Mixture distribution-based forecasting using stochastic volatility models
Clements, Adam E., Hurn, Stanley, & White, Scott I. (2006) Mixture distribution-based forecasting using stochastic volatility models. Applied Stochastic Models in Business and Industry, 22(5-6), 547 -557.
Many traditional econometric methods forecast the conditional distribution of asset returns by a point prediction of volatility. Alternatively, forecasts of this distribution may be generated from a mixture of distributions. This paper proposes a method by which information extracted from the estimation of a standard stochastic volatility model (using non-linear filtering) can be used to generate mixture distribution forecasts. In general, it is found that forecasts based on mixture distributions are superior to those simply using point predictions of volatility. In terms of mixture distribution forecasts, the method proposed in this paper is found to be superior to a number of competing approaches.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Journal Article|
|Additional Information:||For more information, please refer to the journal's website (see hypertext link) or contact the author. Author contact details: email@example.com|
|Keywords:||forecasting, mixture distributions, non, linear filtering, stochastic volatility|
|Subjects:||Australian and New Zealand Standard Research Classification > ECONOMICS (140000)|
|Divisions:||Current > QUT Faculties and Divisions > QUT Business School|
|Copyright Owner:||Copyright 2006 John Wiley & Sons|
|Deposited On:||13 Jul 2007 00:00|
|Last Modified:||26 May 2015 05:20|
Repository Staff Only: item control page