Forecasting liquidity-adjusted intraday Value-at-Risk with vine copulas

- We find strong tail dependence between intraday returns and bid-ask spreads of NASDAQ-stocks.
- Our understanding of non-linear liquidity commonality is integrated into the forecasting of liquidity-adjusted risk measures.
- We propose the first multivariate model for the joint distribution of stock returns and bid-ask spreads.
- The proposed multivariate model performs exceptionally well in forecasting liquidity-adjusted portfolio losses.
- Neglecting liquidity risk can lead to a severe underestimation of portfolio risk.

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 intraday 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.