Cookies?
Library Header Image
LSE Research Online LSE Library Services

A network analysis of the volatility of high-dimensionalfinancial series

Barigozzi, Matteo and Hallin, Marc (2017) A network analysis of the volatility of high-dimensionalfinancial series. Journal of the Royal Statistical Society. Series C: Applied Statistics. ISSN 0035-9254

[img] PDF - Accepted Version
Restricted to Repository staff only until 17 September 2018.

Download (2MB) | Request a copy

Identification Number: 10.1111/rssc.12177

Abstract

Interconnectedness between stocks and firms plays a crucial role in the volatility contagion phenomena that characterise financial crises, and graphs are a natural tool in their analysis. In this paper, we are proposing graphical methods for an analysis of volatility interconnections in the Standard & Poor’s 100 dataset during the period 2000-2013, which contains the 2007-2008 Great Financial Crisis. The challenges are twofold: first, volatilities are not directly observed and have to be extracted from time series of stock returns; second, the observed series, with about 100 stocks, is high-dimensional, and curse of dimensionality problems are to be faced. To overcome this double challenge, we propose a dynamic factor model methodology, decomposing the panel into a factor-driven and an idiosyncratic component modelled as a sparse vector autoregressive model. The inversion of this autoregression, along with suitable identification constraints, produces networks in which, for a given horizon h, the weight associated with edge (i; j) represents the h-step-ahead forecast error variance of variable i accounted for by variable j’s innovations. Then, we show how those graphs yield an assessment of how systemic each firm is. They also demonstrate the prominent role of financial firms as sources of contagion during the 2007-2008 crisis.

Item Type: Article
Official URL: http://onlinelibrary.wiley.com/journal/10.1111/(IS...
Additional Information: © 2016 Royal Statistical Society
Subjects: H Social Sciences > HG Finance
Sets: Departments > Statistics
Date Deposited: 12 Aug 2016 14:50
Last Modified: 16 Oct 2017 09:41
URI: http://eprints.lse.ac.uk/id/eprint/67456

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics