We illustrate the role of left tail dependence—left tail mean (LTM)—in equity risk premium (ERP) predictability. LTM measures the average of pairwise left tail dependency among major equity sectors incorporating shocks imperceptible at the aggregate level. LTM, as well as the variance risk premium, significantly predicts the ERP in and out of sample, which is not the case with commonly used predictors. We find this predictability is the result of procyclical shocks’ reversals in a stable business cycle. This paper contributes to the ongoing debate on ERP predictability.

This paper investigates the counterparty credit risk of interest rate swaps positions using the credit valuation adjustment (CVA) measure, and examines the potential dependence relationships between the probability of default (PD) and exposure at default (EAD). We empirically tested, using interest rate swaption implied market volatilities, six tail dependence models: a Basel III Committee independent model, a Gaussian, Student’s t, Gumbel, Clayton, and a wrong way risk (WWR) copula with dependence approach. The results show that the CVA underestimation when using a Clayton copula for modelling the dependence of PD and EAD is about 10%–856% compared to using WWR, and the underestimation between using the standardised Basel independent model and using the Gaussian copula is about 552%–2621%, including the period of the 2007/2008 crisis, signalling that dependence is more important than tail dependence for CVA measurement. This has important implications for regulators, financial institutions, and credit risk managers when calculating counterparty risk.