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The purpose of this paper is to examine whether volatility implied from dollar-rupee options is an unbiased and efficient predictor of ex post volatility, and to determine which options market is a better predictor of future realized volatility and to ascertain whether the model-free measure of implied volatility outperforms the traditional measure derived from the Black–Scholes–Merton model.

The information content of exchange-traded implied volatility and that of quoted implied volatility for OTC options is compared with that of historical volatility and a GARCH(1, 1)-based volatility. Ordinary least squares regression is used to examine the unbiasedness and informational efficiency of implied volatility. Robustness of the results is tested by using two specifications of implied volatility and realized volatility and comparison across two markets.

Implied volatility from both OTC and exchange-traded options is found to contain significant information for predicting ex post volatility, but is neither unbiased nor informationally efficient. The implied volatility of at-the-money options derived using the Black–Scholes–Merton model is found to outperform the model-free implied volatility (MFIV) across both markets. MFIV from OTC options is found to be a better predictor of realized volatility than MFIV from exchange-traded options.

This study throws light on the predictive power of currency options in India and has strong practical implications for market practitioners. Efficient currency option markets can serve as effective vehicles both for hedging and speculation and can convey useful information to the regulators regarding the market participants’ expectations of future volatility.

This study is a comprehensive study of the informational efficiency of options on an emerging currency such as the Indian rupee. To the author’s knowledge, this is one of the first studies to compare the predictive ability of the exchange-traded and OTC markets and also to compare traditional model-dependent volatility with MFIV.

This paper aims to propose the implied volatility index for the US dollar–Indian rupee pair (INRVIX). The study seeks to examine whether INRVIX truly reflects future USDINR (US Dollar-Indian rupee) volatility and signals profitable currency trading strategies.

Two measures of INRVIX are constructed and compared: a model-free version based on the methodology adopted by the Chicago Board of Options Exchange (CBOE) and a model-dependent version constructed from Black–Scholes–Merton-implied volatility. The proposed INRVIX is computed by tweaking some parameters of the CBOE methodology to ensure compatibility with the microstructure of the Indian currency derivatives market. The volatility forecasting ability of INRVIX is compared to that of a generalized autoregressive conditional heteroscedasticity (1,1) model. Ordinary least squares regression is used to examine the relationship between n-day-ahead USDINR returns and different quantiles of INRVIX.

Results indicate that INRVIX based on the model-free approach reflects ex post volatility in a better manner than its model-dependent counterpart, although neither measure is found to be an unbiased and efficient forecast. Subsample analysis across tranquil and turbulent periods corroborates the results. The volatility forecasting performance of INRVIX is found to be better than that of forecasts based on historical time-series. These results are consistent with similar studies of developed market currencies. The study does not find any significant relationship between extreme levels of INRVIX and the profitability of trading strategies based on such levels, which is contrary to results from the equity options market.

Foreign exchange volatility affects the costs of international trade and the external sector competitiveness of Indian multinationals. It is a significant risk factor for financial institutions and traders in the financial markets. An implied VIX for the USDINR could serve as an indicator of expected foreign exchange risk. It could thus provide a signal for a possible intervention in the forex market by the regulator. Regulators could introduce volatility derivative contracts based on the INRVIX. Such contracts would enable hedging of the pure volatility risk of dollar–rupee exposure. Thus, the study has practical implications for investors, hedgers, regulators and academicians alike.

To the author’s knowledge, this is one of a few studies to construct an implied VIX for an emerging currency like the rupee. The study is based on up-to-date sample data that includes the recent COVID-19 market crash. A novel contribution of this paper is that in addition to examining whether INRVIX contains information about future USDINR volatility, and it also examines the signalling power of INRVIX for currency trading strategies.

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 ascertain the pattern of the implied volatility function for currency options traded on the National Stock Exchange of India (NSE), identify its potential determinants and to investigate any seasonality in the pattern.

The paper examines four different specifications for the implied volatility smile of exchange-traded dollar-rupee options. These specifications are tested by running Ordinary Least Squares (OLS) regressions on a daily basis for all options over the entire sample period. Seven potential determinants for the shape of the volatility function are identified. Contemporaneous and lead-lag relationships between these determinants and the shape of the volatility function are examined using OLS and multivariate VAR. Impulse response functions are employed to test the strength and persistence of the lead-lag relations. Seasonality of the smile pattern is tested using OLS.

The study shows that the implied volatility function for dollar-rupee options is asymmetric and varies with the time to maturity of the option. Historical volatility, momentum and jumps in the exchange rate, time to maturity, traded volume of options and volatility in the stock market appear to Granger-cause the shape of the volatility smile. Feedback causality is observed from the shape of the smile to the volatility, momentum and jumps in the exchange rate and trading volume of currency options. A weak day-of-the-week effect is observed in the pattern of the volatility smile.

The study sheds light on the potential determinants of the smile and highlights the predictive power of the smile which findings can be useful to market practitioners for pricing and hedging of dollar-rupee options. The study has strong practical implications during a period of increased volatility in the dollar-rupee pair.

Most of the existing literature regarding implied volatility smiles has focused either on the volatility smile of US equity index options or that of major liquid currencies. There is a need for such studies in the context of options on emerging market currencies such as the Indian rupee which are characterized by thin trading and frequent central bank intervention and signaling. To the best of the author’s knowledge this study is the first to focus on the volatility smile of exchange-traded options on the US dollar–Indian rupee.