Measuring crisis risk using conditional copulas: An empirical analysis of the 2008 shipping crisis

Abstract

Summary The shipping crisis starting in 2008 was characterized by sharply decreasing freight rates and sharply increasing financing costs. We analyze the dependence structure of these two risk factors employing a conditional copula model. As conditioning factors we use the supply and demand of seaborne transportation. We find that crisis risk strongly increased already about 1 year before the actual crisis outburst and that the shipping crisis was predominantly driven by an oversupply of transport capacity. Therefore, market participants could have prevented or alleviated the consequences of the crisis by reducing the ordering and financing of new vessels.

Telling tales from the tails: High‐dimensional tail interdependence

“…The most widely applied dependence measure, Pearson's correlation coefficient, is an inadequate measure in many situations as it captures only the linear dependence between pairs of random variables (e.g., Longin & Solnik, 2001). Extreme dependence has been captured by copulas (e.g., Nelsen, 2007;Opitz, Seidel, & Szimayer, 2017;Patton, 2006), multivariate quantile regressions (e.g., White, Kim, & Manganelli, 2015), and multivariate extreme-value theory (e.g., Asimit, Gerrard, Hou, & Peng, 2016;Bücher, Jäschke, & Wied, 2015;Jansen & de Vries, 1991). However, these measures of dependence are generally feasible only in low dimensions.…”

Telling Tales from the Tails: High-Dimensional Tail Interdependence

“…Extreme dependence has been captured by copulas (e.g. Opitz et al, 2017;Nelsen, 2007;Patton, 2006), multivariate quantile regressions (e.g. White et al, 2015) and multivariate extreme-value theory (e.g.…”