Development of financial option pricing system based on fpga and machine learning

The Special Issue aims to highlight the interaction between actuarial and financial mathematics, which, due to the recent low interest rates and implications of COVID-19, requires an interlace between actuarial and financial methods, along with control theory, machine learning, mortality models, option pricing, hedging, unit-linked contracts and drawdown analysis, among others [...]

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A new hybrid parametric and machine learning model with homogeneity hint for European-style index option pricing

Neural Computing and Applications ◽

10.1007/s00521-016-2303-y ◽

2016 ◽

Vol 28 (12) ◽

pp. 4061-4077 ◽

Cited By ~ 13

Author(s):

Shom Prasad Das ◽

Sudarsan Padhy

Keyword(s):

Machine Learning ◽

Option Pricing ◽

Learning Model ◽

Machine Learning Model ◽

Index Option

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Galactic Air Improves Ancillary Revenues with Dynamic Personalized Pricing

INFORMS Journal on Applied Analytics ◽

10.1287/inte.2021.1105 ◽

2022 ◽

Author(s):

Arinbjörn Kolbeinsson ◽

Naman Shukla ◽

Akhil Gupta ◽

Lavanya Marla ◽

Kartik Yellepeddi

Keyword(s):

Machine Learning ◽

Dynamic Pricing ◽

Machine Learning Techniques ◽

Testing Framework ◽

Customer Group ◽

Industry Standard ◽

Learning Techniques ◽

Start Up ◽

Pricing System ◽

Implementation Period

Ancillaries are a rapidly growing source of revenue for airlines, yet their prices are currently statically determined using rules of thumb and are matched only to the average customer or to customer groups. Offering ancillaries at dynamic and personalized prices based on flight characteristics and customer needs could greatly improve airline revenue and customer satisfaction. Through a start-up (Deepair) that builds and deploys novel machine learning techniques to introduce such dynamically priced ancillaries to airlines, we partnered with a major European airline, Galactic Air (pseudonym), to build models and algorithms for improved pricing. These algorithms recommend dynamic personalized ancillary prices for a stream of features (called context) relating to each shopping session. Our recommended prices are restricted to be lower than the human-curated prices for each customer group. We designed and compared multiple machine learning models and deployed the best-performing ones live on the airline’s booking system in an online A/B testing framework. Over a six-month live implementation period, our dynamic pricing system increased the ancillary revenue per offer by 25% and conversion rate by 15% compared with the industry standard of human-curated rule-based prices.

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