DocumentCode
3261369
Title
Modelling volatility with mixture density networks
Author
Mostafa, Fahed ; Dillon, Tharam
Author_Institution
DEBII, Curtin Univ., Bentley, WA
fYear
2008
fDate
26-28 Aug. 2008
Firstpage
501
Lastpage
505
Abstract
Volatility is an important variable in financial forecasting. Forecasting volatility requires a development of a suitable model for it. In this paper, we examine different time series models for volatility modelling. Specifically, we will study the use of recurrent mixture density networks, GARCH and EGARCH models to model volatility. In addition, we demonstrate the impact of different factors on the accuracy and completeness of each of these models.
Keywords
financial management; recurrent neural nets; time series; exponential GARCH model; financial forecasting; recurrent mixture density network; time series; volatility modelling; Economic forecasting; Equations; Gaussian processes; Instruments; Neural networks; Portfolios; Predictive models; Pricing; Risk management; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Granular Computing, 2008. GrC 2008. IEEE International Conference on
Conference_Location
Hangzhou
Print_ISBN
978-1-4244-2512-9
Electronic_ISBN
978-1-4244-2513-6
Type
conf
DOI
10.1109/GRC.2008.4664673
Filename
4664673
Link To Document