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The purpose of this paper is to test for and model non‐linearities in option price deviations from the Black Scholes (BS) model in FTSE 100 index options over the time period 1997‐2006.

The economic specification and estimation methodology is outlined, the data are discussed, and the empirical results are analysed.

The tests reject the linearity hypothesis and the paper shows that the exponential smooth transition autoregressive model is capable of capturing the non‐linear behaviour of option price misalignments. The paper finds that even though FTSE 100 index options are heavily traded, transaction costs prevent rapid adjustments of option prices from their “optimal” value.

The paper presents new empirical evidence, which explicitly allows for the possibility that option price misalignments from the BS price can be characterised by a non‐linear mean reverting process.

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 Black‐Scholes (B‐S) model in its various formulations has been the mainstay paradigm on option pricing since its basic formulation in 1973. The model has generally been proven empirically robust, despite the well documented empirical evidence of mispricing deep‐in‐the‐money, deep out‐of‐the‐money and, occasionally, at‐the‐money options with near maturities [see Galai (1983)]. Research on explaining the observed pricing anomalies has focused on the variance of the return of the underlying asset, which, in the case of the B‐S model, is assumed to remain invariant over time. The variance term is not directly observable, leading researchers to speculate that pricing discrepancies may be caused by misspecification of this variable. More specifically, interest in the volatility variable has centered about the implied standard deviation (ISD).

The purpose of this paper is to address the concern about the impact of accounting regulatory change pertaining to employee share options (ESOs) on earnings management. Following Australia's adoption of International Financial Reporting Standards (IFRS) in 2005, companies are required to recognise the fair value of ESOs as expenses. Due to inherent imprecision in the estimate of ESO's fair value, the regulatory change from disclosure to recognition was widely claimed to potentially give rise to an alternative mechanism to manage earnings. This study provides empirical evidence on whether the regulatory change leads to earnings management problems.

This study uses the regulatory change in accounting for ESOs to provide a direct test of earnings management between disclosed versus recognised regimes for the same sample of firms. The sample consists of Australian firms from S&P/ASX300 for the period from 2003 to 2006.

The results show that, although the accounting regulatory change from disclosure to recognition may provide an alternative earnings management vehicle, there is no evidence of this occurring. There could be several reasons for this finding. First, the statistical tests lack power. Second, there are stricter audit tests on recognised amounts than on disclosed amounts. Third, given the concern of excessive pay and the close scrutiny of compensation, managers may have already understated ESO values in the disclosure regime. Finally, managers have limited time and resources and the effort involved in the adoption of IFRS in 2005 could have restricted the time available to manage earnings via the ESO reporting channel.

This study adds to the limited research on whether a change in accounting regulation for employee share options from disclosure to recognition gives rise to greater scope for earnings management. One reason for the lack of empirical evidence in the research is due to the problem of designing a test. Bernard and Schipper suggest that within‐firm studies have limitations for comparing the effects of recognition versus disclosure when the change is driven by an estimate becoming more reliable. A cross‐sectional study is also problematic due to self‐selection bias if firms can choose between disclosure versus recognition. This study circumvents potential design problems raised by Bernard and Schipper by setting a test using regulatory change which allows the test to be compared directly using the same company.

The purpose of this paper is to derive a new option pricing model for options on futures calendar spreads. Calendar spread option volume has been low and a more precise model to price them could lead to lower bid-ask spreads as well as more accurate marking to market of open positions.

The new option pricing model is a two-factor model with the futures price and the convenience yield as the two factors. The key assumption is that convenience follows arithmetic Brownian motion. The new model and alternative models are tested using corn futures prices. The testing considers both the accuracy of distributional assumptions and the accuracy of the models’ predictions of historical payoffs.

Panel unit root tests fail to reject the unit root null hypothesis for historical calendar spreads and thus they support the assumption of convenience yield following arithmetic Brownian motion. Option payoffs are estimated with five different models and the relative performance of the models is determined using bias and root mean squared error. The new model outperforms the four other models; most of the other models overestimate actual payoffs.

The model is parameterized using historical data due to data limitations although future research could consider implied parameters. The model assumes that storage costs are constant and so it cannot separate between negative convenience yield and mismeasured storage costs.

The over 30-year search for a calendar spread pricing model has not produced a satisfactory model. Current models that do not assume cointegration will overprice calendar spread options. The model used by the Chicago Mercantile Exchange for marking to market of open positions is shown to work poorly. The model proposed here could be used as a basis for automated trading on calendar spread options as well as marking to market of open positions.

The model is new. The empirical work supports both the model’s assumptions and its predictions as being more accurate than competing models.

