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Option pricing with state‐dependent pricing kernel
Author(s) -
Tong Chen,
Hansen Peter Reinhard,
Huang Zhuo
Publication year - 2022
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.22338
Subject(s) - autoregressive conditional heteroskedasticity , valuation of options , stochastic discount factor , econometrics , heteroscedasticity , autoregressive model , volatility (finance) , markov chain , economics , conditional variance , kernel (algebra) , implied volatility , computer science , mathematics , capital asset pricing model , statistics , combinatorics
We introduce a new volatility model for option pricing that combines Markov switching with the realized generalized autoregressive conditional heteroskedasticity (GARCH) framework. This leads to a novel pricing kernel with a state‐dependent variance risk premium and a pricing formula for European options, which is derived with an analytical approximation method. We apply the Markov‐switching Realized GARCH model to Standard and Poor's 500 index options from 1990 to 2019 and find that investors' aversion to volatility‐specific risk is time‐varying. The proposed framework outperforms competing models and reduces (in‐sample and out‐of‐sample) option‐pricing errors by 15% or more.