EVALUATION OF THE FORECASTING ABILITY OF RISK-NEUTRAL DENSITY IN BITCOIN OPTIONS
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Date
2024-12-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
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.
Description
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.