Model complexity and out-of-sample performance: evidence from S&P 500 index returns

Kaeck, Andreas, Rodrigues, Paulo and Seeger, Norman (2018) Model complexity and out-of-sample performance: evidence from S&P 500 index returns. Journal of Economic Dynamics and Control, 90. pp. 1-29. ISSN 0165-1889

[img] PDF - Accepted Version
Restricted to SRO admin only until 5 February 2020.
Available under License Creative Commons Attribution-NonCommercial No Derivatives.

Download (3MB)


We apply a range of out-of-sample specification tests to more than forty competing stochastic volatility models to address how model complexity affects out-of-sample performance. Using daily S&P 500 index returns, model confidence set estimations provide strong evidence that the most important model feature is the non-affinity of the variance process. Despite testing alternative specifications during the turbulent market regime of the global financial crisis of 2008, we find no evidence that either finite- or infinite-activity jump models or other previously proposed model extensions improve the out-of-sample performance further. Applications to Value-at-Risk demonstrate the economic significance of our results. Furthermore, the out-of-sample results suggest that standard jump diffusion models are misspecified.

Item Type: Article
Schools and Departments: School of Business, Management and Economics > Business and Management
Research Centres and Groups: Quantitative International Finance Network
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HG Finance
Depositing User: Andreas Kaeck
Date Deposited: 26 Jan 2018 11:12
Last Modified: 02 May 2018 15:11

View download statistics for this item

📧 Request an update