Paper Info
Title
Online Learning of Volatility from Multiple Option Term Lengths.
Abstract
In this work we utilize implied volatility predictions from options on individual securities to predict the average volatility of an ensemble of securities. We cast this as a problem of online learning with expert advice, which has been studied extensively in the Machine Learning community. In this setting we treat each security as an expert and adaptively maintain a set of weights to combine the implied volatility predictions of all securities in our ensemble. We also consider options of different term lengths, treating each term length as a separate learning task in a Multi-task Learning (MTL) framework. In this MTL framework information is shared between the learning tasks to improve the overall learning process. We conduct experiments using historical option pricing data and corresponding historical volatility records. Using the straight mean of the implied volatility predictions as a benchmark strategy, we demonstrate improved combined predictions with a basic online learning algorithm and further improvement with our MTL framework.
Year
DOI
Venue
2016
10.1145/2951894.2951902
DSMM@SIGMOD
Field
DocType
Citations 
Stochastic volatility,Systemic risk,Data mining,Implied volatility,Valuation of options,Computer science,Granger causality,Lasso (statistics),Vector autoregression,Volatility (finance)
Conference
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Scott McQuade161.47
Claire Monteleoni232724.15