Behavior of Extreme Dependence between Stock Markets when the Regime Shifts

Abstract

We propose a methodology based on multivariate extreme value theory, to analyze the dependence between markets during the financial crisis. We argue that extreme dependence based on block maximum is a more appropriate measure to study dependence between stock markets, when a regime shifts, than other alternatives. With this methodology, we are able to detect the increase in the extreme dependences between US and other markets during the 2008 financial crisis where traditional approaches fail to do so. In addition, the estimated dependent function allows one to quantify maximum impact of the crisis on each individual market. We then propose the use of a conditional loss distribution as a constructive tool for a stress test analysis in risk management study. Stress test levels with respect to 2008 financial data calculated from the conditional loss distribution are given.

Time series synchronization in cross-recurrence networks: uncovering a homomorphic law across diverse complex systems

“…Conversely, if it is too large, each state will occur with lower frequency, resulting in very few edges in the CRN and making it challenging to extract useful information. Considering the application of recurrence-based methods on shorter time series, an empirical range for n could be between [10,20]. Consider a two-dimensional time series {X t , Y t ; t ⩾ 0}, where the components have the same state space (a 1 , a 2 , .…”

“…Considering individual time series as elements in a system, the dependence between elements often leads to emergent behaviors in the system [5][6][7][8]. Particularly, the dependence or synchronous patterns between the extreme values in time series deserve much more attention as it is often associated with revealing systemic risks [9][10][11]. Traditional methods for analyzing the relationship between two time series typically employ linear measures such as Euclidean distance and Pearson correlation coefficient.…”