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The purpose of this paper is to develop a loan insurance pricing model allowing for the skewness and kurtosis existing in underlying asset returns.

Using the theory of Gram-Charlier option, the authors first derive a closed-form solution of the Gram-Charlier pricing model. To address the difficulties in implementing the pricing model, the authors subsequently propose an iterative method to estimate skewness and kurtosis in practical application, which shows a relatively fast convergence rate in the empirical test.

Not only the theoretical analysis but also the empirical evidence shows that the effects of skewness and kurtosis on loan insurance premium tend to be negative and positive, respectively. Furthermore, the actual values of skewness and kurtosis are usually negative and positive, respectively, which leads to the empirical result that the pricing model ignoring skewness and kurtosis substantially underestimates loan insurance premium.

This paper proposes a loan insurance pricing model considering the skewness and kurtosis of asset returns, in which the authors use the theory of Gram-Charlier option. More importantly, the authors further propose a novel iterative method to estimate skewness and kurtosis in practical application. The empirical evidence suggests that the Gram-Charlier pricing model captures the information content of skewness and kurtosis.

The purpose of this paper is to ascertain the effectiveness of major deterministic and stochastic volatility-based option pricing models in pricing and hedging exchange-traded dollar–rupee options over a five-year period since the launch of these options in India.

The paper examines the pricing and hedging performance of five different models, namely, the Black–Scholes–Merton model (BSM), skewness- and kurtosis-adjusted BSM, NGARCH model of Duan, Heston’s stochastic volatility model and an ad hoc Black–Scholes (AHBS) model. Risk-neutral structural parameters are extracted by calibrating each model to the prices of traded dollar–rupee call options. These parameters are used to generate out-of-sample model option prices and to construct a delta-neutral hedge for a short option position. Out-of-sample pricing errors and hedging errors are compared to identify the best-performing model. Robustness is tested by comparing the performance of all models separately over turbulent and tranquil periods.

The study finds that relatively simpler models fare better than more mathematically complex models in pricing and hedging dollar–rupee options during the sample period. This superior performance is observed to persist even when comparisons are made separately over volatile periods and tranquil periods. However the more sophisticated models reveal a lower moneyness-maturity bias as compared to the BSM model.

The study concludes that incorporation of skewness and kurtosis in the BSM model as well as the practitioners’ approach of using a moneyness-maturity-based volatility within the BSM model (AHBS model) results in better pricing and hedging effectiveness for dollar–rupee options. This conclusion has strong practical implications for market practitioners, hedgers and regulators in the light of increased volatility in the dollar–rupee pair.

Existing literature on this topic has largely centered around either US equity index options or options on major liquid currencies. While many studies have solely focused on the pricing performance of option pricing models, this paper examines both the pricing and hedging performance of competing models in the context of Indian currency options. Robustness of findings is tested by comparing model performance across periods of stress and tranquility. To the best of the author’s knowledge, this paper is one of the first comprehensive studies to focus on an emerging market currency pair such as the dollar–rupee.

The purpose of this paper is to develop an option pricing model applicable to US options. The lognormality assumption that has typically been imposed with past binomial and trinomial option pricing models is relaxed. The relaxed lattice model is then used to determine skewness and kurtosis of distributions of futures prices implied from option prices.

The relaxed lattice is based on Gaussian quadrature. The markets studied include corn, soybeans, and wheat. Skewness and kurtosis are implied by minimizing the squared deviations of actual option premia from predicted premia.

Positive skewness is the major source of nonnormality, but both skewness and kurtosis are important as the trinomial model that considers kurtosis has greater accuracy than the binomial model. The out‐of‐sample forecasting accuracy of the relaxed lattice models is better than the Black‐Scholes model in most, but not all cases.

The model might benefit from using option prices from more than one day. The implied skewness and kurtosis were quite variable and using more data might reduce this variability.

Empirical results mostly show positive implied skewness, which suggests extreme price rises were more likely than extreme price decreases.

The relaxed lattice is a new model and the results about implied higher moments are new for these commodities. There are competing models available that should be able to get similar accuracy, so one key advantage of the new approach is its simplicity and ease of use.

The purpose of this paper is to empirically analyze the investment and pension functions of housing reverse mortgages (HRM) with redemption option and raise the participation initiative of the Chinese elderly for the HRM model and enhance their cognition of this financial product.

