Which is the right option for Indian market: Gaussian, normal inverse Gaussian, or Tsallis?
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RAVI aNSHUMAN V. IIMB Management ReviewSummary: This paper models Nifty spot prices using frameworks based on Gaussian distribution (geometric Brownian motion) and non-Gaussian distributions, viz. normal inverse Gaussian (NIG), and Tsallis distributions, to investigate which model best captures the underlying dynamics. The simulation results suggest that Tsallis outperforms the Gaussian model and NIG in predicting the Nifty spot prices. Amongst the non-Gaussian models, Tsallis better captures the behaviour of Nifty spot prices than NIG distribution. Based on our findings, we conclude that non-Gaussian option pricing frameworks to price Nifty options are likely to give better results over the traditional class of Gaussian models.
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Main Library | Vol 31, Issue 3/ 55511085JA2 (Browse shelf(Opens below)) | Available | 55511085JA2 | |||||
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Main Library On Display | JRNL/GEN/Vol 31, Issue 3/55511085 (Browse shelf(Opens below)) | Vol 31, Issue 3 (30/01/2019) | Not for loan | September, 2019 | 55511085 |
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| Vol 31, Issue 2/ 55510746JA7 Techno-commercial feasibility analysis of 4G mobile services in India. | Vol 31, Issue 2/ 55510746JA8 Angel Investments in India – Trends, Prospects and Issues. | Vol 31, Issue 3/ 55511085JA1 Central bank target rates and term structure of interest rates: A study of six Asia-Pacific countries | Vol 31, Issue 3/ 55511085JA2 Which is the right option for Indian market: Gaussian, normal inverse Gaussian, or Tsallis? | Vol 31, Issue 3/ 55511085JA3 High bids and low recovery: A possible case for non-performing loan auctions in India | Vol 31, Issue 3/ 55511085JA4 Investor reaction to extreme price shocks in stock markets: A cross country examination | Vol 31, Issue 3/ 55511085JA5 Innovation and economic growth in European Economic Area countries: The Granger causality approach |
This paper models Nifty spot prices using frameworks based on Gaussian distribution (geometric Brownian motion) and non-Gaussian distributions, viz. normal inverse Gaussian (NIG), and Tsallis distributions, to investigate which model best captures the underlying dynamics. The simulation results suggest that Tsallis outperforms the Gaussian model and NIG in predicting the Nifty spot prices. Amongst the non-Gaussian models, Tsallis better captures the behaviour of Nifty spot prices than NIG distribution. Based on our findings, we conclude that non-Gaussian option pricing frameworks to price Nifty options are likely to give better results over the traditional class of Gaussian models.
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