Testing for multivariate volatility functions using minimum volume sets and inverse regression

Polonik, Wolfgang and Yao, Qiwei ORCID: 0000-0003-2065-8486 (2008) Testing for multivariate volatility functions using minimum volume sets and inverse regression. Journal of Econometrics, 147 (1). pp. 151-162. ISSN 0304-4076 (Submitted)

Preview

PDF Download (680kB) | Preview

• Publisher

Abstract

We propose two new types of nonparametric tests for investigating multivariate regression functions. The tests are based on cumulative sums coupled with either minimum volume sets or inverse regression ideas; involving no multivariate nonparametric regression estimation. The methods proposed facilitate the investigation for different features such as if a multivariate regression function is (i) constant, (ii) of a bathtub shape, and (iii) in a given parametric form. The inference based on those tests may be further enhanced through associated diagnostic plots. Although the potential use of those ideas is much wider, we focus on the inference for multivariate volatility functions in this paper, i.e. we test for (i) heteroscedasticity, (ii) the so-called ‘smiling effect’, and (iii) some parametric volatility models. The asymptotic behavior of the proposed tests is investigated, and practical feasibility is shown via simulation studies. We further illustrate our methods with real financial data.

Item Type:	Article
Official URL:	http://www.elsevier.com/locate/jeconom
Additional Information:	© 2008 Elsevier B.V
Divisions:	Statistics
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HA Statistics
Date Deposited:	27 May 2009 11:27
Last Modified:	05 Jan 2024 00:21
Projects:	DMS 0103606, DMS 0406431, GR/R97430, EP/C549058
Funders:	National Science Foundation, National Science Foundation, Engineering and Physical Sciences Research Council, Engineering and Physical Sciences Research Council
URI:	http://eprints.lse.ac.uk/id/eprint/24132

Actions (login required)

View Item