Performance of the Heston’s Stochastic Volatility Model: A Study in Indian Index Options Market

Singh, Shivam; Dixit, Alok

doi:10.4236/tel.2016.62018

Cited by 12 publications

(10 citation statements)

References 38 publications

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“…Drawing from extant literature the following potential determinants of the volatility smile are examined: option market-specific variables; variables representing the spot exchange rate dynamics; and a measure of domestic stock market volatility. The intuition for considering these variables is explained here: Time to maturity (T) is measured as the ratio of the number of working days till the option expiry date to total number of working days in the year considered as 252 (Beber, 2008; Sehgal and Vijayakumar, 2008; Singh and Dixit, 2016). Trading days are considered because non-trading days are often ignored by market participants. Following Pena et al (1999), Beber (2008) and Hafner and Wallmeier (2000), the natural log of the number of contracts traded on each sample day (LNCONTR) is used as a proxy for liquidity in the options market. Historical volatility (FXHVOL) is defined as the standard deviation of the daily logarithmic returns of the USDINR exchange rate over a moving window of 20 trading days and annualized considering 252 trading days in a year.…”

Section: Methodsmentioning

confidence: 99%

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The economic determinants of the implied volatility function for currency options

Bhat¹

2018

IJOEM

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Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

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Section: Methodsmentioning

confidence: 99%

“…Time to maturity (T) is measured as the ratio of the number of working days till the option expiry date to total number of working days in the year considered as 252 (Beber, 2008; Sehgal and Vijayakumar, 2008; Singh and Dixit, 2016). Trading days are considered because non-trading days are often ignored by market participants.…”

Section: Methodsmentioning

confidence: 99%

The economic determinants of the implied volatility function for currency options

Bhat¹

2018

IJOEM

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“…Singh and Vipul (2015) found mispricing by the BSM model to persist despite the use of a realized volatility measure. Singh and Dixit (2016) found outperformance by the Heston model. All these studies have focused only on the pricing of Indian equity index options.…”

Section: Literature Reviewmentioning

confidence: 93%

An empirical exploration of the performance of alternative option pricing models

Bhat¹

2019

JIBR

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Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

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“…When taking DVF predictions, we obtain the socalled ad hoc Black-Sholes model (AHBS) [13,14,15]. Quite a few studies found that AHBS approaches outperform much better than classical BS models [16,17].…”

Section: Parametric Modelsmentioning

confidence: 99%

“…where AHBS C is the call option prices under the AHBS model, and IV  is the estimated implied volatility. The AHBS model releases the unrealistic assumption of constant volatility and is able to obtain better performances [16,17].…”

Section: Featuresmentioning

confidence: 99%

A CNN Based System for Predicting the Implied Volatility and Option Prices

Wei

Xie

Cheng

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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The evaluations of option prices and implied volatility are critical for option risk management and trading. Common strategies in existing studies relied on the parametric models. However, these models are based on several idealistic assumptions. In addition, previous research on option pricing mainly depends on the historical transaction records without considering the performance of other concurrent options. To address these challenges, we proposed a convolutional neural network (CNN) based system for predicting the implied volatility and the option prices. Specifically, the customized non-parametric learning approach is first used to estimate the implied volatility. Second, several traditional parametric models are also implemented to estimate these prices as well. The convolutional neural network is utilized to obtain the predictions based on the estimation of the implied volatility. Our experiments based on Chinese SSE 50 ETF options demonstrate that the proposed framework outperforms the traditional methods with at least 40.11% performance enhancement in terms of RMSE.

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Performance of the Heston’s Stochastic Volatility Model: A Study in Indian Index Options Market

Cited by 12 publications

References 38 publications

The economic determinants of the implied volatility function for currency options

The economic determinants of the implied volatility function for currency options

An empirical exploration of the performance of alternative option pricing models

A CNN Based System for Predicting the Implied Volatility and Option Prices

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