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Implementing Option Pricing Models When Asset Returns Are Predictable
Author(s) -
LO ANDREW W.,
WANG JIANG
Publication year - 1995
Publication title -
the journal of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.151
H-Index - 299
eISSN - 1540-6261
pISSN - 0022-1082
DOI - 10.1111/j.1540-6261.1995.tb05168.x
Subject(s) - predictability , valuation of options , econometrics , asset (computer security) , black–scholes model , economics , construct (python library) , computer science , financial economics , mathematics , statistics , volatility (finance) , computer security , programming language
The predictability of an asset's returns will affect the prices of options on that asset, even though predictability is typically induced by the drift, which does not enter the option pricing formula. For discretely‐sampled data, predictability is linked to the parameters that do enter the option pricing formula. We construct an adjustment for predictability to the Black‐Scholes formula and show that this adjustment can be important even for small levels of predictability, especially for longer maturity options. We propose several continuous‐time linear diffusion processes that can capture broader forms of predictability, and provide numerical examples that illustrate their importance for pricing options.