From 2007, New Zealand firms must report the cost of granting employee stock options (ESOs). Market‐based option pricing models assume that option holders are unconstrained in their portfolio choices and thus are indifferent to the specific risk of any firm. By contrast, ESO holders are frequently required to hold portfolios that are over‐exposed to the firm that employs them and so adopt exercise policies that reflect their individual risk preferences. Applying the model of Ingersoll (2006) to hypothetical ESOs, we show that ESO cost can be extremely sensitive to employee characteristics of risk aversion and under‐diversification. This result casts doubt on the usefulness of any market‐based model for pricing ESOs, since such models, by definition, produce option values that are independent of employee characteristics. By limiting employee discretion over the choice of exercise date, vesting restrictions help reduce the magnitude of this problem.

This paper studies the performance of commonly employed stochastic volatility and jump models in the consistent pricing of The CBOE Volatility Index (VIX) and The S&P 500 Index (SPX) options. With the existence of active markets for volatility derivatives and options on the underlying instrument, the need for models that are able to price these markets consistently has increased. Although pricing formulas for VIX and vanilla options are now available for commonly used models exhibiting stochastic volatility and/or jumps, it remains to be shown whether these are able to price both markets consistently. This paper fills this vacuum.

In particular, the Heston model, the Heston model with jumps in returns and the Heston model with simultaneous jumps in returns and variance (SVJJ) are jointly calibrated to market quotes on SPX and VIX options together with VIX futures.

The full flexibility of having jumps in both returns and volatility added to a stochastic volatility model is essential. Moreover, we find that the SVJJ model with the Feller condition imposed and calibrated jointly to SPX and VIX options fits both markets poorly. Relaxing the Feller condition in the calibration improves the performance considerably. Still, the fit is not satisfactory, and we conclude that one needs more flexibility in the model to jointly fit both option markets.

Compared to existing literature, we derive numerically simpler VIX option and futures pricing formulas in the case of the SVJ model. Moreover, the paper is the first to study the pricing performance of three widely used models to SPX options and VIX derivatives.

The purpose of this paper is to use fundamental models incorporating structural relationships within the firm in a terminal value model for the second stage of a two-stage valuation model utilized to estimate the value of a company.

The innovation is that growth options are identified within the structural relationships and a model capturing the value of the optionality is incorporated in the second stage of the two-stage valuation model.

Significant outcomes are that terminal value is shown to be a large portion of a company’s total value and the price behavior for initial public offerings produced by the model is consistent with the result of empirical studies.

This paper explicitly incorporates growth options in the second stage of a two-stage valuation model for the firm.

The purpose of this paper is to analyze different behaviors between long-term options’ implied volatilities and realized volatilities.

This paper uses a widely adopted short interest rate model that describes a stochastic process of the short interest rate to capture interest rate risk. Price a long-term option by a system of two stochastic processes to capture both underlying asset and interest rate volatilities. Model capital charges according to the Basel III regulatory specified approach. S&P 500 index and relevant data are used to illustrate how the proposed model works. Coup with the low interest rate scenario by first choosing an optimal time segment obtained by a multiple change-point detection method, and then using the data from the chosen time segment to estimate the CIR model parameters, and finally obtaining the final option price by incorporating the capital charge costs.

Monotonic increase in long-term option implied volatility can be explained mainly by interest rate risk, and the level of implied volatility can be explained by various valuation adjustments, particularly risk capital costs, which differ from existing published literatures that typically explained the differences in behaviors of long-term implied volatilities by the volatility of volatility or risk premium. The empirical results well explain long-term volatility behaviors.

The authors only consider the market risk capital in this paper for demonstration purpose. Dealers may price the long-term options with the credit risk. It appears that other than the market risks such as underlying asset volatility and interest rate volatility, the market risk capital is a main nonmarket risk factor that significantly affects the long-term option prices.

Analysis helps readers and/or users of long-term options to understand why long-term option implied equity volatilities are much higher than observed. The framework offered in the paper provides some guidance if one would like to check if a long-term option is priced reasonable.

It is the first time to analyze mathematically long-term options’ volatility behavior in comparison with historically observed volatility.

The purpose of this research is to examine the way uncertainty plays a role in built land prices. This paper provides basic real option pricing models of land prices on the demand side in central Tokyo. The model in this research analyzes micro land prices covering individual lot data provided by the Land Price Index. Since land prices are determined by both macro economic environment and micro lot‐specific attributes, this paper utilizes both time‐series economic data and cross‐sectional lot‐specific data. The model incorporates both time‐series (macro) and cross‐sectional (micro) data including uncertainty terms. In addition to the total uncertainty in asset prices over years, this research also gives some ideas of cross‐sectional uncertainty in land price variations by utilizing cross‐sectional amenity variables. These cross‐sectional and time‐series variables including the two uncertainty variables are arithmetically combined and the OLS method is conducted. The data set consists of 4,368 land price data from 1985 through 2000. The results from the option‐based models favor the application of the real option theory in land prices. The total uncertainty with respect to built asset return has a substantial effect on increasing land prices, which implies that an increase in uncertainty leads to an increase in land prices.