EVALUATION OF THE FORECASTING ABILITY OF RISK-NEUTRAL DENSITY IN BITCOIN OPTIONS

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dc.contributor.authorSaparbekov, Dias
dc.date.accessioned2024-12-23T07:31:05Z
dc.date.available2024-12-23T07:31:05Z
dc.date.issued2024-12-12
dc.description.abstractThis study assesses the out-of-sample forecasting capabilities of risk-neutral density models in Bitcoin options market, with a focus on the Normal Inverse Gaussian (NIG) density. Understanding forward-looking price dynamics becomes critical as cryptocurrencies continue to gain a reputation in financial markets. This research examines how the NIG model, with its capability to capture skewness and kurtosis, compares to the benchmark log-normal (LN) distribution. The analysis applies the likelihood ratio test to evaluate the predictive performance of the models. As a result, NIG model improves the accuracy of tail forecasts, outperforming LN in capturing extreme market movements, which holds implications for risk management and market timing in Bitcoin market.
dc.identifier.citationSaparbekov, Dias. (2024). Evaluation of the Forecasting Ability of Risk-Neutral Density in Bitcoin Options. Nazarbayev University Graduate School of Business.
dc.identifier.urihttps://nur.nu.edu.kz/handle/123456789/8362
dc.language.isoen
dc.publisherNazarbayev University Graduate School of Business
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/
dc.subjectType of access: Open access
dc.subjectBitcoin
dc.subjectRisk-neutral density
dc.subjectNormal inverse Gaussian
dc.subjectLikelihood ratio test
dc.titleEVALUATION OF THE FORECASTING ABILITY OF RISK-NEUTRAL DENSITY IN BITCOIN OPTIONS
dc.typeMaster`s thesis

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