Liquidity commonality and risk management

We propose to model the joint distribution of bid-ask spreads and log returns of a stock portfolio by using Autoregressive Conditional Double Poisson and GARCH processes for the marginals and vine copulas for the dependence structure. By estimating the joint multivariate distribution of both returns and bid-ask spreads from intraday data, we incorporate the measurement of commonalities in liquidity and comovements of stocks and bid-ask spreads into the forecasting of three types of liquidity-adjusted Value-at-Risk (L-IVaR). In a preliminary analysis, we document strong extreme comovements in liquidity and strong tail dependence between bid-ask spreads and log returns across the firms in our sample thus motivating our use of a vine copula model. Furthermore, the backtesting results for the L-IVaR of a portfolio consisting of five stocks listed on the NASDAQ show that the proposed models perform well in forecasting liquidity-adjusted intraday portfolio profits and losses

Four essays on linear and extreme dependences in credit derivatives and equity markets

The dissertation investigates the linear and extreme dependence structures in credit derivatives and equity markets and studies their possible implications for asset pricing and portfolio management. Subsequent to the introduction in Chapter one, the second chapter deals with extreme dependence between the CDS spreads of major European banks and shows that the propensity of a bank to experience extreme co-movements in its CDS premia together with the market is priced in the bank's default swap spread during the recent financial crisis. The third chapter studies linear dependence structures in the liquidity of CDS contracts and investigates the impact of commonality in CDS liquidity on the pricing of credit default swaps. The fourth chapter extends the scope of the dissertation and additionally studies the dependence structures in equity markets as well as cross-dependences between credit derivatives and equity markets. The chapter analyzes the linear and extreme dependence between stock prices, stock liquidity, and credit risk using a dynamic vine copula model and proposes a liquidity- and credit-adjusted Value-at-Risk that enables risk managers to reliably forecast the total risk exposure of a stock investment. Finally, Chapter five investigates whether the choice of extreme dependence estimator affects the assessment of tail risks and has a significant impact on the validity and economic significance of key results from recent studies in the financial economics literature

Is tail risk priced in credit default swap premia?

We show that the propensity of a bank to experience extreme comovements in its credit default swap premia together with the market is priced in the bank’s default swap spread during the financial crisis. We measure a bank’s CDS tail beta by estimating the upper tail dependence between its default swap spreads and a credit default swap market index. Our study shows that protection sellers receive a premium for bearing the risk of extreme upward comovements in default risk. The economic significance of this effect is large yet limited to the recent financial crisis. Banks in the upper quintile of CDS tail beta have spreads that are on average 140 basis points higher than those of banks in the lower CDS tail beta quintile

A Comparison of Tail Dependence Estimators

We review several commonly used methods for estimating the tail dependence in a given data sample. In simulations, we show that especially static estimators produce severely biased estimates of tail dependence when applied to samples with time-varying extreme dependence. In some instances, using static estimators for time-varying data leads to estimates more than twice as high as the true tail dependence. Our findings attenuate the need to account for the time-variation in extreme dependence by using dynamic models. Taking all simulations into account, the dynamic tail dependence estimators perform best with the Dynamic Symmetric Copula (DSC) taking the lead. We test our findings in an empirical study and show that the choice of estimator significantly affects the importance of tail dependence for asset prices