  This paper explores the dependence modeling of financial assets in a dynamic
way and its critical role in measuring risk. Two new methods, called
Accelerated Moving Window method and Bottom-up method are proposed to detect
the change of copula. The performance of these two methods together with Binary
Segmentation \cite{vostrikova1981detection} and Moving Window method
\cite{guegan2009forecasting} is compared based on simulated data. The
best-performing method is applied to Standard \& Poor 500 and Nasdaq indices.
Value-at-Risk and Expected Shortfall are computed from the dynamic and the
static model respectively to illustrate the effectiveness of the best method as
well as the importance of dynamic dependence modeling through backtesting.
