Applying Dynamic Training-Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{journals/ci/HamidaAA16,
-
title = "Applying Dynamic Training-Subset Selection Methods
Using Genetic Programming for Forecasting Implied
Volatility",
-
author = "Sana Ben Hamida and Wafa Abdelmalek and Fathi Abid",
-
journal = "Computational Intelligence",
-
year = "2016",
-
volume = "32",
-
number = "3",
-
pages = "369--390",
-
month = aug,
-
keywords = "genetic algorithms, genetic programming, implied
volatility forecast, static training-subset selection,
dynamic training-subset selection, mean squared errors,
percentage of non-fitted observations",
-
bibdate = "2017-05-27",
-
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/ci/ci32.html#HamidaAA16",
-
URL = "http://dx.doi.org/10.1111/coin.12057",
-
DOI = "doi:10.1111/coin.12057",
-
abstract = "Volatility is a key variable in option pricing,
trading, and hedging strategies. The purpose of this
article is to improve the accuracy of forecasting
implied volatility using an extension of genetic
programming (GP) by means of dynamic training-subset
selection methods. These methods manipulate the
training data in order to improve the out-of-sample
patterns fitting. When applied with the static subset
selection method using a single training data sample,
GP could generate forecasting models, which are not
adapted to some out-of-sample fitness cases. In order
to improve the predictive accuracy of generated GP
patterns, dynamic subset selection methods are
introduced to the GP algorithm allowing a regular
change of the training sample during evolution. Four
dynamic training-subset selection methods are proposed
based on random, sequential, or adaptive subset
selection. The latest approach uses an adaptive subset
weight measuring the sample difficulty according to the
fitness cases' errors. Using real data from S&P500
index options, these techniques are compared with the
static subset selection method. Based on mean squared
error total and percentage of non-fitted observations,
results show that the dynamic approach improves the
forecasting performance of the generated GP models,
especially those obtained from the adaptive-random
training-subset selection method applied to the whole
set of training samples.",
- }
Genetic Programming entries for
Sana Ben Hamida
Wafa Abdelmalek
Fathi Abid
Citations