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The depth and breadth of the market for contingent claims, including exotic options, has expanded dramatically. Regulators have expressed concern regarding the risks of exotics to the financial system, due to the difficulty of hedging these instruments. Recent literature focuses on the difficulties in hedging exotic options, e.g., liquidity risk and other violations of the standard Black‐Scholes model. This article provides insight into hedging problems associated with exotic options: 1) hedging in discrete versus continuous time, 2) transaction costs, 3) stochastic volatility, and 4) non‐constant correlation. The author applies simulation analysis of these problems to a variety of exotics, including Asian options, barrier options, look‐back options, and quanto options.

Leveraged exchange traded funds (ETFs) have become increasingly popular since their introduction in 2006. In recent years, options on leveraged ETFs have been promoted as a means of enhancing returns and reducing risk. The purpose of this paper is to examine the interchangeability of S & P 500 ETF options with leveraged S & P 500 ETF options and to what extent these options allow investors to manage their risk exposure.

With increasing liquidity for these fund’s options, simple option strategies such as covered calls and protective puts can be implemented. This study derives call-call and put-put parity between options on the underlying index and the associated leveraged ETFs. The paper examines comparative measures of return and risk on the underlying indices, along with covered call and protective put positions.

Using the formulations derived, this study shows options on non-leveraged ETFs or on the underlying index can be substituted for leveraged ETF options. Empirical results suggest substituting options on leveraged ETFs with options on the underlying index or index ETF give comparable results, but can differ as the realized leverage ratio over time differs from projected values.

This study is the first to the authors’ knowledge that investigates option strategies on leveraged and inverse ETFs of equity indices. It is also the first to derive call-call and put-put parity relations between options on ETFs and related leveraged and inverse ETFs. The results contribute to securities issuance, investment strategies, and option parity relations.

Informed traders may prefer the options market to the stock market for reasons including the leverage effect, transaction costs, restrictions on short sale. Many studies try to predict future returns of stocks using informed traders' behavior in the options market. In this study, we examine whether the trading volume ratios of single stock options have the predictive power for future returns of the underlying stock. By analyzing the stock price responses to the “preliminary announcement of performance” of 36 underlying stocks on the Korea Exchange from November 2014 to March 2021 and the trading volume of options written on those stocks, we investigate the relation between the option ratios, which are the call option volume to put option volume ratio (C/P ratio) and the option volume to stock volume ratio (O/S ratio), and the future returns of the underlying stock. We also examine which ratio is better in predicting the future returns. The authors found that both option ratios showed the statistically significant predictability about future returns of the underlying stock and that the return predictability of the O/S ratio is more robust than that of the C/P ratio. This study shows that indicators generated in the options market can be used to predict future underlying stock returns. Further, the findings of this study contributed to a dearth of literature pertaining to single stock options. The results suggest that the single stock options market is efficient and influences the price discovery in the stock market.

The KOSPI 200 options at its initial stage generated a significant number of violations in no-arbitrage conditions which involve both options and the underlying index. However, when the arbitrage conditions are formed independent of the underlying index, the average size of violation is not large and few arbitrage opportunities exist. There are more frequent violations on near-maturity days, with in-the-money options and larger violation sizes during opening and closing hours. The arbitrage opportunities remain intact even after realistic transaction costs are taken into account and index futures prices are used instead of the stock index in an alternative specification.

This study analyses the determinants of the value of a portfolio of real options and explores implications for strategic management. It focuses the analysis on four elements: the number of real options in the portfolio, constraints on the number of options that can be exercised, the volatility of underlying assets, and the correlation between underlying assets. These elements are articulated around a trade-off between growth options and switching options and are applied to different strategic situations of technological, market, and macroeconomic uncertainty.

This article empirically tests the time-correlation of implied information reflecting the return dynamics of KOSPI 200 markets in the view of the decision making and market efficiency. Because option prices are not perfectly correlated with each other and with the underlying asset, the information contents of the option are different from those of the underlying market price. And, under the non-complete of the market and the limited arbitrage, the information implied in option (underlying) market price may be more useful in the option (underlying) market than in the underlying (option) market.

The estimation results show that the time-correlation of incremental information are existed in performance of out-of-sample pricing and delta hedging conditioned on MR, a result which is not suggestive of the informational efficiency of the KOSPI 200 market. But, the decision marking using the systematic pattern may not be useful due to the option pricing models that allows moments of higher order than two reflecting the source of which the risk-neutrality assumption is strongly rejected by the data.

