy benefited from a low VIX level. When testing for statistical significance between returns of different portfolios, in most cases, the difference in returns between portfolios resulted to be statistically insignificant. Although some similarities were noticed in distribution curves, these differed from the normal distribution. When analysing the kurtosis levels, it is found that such levels differed from that of a normal distribution which has a kurtosis level of 3. Interpretation of such histograms, distribution curves and the kurtosis analysis was explained.