EVALUATION OF THE FORECASTING ABILITY OF RISK-NEUTRAL DENSITY IN BITCOIN OPTIONS
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Date
2024-12-12
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Nazarbayev University Graduate School of Business
Abstract
This 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.
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Keywords
Type of access: Open access, Bitcoin, Risk-neutral density, Normal inverse Gaussian, Likelihood ratio test
Citation
Saparbekov, Dias. (2024). Evaluation of the Forecasting Ability of Risk-Neutral Density in Bitcoin Options. Nazarbayev University Graduate School of Business.