Yin Liao

The benefit of modeling jumps in realized volatility for risk prediction: Evidence from Chinese mainland stocks

Abstract Recent literature has focused on realized volatility models to predict financial risk. This paper studies the benefit of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized volatility models are compared in terms of their VaR forecasting performances through a Monte Carlo study and an analysis based on empirical data of eight Chinese stocks. The results suggest that careful modeling of jumps in realized volatility models can largely improve VaR prediction, especially for emerging markets where jumps play a stronger role than those in developed markets.

Contingent Liabilities from Banks: How to Track Them?

In this paper, we develop a methodology to assess potential losses to the government that could arise from bank failures. The approach is intended to be simple, parsimonious, and used in real time. It generates an index that we call the banking sector contingent liability index (BCLI), based on the banking sector’s size, concentration, diversification, leverage, and riskiness of assets. The index is illustrated for 32 advanced and emerging market economies from 2006 to 2013, as well as a group of banks including global systemically important banks (G-SIBs).

Simulation-Based Density Estimation for Time Series Using Covariate Data

This article proposes a simulation-based density estimation technique for time series that exploits information found in covariate data. The method can be paired with a large range of parametric models used in time series estimation. We derive asymptotic properties of the estimator and illustrate attractive finite sample properties for a range of well-known econometric and financial applications.