TY - JOUR
T1 - A data-driven framework for consistent financial valuation and risk measurement
AU - Cui, Zhenyu
AU - Kirkby, J. Lars
AU - Nguyen, Duy
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/2/16
Y1 - 2021/2/16
N2 - In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for the underlying asset prices, and then utilize Fourier techniques to obtain the physical nonparametric density and cumulative distribution function for the log-returns process, based on which we compute risk measures. Then we risk-neutralize the nonparametric density and distribution functions to model-independently valuate a variety of financial derivatives, including path-independent European options and path-dependent exotic contracts. By estimating the state-price density explicitly, and utilizing a convenient basis representation, we are able to greatly simplify the pricing of exotic options all within a consistent model-free framework. Numerical examples, and an empirical example using real market data (Brent crude oil prices) illustrate the accuracy and versatility of the proposed method in handling pricing and risk management of multiple financial contracts based solely on observable time series data.
AB - In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for the underlying asset prices, and then utilize Fourier techniques to obtain the physical nonparametric density and cumulative distribution function for the log-returns process, based on which we compute risk measures. Then we risk-neutralize the nonparametric density and distribution functions to model-independently valuate a variety of financial derivatives, including path-independent European options and path-dependent exotic contracts. By estimating the state-price density explicitly, and utilizing a convenient basis representation, we are able to greatly simplify the pricing of exotic options all within a consistent model-free framework. Numerical examples, and an empirical example using real market data (Brent crude oil prices) illustrate the accuracy and versatility of the proposed method in handling pricing and risk management of multiple financial contracts based solely on observable time series data.
KW - Data-driven
KW - Empirical characteristic function
KW - Empirical density
KW - Finance
KW - Model-free
KW - Nonparametric
KW - Risk management
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U2 - 10.1016/j.ejor.2020.07.011
DO - 10.1016/j.ejor.2020.07.011
M3 - Article
AN - SCOPUS:85089460808
SN - 0377-2217
VL - 289
SP - 381
EP - 398
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 1
ER -