Change point detection for subprime crisis in American banking: From the perspective of risk dependence

The subprime crisis has received great attention in academic research but there is no consensus on when the crisis started and when it ended. Previous researchers have only mentioned their subjective judgments in related papers and well-accepted change point detection methods are not available. So the objective of this paper is to propose a multiple change point detection approach from the perspective of risk dependence by using copula function. Since the inter-dependence of different types of risks during crisis and non-crisis periods is significantly different, we monitor the change of dependence structure over time. The first step is to choose a proper copula that can accurately describe the dependence structure of the data. Thereafter, using the chosen copula to fit the data dynamically, a series of parameters are attained. Finally, the change points are identified by analyzing the trend of the fitted parameters. Based on the financial data of the top 100 American banks in Forbes' list, we empirically detect the start point, end point and peak period of the subprime crisis in American banking. The results show that the crisis started in 2007Q4 and ended in 2011Q3, and the peak period of the crisis was from 2009Q3 to 2010Q2.

[1]  Tomohiro Ando,et al.  Bayesian panel data analysis for exploring the impact of subprime financial crisis on the US stock market , 2012, Comput. Stat. Data Anal..

[2]  Eric Bouyé,et al.  Copulas for Finance - A Reading Guide and Some Applications , 2000 .

[3]  A. Kabundi,et al.  Domestic and foreign sources of volatility spillover to South African asset classes , 2013 .

[4]  M. Dooley,et al.  Transmission of the U.S. Subprime Crisis to Emerging Markets: Evidence on the Decoupling-Recoupling Hypothesis , 2009 .

[5]  Paul Embrechts,et al.  Change-Point Analysis for Dependence Structures in Finance and Insurance , 2001 .

[6]  Ling Hu Dependence patterns across financial markets: a mixed copula approach , 2006 .

[7]  Zhong Guan,et al.  A semiparametric changepoint model , 2004 .

[8]  Piotr Kokoszka,et al.  Change-point estimation in ARCH models , 2000 .

[9]  Francis X. Diebold,et al.  The Known, the Unknown, and the Unknowable in Financial Risk Management: Measurement and Theory Advancing Practice , 2010 .

[10]  The global financial crisis: World market or regional contagion effects? , 2014 .

[11]  Seong‐Min Yoon,et al.  Structural breaks and long memory in modeling and forecasting volatility of foreign exchange markets of oil exporters: The importance of scheduled and unscheduled news announcements , 2014 .

[12]  E. Luciano,et al.  Copula methods in finance , 2004 .

[13]  Georgios P. Kouretas,et al.  Dynamic correlation analysis of financial contagion: Evidence from the Central and Eastern European markets☆ , 2011 .

[14]  Zhijie Xiao,et al.  A Semiparametric Panel Model for Unbalanced Data with Application to Climate Change in the United Kingdom , 2010 .

[15]  Xiaoquan Liu,et al.  Measuring the subprime crisis contagion: Evidence of change point analysis of copula functions , 2012, Eur. J. Oper. Res..

[16]  P. Embrechts,et al.  Risk Management: Correlation and Dependence in Risk Management: Properties and Pitfalls , 2002 .

[17]  J. C. Rodríguez,et al.  Measuring financial contagion:a copula approach , 2007 .

[18]  S. Claessens,et al.  Financial Crises: Review and Evidence , 2013 .

[19]  Peter J. Elmer,et al.  Insolvency, Trigger Events, and Consumer Risk Posture in the Theory of Single-Family Mortgage Default , 1998 .

[20]  Arjun K. Gupta,et al.  Parametric Statistical Change Point Analysis , 2000 .

[21]  Berthold Schweizer,et al.  Probabilistic Metric Spaces , 2011 .

[22]  Peter Grundke,et al.  Crisis and risk dependencies , 2012, Eur. J. Oper. Res..

[23]  L. Horváth,et al.  Change‐point detection in panel data , 2012 .

[24]  Piotr Kokoszka,et al.  SEQUENTIAL CHANGE-POINT DETECTION IN GARCH(p,q) MODELS , 2004, Econometric Theory.

[25]  C. Genest,et al.  Statistical Inference Procedures for Bivariate Archimedean Copulas , 1993 .

[26]  M. Sklar Fonctions de repartition a n dimensions et leurs marges , 1959 .

[27]  D. Guégan,et al.  Change analysis of dynamic copula for measuring dependence in multivariate financial data , 2010 .

[28]  Andrew J. Patton Modelling Time-Varying Exchange Rate Dependence Using the Conditional Copula , 2001 .

[29]  Rong Li,et al.  Modelling dynamic dependence between crude oil prices and Asia-Pacific stock market returns , 2014 .

[30]  R. Nelsen An Introduction to Copulas , 1998 .

[31]  D. Andrews Tests for Parameter Instability and Structural Change with Unknown Change Point , 1993 .

[32]  Michael S. Gibson,et al.  Pitfalls in Tests for Changes in Correlations , 1997 .

[33]  B. Ewing,et al.  Volatility transmission between gold and oil futures under structural breaks , 2013 .

[34]  Chi Xie,et al.  Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree , 2012 .

[35]  Elias Tzavalis,et al.  Detection of structural breaks in linear dynamic panel data models , 2012, Comput. Stat. Data Anal..

[36]  A. Pettitt A Non‐Parametric Approach to the Change‐Point Problem , 1979 .

[37]  Martin T. Wells,et al.  Model Selection and Semiparametric Inference for Bivariate Failure-Time Data , 2000 .

[38]  L. Joseph,et al.  Estimation in multi-path change-point problems , 1992 .

[39]  Pascal Bondon,et al.  Scalable structural break detection , 2012, Appl. Soft Comput..