Implied volatility estimation of bitcoin options and the stylized facts of option pricing
作者机构：COMSATS University Islamabad-Wah CampusWah CanttPakistan
出 版 物：《Financial Innovation》 (金融创新（英文）)
年 卷 期：2021年第7卷第1期
页 面：1508-1537页
核心收录：
学科分类：0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 020205[经济学-产业经济学]
主 题：Bitcoin options Deribit Bitcoin smile Implied volatility estimation Numerical estimation
摘 要：The recently developed Bitcoin futures and options contracts in cryptocurrency derivatives exchanges mark the beginning of a new era in Bitcoin price risk *** need for these tools dates back to the market crash of 1987,when investors needed better ways to protect their portfolios through option *** tools provide greater flexibility to trade and hedge volatile swings in Bitcoin prices *** violation of constant volatility and the log-normality assumption of the Black–Scholes option pricing model led to the discovery of the volatility smile,smirk,or skew in options *** stylized facts;that is,the volatility smile and implied volatilities implied by the option prices,are well documented in the option literature for almost all financial *** are expected to be true for Bitcoin options as *** data sets for the study are based on short-dated Bitcoin options(14-day maturity)of two time periods traded on Deribit Bitcoin Futures and Options Exchange,a Netherlandsbased cryptocurrency derivative *** estimated results are compared with benchmark Black–Scholes implied volatility values for accuracy and efficiency *** study has two aims:(1)to provide insights into the volatility smile in Bitcoin options and(2)to estimate the implied volatility of Bitcoin options through numerical approximation techniques,specifically the Newton Raphson and Bisection *** experimental results show that Bitcoin options belong to the commodity class of assets based on the presence of a volatility forward skew in Bitcoin option ***,the Newton Raphson and Bisection methods are effective in estimating the implied volatility of Bitcoin ***,the Newton Raphson forecasting technique converges faster than does the Bisection method.
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