We propose a novel method of forecasting equity option spreads using the degree of multiple listing as a proxy for expectations of future spreads. Spreads are a transactions fee for traders. To determine the future spreads on options being considered for purchase, traders must take current market trends affecting spreads into account. One such trend is the continued decline in spreads due to the multiple listing of options. Options listed on 4–6 exchanges compete more intensely than those listed on fewer exchanges, so that they may be expected to experience greater future declines in spreads. This study identifies the listing dates and number of listed exchanges for options listed on up to six exchanges as of May 2005. Listing criteria for multiple listing are defined with short- and long-term volumes, market capitalization, net income, and total assets being significant determinants of multiple listing. Short- and long-term volumes were found to have no explanatory power for multiple listing. Ranges of listing criteria are specified so that traders may locate the options of their choice.

The purpose of this paper is to investigate empirically the forecasting performance of jump-diffusion option pricing models of (Merton and Bates) with the benchmark Black–Scholes (BS) model relative to market, for pricing Nifty index options of India. The specific period chosen for this study canvasses the extreme up and down limits (jumps) of the Indian capital market. In addition, equity markets keep on facing high and low tides of financial flux amid new economic and financial considerations. With this backdrop, the paper focuses on finding an impeccable option-pricing model which can meet the requirements of option traders and practitioners during tumultuous periods in the future.

Envisioning the fact, the all option-pricing models normally does wrong valuation relative to market. For estimating the structural parameters that governs the underlying asset distribution purely from the underlying asset return data, we have used the nonlinear least-square method. As an approach, we analyzed model prices by dividing the option data into 15 moneyness-maturity groups – depending on the time to maturity and strike price. The prices are compared analytically by continuously updating the parameters of two models using cross-sectional option data on daily basis. Estimated parameters then used to figure out the forecasting performance of models with corresponding BS and market – for pricing day-ahead option prices and implied volatility.

The outcomes of the paper reveal that the jump-diffusion models are a better substitute of classical BS, thus improving the pricing bias significantly. But compared to jump-diffusion model of Merton’s, the model of Bates’ can be applied more uniquely to find out the pricing of three popularly traded categories: deep-out-of-the-money, out-of-the-money and at-the-money of Nifty index options.

The outcome of this research work reveals that the jumps are important components of pricing dynamics of Nifty index options. Incorporation of jump-diffusion process into option pricing of Nifty index options leads to a higher pricing effectiveness, reduces the pricing bias and gives values closer to the market. As the models have been tested in extreme conditions to determine the dominant effectuality, the outcome of this paper helps traders in keeping the investment protected under normal conditions.

The specific period chosen for this study is very unique; it canvasses the extreme up and down limits (jumps) of the Indian capital market and provides the most apt situation for testifying the pricing competitiveness of the models in question. To testify the robustness of models, they have been put into a practical implication of complete cycle of financial frame.

The purpose of this paper is to present a review of selected applications of contingent claims analysis in corporate finance. Contingent claims analysis is an approach to valuing payoffs which are contingent on other uncertain payoffs. The essential logic of the different applications reviewed is that we can value these derivative payoffs by replicating them with assets, whose prices are known. This simple logic is capable of providing one with insights into the pricing of new types of financial assets (e.g. optional bonds) as well as providing new insights into the valuation of strategic corporate investments.

The purpose of this paper is to examine the lead‐lag relationships between the National Stock Exchange (NSE) Nifty stock market index (in India) and its related futures and options contracts, and also the interrelation between the derivatives markets.

The paper uses serial correlation of return series and autoregressive moving average (ARMA) model for studying the lead‐lag relationship between hourly returns on the NSE Nifty index and its derivatives contracts like futures, call and put options. Further, the lead‐lag relation between hourly returns of the derivatives contracts among themselves is also studied using ARMA models.

The ARMA analysis shows that the NSE Nifty derivatives markets tend to lead the underlying stock index. The futures market clearly leads the cash market although this lead appears to be eroding slightly over time. Although the options market leads the cash overall, there is some feedback between the two with the underlying index leading at times. Further, it is found that the index call options lead the index futures more strongly than futures lead calls, while the futures lead puts more strongly than the reverse.

The results imply that the derivative contracts on NSE Nifty lead the underlying cash market. Thus, the derivative markets are indicative of futures price movements and this will certainly be helpful to potential investors to design their risk‐return portfolio while investing in stocks and derivatives contracts.

This paper is an original piece of work towards exploring the lead‐lag relation between NSE Nifty and the derivative contracts. The issue of price discovery on futures and spot markets and the lead‐lag relationship are topics of interest to traders, financial economists, and analysts.