Book contents
- Frontmatter
- Contents
- Contributors
- Introduction
- PART IDATA: THE PREREQUISITE FOR MANAGING SYSTEMIC RISK
- PART IISTATISTICS AND SYSTEMIC RISK
- 6Statistical Assessments of Systemic Risk Measures
- 7Regime Switching Models and Risk Measurement Tools
- PART IIIMEASURING AND REGULATING SYSTEMIC RISK
- PART IVNETWORKS
- PART VSYSTEMIC RISK ANDMATHEMATICAL FINANCE
- PART VICOUNTERPARTY RISK AND SYSTEMIC RISK
- PART VIIALGORITHMIC TRADING
- PART VIIIBEHAVIORAL FINANCE: THE PSYCHOLOGICAL DIMENSION OF SYSTEMIC RISK
- PART IXREGULATION
- PART XCOMPUTATIONAL ISSUES AND REQUIREMENTS
- PART XIACCOUNTING ISSUES
- References
7 - Regime Switching Models and Risk Measurement Tools
from PART II - STATISTICS AND SYSTEMIC RISK
Published online by Cambridge University Press: 05 June 2013
Book contents
- Frontmatter
- Contents
- Contributors
- Introduction
- PART IDATA: THE PREREQUISITE FOR MANAGING SYSTEMIC RISK
- PART IISTATISTICS AND SYSTEMIC RISK
- 6Statistical Assessments of Systemic Risk Measures
- 7Regime Switching Models and Risk Measurement Tools
- PART IIIMEASURING AND REGULATING SYSTEMIC RISK
- PART IVNETWORKS
- PART VSYSTEMIC RISK ANDMATHEMATICAL FINANCE
- PART VICOUNTERPARTY RISK AND SYSTEMIC RISK
- PART VIIALGORITHMIC TRADING
- PART VIIIBEHAVIORAL FINANCE: THE PSYCHOLOGICAL DIMENSION OF SYSTEMIC RISK
- PART IXREGULATION
- PART XCOMPUTATIONAL ISSUES AND REQUIREMENTS
- PART XIACCOUNTING ISSUES
- References
Summary
Abstract Structural changes in the financial system, resulting in broader economic crisis, have the common theme of the creation of new money equivalents that have a sudden change in value and spark contagion in the broader credit markets. Approaches to modeling the financial system, with a sufficient level of precision to be able to predict or anticipate the collapse of key assets (e.g. money equivalents) has been hampered by a lack of data about the markets and participants.
Given the type of market data that is readily available, we consider a variety of time-series models which could be used to identify and potentially anticipate a structural change in the financial system. There are two key characteristics of asset prices, which are related to market stress: the level of volatility and the amount of correlation. Typically, volatility is modeled using an auto-regressive structure and if the parameters of this structure (e.g. the average level of volatility) are allowed to be driven by a marked Poisson process (e.g. a hidden Markov chain), then we have a tool for identifying regime shifts or structural changes in financial markets. While correlation models can be more complicated, because of the number of parameters, the same type of underlying dynamic structure of a marked Poisson process can be used to identify changes in a correlation structure.
Information
- Type
- Chapter
- Information
- Handbook on Systemic Risk , pp. 180 - 190Publisher: Cambridge University PressPrint publication year: 2013
References
, and , (2002). International asset allocation with regime shifts. The Review of Financial Studies 15 (4) 1137–1187.CrossRefGoogle Scholar
, , and (2011). Measuring Systemic Risk in Finance and Insurance Sectors. Working Paper.
, (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31 307-327. http://en.wikipedia.org/wiki/Tim_ BollerslevCrossRefGoogle Scholar
, (1990). Modeling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. The Review of Economics and Statistics 72 (3) 498–505. http://en.wikipedia.org/wiki/Tim_BollerslevCrossRefGoogle Scholar
, (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation. Econometrica 50 987-1008.CrossRefGoogle Scholar
, (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics 20 (3) 339–50.CrossRefGoogle Scholar
, (1990). Analysis of time series subject to changes in regime. Journal of Econometrics 45 39-70.CrossRefGoogle Scholar
, , and , (1994). Bayesian analysis of stochastic volatility models. Journal of Business and Economic Statistics 12(4) 371-89.Google Scholar
, , and , (1998). Stochastic volatility: likelihood inference and comparison with ARCH models. Review of Economic Studies 65 (3) 361–93.CrossRefGoogle Scholar
and , (2001). Markov chain Monte Carlo methods for switching diffusion models. Biometrika 88 299-315.CrossRefGoogle Scholar
(1991). Conditional heteroskedasticity in asset returns: a new approach. Econometrica 59 347-70.CrossRefGoogle Scholar
and (2010). MCMC methods for continuous-time financial econometrics. In Handbook of Financial Econometrics, and (eds), 1-72.Google Scholar
, and , (2011). Bayesian analysis of multivariate regime switching covariance model. Working Paper, Penn State University Center for the Study of Global Financial Stability.
Accessibility standard: Unknown
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
Accessibility compliance for the PDF of this chapter is currently unknown and may be updated in the future.