In this paper, based on Acharya and Pedersen’s [Journal of Financial Eco- nomics (2006)] overlapping generation model, we show that liquidity risk could influence the market risk forecasting through at least two ways. Then we argue that traditional liquidity adjusted VaR measure, the simply adding of the two risk measure, would underestimate the risk. Hence another approach, by modeling the liquidity adjusted returns (LAr) directly, was employed to incorporate liquidity risk in VaR measure in this study. Under such an approach, China’s stock market is specifically studied. We estimate the one-day-ahead “standard” VaR and liquidity adjusted VaR by forming a skewed Student’s t AR-GJR model to capture the asymmetric effect, non-normality and excess skewness of return, illiquidity and LAr. The empirical results support our theoretical arguments very well. We find that for the most illiquidity portfolio, liquidity risk represents more than 22% of total risk. We also find that simply adding of the two risk measure would underestimate the risk. The accuracy testing show that our approach is more accurate than the method of simply adding.

This paper examines a wide variety of popular volatility models for stock index return, including Random Walk model, Autoregressive model, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and extensive GARCH model, GARCH-jump model with Normal, and Student-t distribution assumption as well as nonparametric specification test of these models. We fit these models to Dhaka stock return index from November 20, 1999 to October 9, 2004. There has been empirical evidence of volatility clustering, alike to findings in previous studies. Each market contains different GARCH models, which fit well. From the estimation, we find that the volatility of the return and the jump probability were significantly higher after November 27, 2001. The model introducing GARCH jump effect with normal and Student-t distribution assumption can better fit the volatility characteristics. We find that that RW-GARCH-t, RW-AGARCH-t RW-IGARCH-t and RW-GARCH-M-t can pass the nonparametric specification test at 5% significance level. It is suggested that these four models can capture the main characteristics of Dhaka stock return index.

College Students’ credit in loan has become the focus of the authority of universities and commercial banks. It is therefore very imperative to establish the appraisal system on the credit of college students and it is pretty important and urgent for the business growth, decrease of risks. In the light of experience of developed countries in college students’ loan and personal consumption loan, the paper aims to design an appraisal system that fits China’s situation well

Numerous studies have shown that the simple random walk model outperforms all structural and time series models in forecasting the conditional mean of exchange rate changes. However, in many important applications, such as risk management, forecasts of the probability distribution of exchange rate changes are often needed. In this paper, we develop a nonparametric portmanteau evaluation procedure for out-of-sample density forecast and provide a comprehensive empirical study on the out-of-sample performance of a wide variety of time series models in forecasting the intraday probability density of two major exchange rates－Euro/Dollar and Yen/Dollar. We find that some nonlinear time series models provide better density forecast than the simple random walk model, although they underperform in forecasting the conditional mean. For Euro/Dollar, it is important to model heavy tails through a Student-t innovation and asymmetric time-varying conditional volatility through a regime-switching GARCH model for both in-sample and out-of-sample performance; modeling conditional mean and serial dependence in higher order moments (e.g.,conditional skewness), although important for in-sample performance, does not help out-of-sample density forecast. For Yen/Dollar, it is also important to model heavy tails and volatility clustering, and the best density forecast model is a RiskMetrics model with a Student-t innovation. As a simple application, we Þnd that the models that provide good density forecast generally provide good forecast of Value-at-Risk.