An application of closed-form GARCH option-pricing model on FTSE 100 option and volatility

Su, Yu-Cheng; Chen, MingDa; Huang, Han-Ching

doi:10.1080/09603101003652417

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…Jackwerth and Rubinstein (2001) and Li and Pearson (2007) found that the AHBS model performed better than the BSM model and other more mathematical models. Duan and Zhang (2001), Fofana and Brorsen (2001), Lehar et al (2002), Sabbatini and Linton (2004), Bollen and Rasiel (2003), Su et al (2010) all found empirical evidence about the superiority of the GARCH option-pricing model as compared to the BSM and AHBS models, but this was contradicted by Harikumar et al (2004). Bakshi et al (1997) found the pricing and hedging performance of stochastic volatility models to be much better than that of the BSM model, which was confirmed by Kim and Kim (2003), Dupoyet (2006) and Moyaert and Petitijean (2011).…”

Section: Literature Reviewmentioning

confidence: 88%

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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Section: Literature Reviewmentioning

confidence: 88%

An empirical exploration of the performance of alternative option pricing models

Bhat¹

2019

JIBR

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“…A comparing review of GARCH-M, EGARCH, TGARCH, and PGARCH is stated by a few researchers based on researchers such as (Su, 2010;Miron, Tudor, 2010;Awartani, Corradi, 2005;Gokan, 2000) that all the asymmetric models in GARCH helps to predict the volatility of daily return on stock throughout the world. Amongst all of the EGARCH models, it fits best to measure the volatility.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Meanwhile, CGARCH and TGARCH are supported by Gokbulut and Pekkaya (2014) as they find them more suitable for measuring volatility. A series of analyses were conducted by researchers from emerging countries like Kumar and Mishra (2019b), Floros (2008) in Nigeria, Moustafa Abd et al (2011, Angabini andWasiuzzaman (2011) in Malaysia, Ezzat Hassan (2012) of Egypt, Su (2010) in China, Emenike (2010) andFreddie et al (2012) of Saudi Arabia who made a comparison of various models from the GARCH and ARCH family and concluded that GARCH, GJR GARCH, and EGARCH are fitting for the clustering effect, leptokurtosis volatility measurements, and identifying leverage effect. Kumar and Biswal (2019) attempted a study to measure the volatility clustering and leverage effect of top future stock markets from Jan 1st, 2014, to Oct 31st, 2018, by implementing the GARCH family model.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Asymmetric GARCH Models for Predicting Indian Base Metal Price Volatility

Kumar,

Sahoo,

Sahoo

et al. 2024

Folia Oeconomica Stetinensia

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Research background Many studies have been done in the field of predicting the Volatility of Commodities; however, very little or no analysis has been conducted on any sector, industry, or indices to identify which model is best to understand the asset’s characteristics, as there is a hypothesis that all financial time series can be interpreted by implementing the same model. Purpose The primary objective is to identify different tools developed by the researchers in estimating impulsive clustering and leverage effects. A comparison will be made among the available tools of the GARCH family models to suggest the best tool to forecast and calculate volatility with the least error. Research methodology The data used are historical time series data of Indian base metal indices, i.e., Aluminum (AL), Copper (CO), Lead (LE), Nickel (NI), and Zinc (ZI) from NSE for a period from 1st June 2012 to 31st August 2022 from the official website of NSE of India. The study compared and attempted to identify which GARCH family model is suitable to measure the volatility clustering and leverage effect in Indian base metal indices by reducing the chances of error. Results The study has revealed that the GRACH asymmetric models, while approximating and predicting the financial time series, can enhance the model’s output when it has a high frequency. Here, the asymmetric GARCH models (TARCH, CGARCH, EGARCH, and PARCH) better predict volatility than classic models. Novelty This study is original in its approach, as a previous study stated the presence of volatility or leverage effect by implementing any one tool. However, this study will compare available tools to suggest which is appropriate for which sector. This analysis will support future researchers and practitioners in evaluating volatility clustering and the effect of leverage by implementing the appropriate GARCH family model without believing in a hypothesis that a single model is good enough to predict volatility.

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“…Since the work of Engle (1982) and Bollerslev (1986), a number of papers have been published to investigate the performance of the volatility models (see, e.g. Christoffersen and Jacobs, 2004b;Su et al, 2010;Huang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Calibrated GARCH models and exotic options

Kanniainen

Halme

2013

Applied Financial Economics

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This article examines the differences between various Generalized Autoregressive Conditional Heteroscedastics (GARCH) models in pricing exotic options, given that the models have been calibrated on the same data sets using information on both returns and plain vanilla options. We focused on four widely recognized specifications: the Heston-Nandi (HN), Leverage, News and Power models, of which the first is an affine model, and the others represent the family of nonaffine models. First, we found that when the models were calibrated using option data, the previously reported superiority of nonaffine models over the HN in option pricing may not be generally true. On the other hand, the HN, Leverage and News models priced various exotic options quite similarly; but contrary to the others, the Power model yielded somewhat abnormal prices, especially for barrier options. Using the Maximum Likelihood Estimation (MLE) approach with return data, however, yielded different conclusions. Especially with long in-sample periods on stock returns, the nonaffine characterizations may outperform the HN model in terms of both the likelihood value and the European option pricing error, and in this case, the nonaffine models also price exotics differently than the HN model. We also demonstrated that different estimation approaches can affect exotic prices substantially.

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An application of closed-form GARCH option-pricing model on FTSE 100 option and volatility

Cited by 7 publications

References 18 publications

An empirical exploration of the performance of alternative option pricing models

An empirical exploration of the performance of alternative option pricing models

Exploring Asymmetric GARCH Models for Predicting Indian Base Metal Price Volatility

Calibrated GARCH models and exotic options

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