Option pricing
OwnerDavid Gershoninventor
Life of this patent
Estimating the term…
Abstract
Methods and systems are described herein for pricing options. In particular, the option price is obtained by satisfying consistency conditions. A new technique is described for pricing an option using minimal inputs, while achieving self-consistent and accurate results. Techniques for generating contingent probability density functions from volatility smile data are also described herein. Techniques are also described for calculating paths for non-vanilla options. As conventional software and hardware may take several hours or days to perform these methods, systems for providing real-time option prices, some of which include destributed processing, are also described herein.
Claims19
A system for optimizing option pricing, comprising:
- a user device configured to transmit a request for a price of an option associated with an asset at a first expiry date;
- a database comprising real-time market data and static data associated with the asset;
- an optimization device communicatively coupled to the user device and the database, and configured to receive the real-time market data and static data, determine an optimized density function, and obtain the requested price of the option using the optimized density function, wherein the optimization device comprises:
- a. an AI-enabled tolerance generator configured to generate, based on the real-time market data and static data, optimization parameters comprising a first tolerance for variable optimization, and a second tolerance, a third tolerance, a time series and a set of strikes for density function optimization;
- b. an AI-enabled variable optimizer configured to determine a first variable satisfying the first tolerance, wherein the first variable satisfies the first tolerance if a change in variables between the first variable and a baseline variable is less than the first tolerance;
- c. an AI-enabled density function optimizer configured to:
- i. determine a first density function based on the first variable, wherein the first density function is optimized to obtain a second density function comprising a series of time homogeneous density functions corresponding to maturities ranging from current instant to the first expiry date in accordance with the time series generated by the AI-enabled tolerance generator;
- ii. determine whether a first density function satisfies the second tolerance, wherein the second tolerance is satisfied if a change in option prices obtained using the first density function and a baseline density function is less than the second tolerance;
- iii. obtain a plurality of integrals using the second density function comprising the series of time homogeneous density functions and the set of strikes received from the AI-enabled tolerance generator;
- iv. determine a third density function at the first expiry date based on the plurality of integrals;
- v. determine whether the third density function satisfies the third tolerance, wherein the third tolerance is satisfied if a maximum threshold change among all strikes associated with the plurality of integrals and a baseline set of integrals is less than the third tolerance;
- and
- vi. determine the third density function is the optimized density function based on a determination that the third density function satisfies the third tolerance;
- or
- continue the optimized option pricing until a fourth density function satisfies the second tolerance and the third tolerance, the fourth density function comprising one or more density functions to be optimized.
A method of optimizing option pricing, comprising:
- receiving a request for a price of an option associated with an asset at a first expiry date;
- obtaining real-time market data and static data associated with the asset;
- determining an optimized density function based at least in part the market data and the static data;
- and
- obtaining the requested price of the option using the optimized density function.
Dependencies are inferred from the claim text.