Based on the perspective of the financial decisions of the elderly, this study designs an analysis model of the economic validity of HRM, and takes Shanghai (a pilot city for HRM loans in China) as an example, using data from the Shanghai real-estate index and market interest rates from January 1998 to December 2018, as well as the contract data of HRM, for empirical analysis.

The results show that the HRM with redemption option has the characteristics of European call option and can obtain the value of implicit option from the perspective of the elderly. Considering the present value of the accumulated pension income, the present value of the redemption option and the present value of the final housing value, the elderly can obtain investment income from HRM with redemption option. Therefore, for Chinese seniors, the HRM with redemption option has economic validity.

From the perspective of the demand of the elderly, participation in the HRM with redemption option can increase the life expectancy annuity for various pension expenditures and improve economic status while meeting the demand of inheritance motivation.

This study helps to clarify the financial decision-making process for elderly people who participate in HRM. On the one hand, it helps policy makers to optimize the implementation mode of HRM and promote the healthy and rapid development of HRM; on the other hand, it is conducive to raising the awareness of Chinese elderly people on this financial products and enhancing their enthusiasm for participating in HRM.

Few studies have directly analyzed the financial decision process of the HRM model from the perspective of the demand of the elderly. This study enriches the research viewpoint and method of HRM and accumulates data about the Chinese experience with HRM.

The assets of bankrupt firms are usually sold to unsuitable buyers at an extremely discounted price. Aiming to reduce the bankruptcy cost, the purpose of this paper is to propose a novel insurance system for associated loans.

In this insurance system, the joined firms are from the same industry and have a responsibility to buy the assets of potentially bankrupt firms at a relatively high price, because they could make better use of the assets than the buyers outside the industry. Further, the authors use the Shapley value to address the problem of bankruptcy cost allocation and additionally employ the method of Monte Carlo simulation to derive the numerical solution of the insurance premium of bankruptcy cost.

First, the relatively healthy and solvent firms in the insurance system could gain a larger proportion of benefits derived from the reduced cost of default, interestingly, the more so when the external cost of default is larger. Second, given the positive relationship between bankruptcy cost and asset correlation in practice, lenders and insurers face a trade-off to balance the cost against the benefit of asset correlation. Third, insurance premiums and bankruptcy costs decrease with the number of firms participating in this insurance system.

This paper proposes a novel insurance for associated loans, in which joined firms can pay a relatively low insurance premium due to the realization of reducing bankruptcy cost.

This chapter derives sentiment indicators (implied volatility and implied skewness) from the option pricing models of Corrado and Su (1996), Bakshi, Kapadia, and Madan (2003), and Zhang, Zhen, Sun, and Zhao (2017), and then integrates these sentiment indicators with artificial intelligence deep neural network (AIDNN) for developing the behavioral finance AIDNN (BFAIDNN) algorithms. We apply the BFAIDNN algorithms to daily derivatives data of Taiwan Futures and Options markets from 2015 to 2017. Our results demonstrate that the trading strategies established by the BFAIDNN algorithms can generate positive rewards.

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 chapter analyzes the empirical performance of alternative option pricing models using Black and Scholes (1973) as a benchmark. Specifically, we consider the Heston (1993) and Corrado and Su (1996) models and price call options on the S&P 500 index over the period from November 2010 to April 2011, evaluating each model by computing in- and out-of-sample pricing errors. We find that the two proposed models reduce both types of errors and mitigate the smile effect with respect to the benchmark. Moreover, in most of the cases, the model in Corrado and Su (1996) beats that in Heston (1993). Then, we conclude that skewness and kurtosis matter for option pricing purposes.

Analysis of Information Options offers new tools for evaluating investments in research, mineral exploration, logistics, energy transmission, and other information operations. With Information Options, the underlying assets are information assets and the rules governing exercise are based on the realities of the information realm (infosphere). Information Options can be modeled as options to “purchase” information assets by paying the cost of the information operations involved. Information Options arise at several stages of value creation. The initial stage involves observation of physical phenomena with accompanying data capture. The next refinement is to organize the data into structured databases. Then bits of information are selected from storage and synthesized into an information product (such as a management report). Next, the information product is presented to the user via an efficient interface that does not require the user to be a field expert. Information Options are similar in concept to real options but substantially different in their details, since real options have physical objects as the underlying assets and the rules governing exercise are based on the realities of the physical world. Also, while exercising a financial option typically kills the option, Information Options may include multiple exercises. Information Options may involve high volatility or jump processes as well, further enhancing their value. This chapter extends several important real option applications into the information realm, including jump process models and models for valuing options to synthesize any of n information items into any of m output assets.