Wei-Choun Yu

Volatility Spillovers between the US and China Stock Markets: Structural Break Test with Symmetric and Asymmetric GARCH Approaches

Abstract The paper examines the short-run spillover effects of daily stock returns and volatilities between the Standard & Poors (S&P) 500 stock index in the US and the Shanghai Stock Exchange (SSE) index in China. First, we find that a structural break occurred in the SSE stock return mean in December 2005. Second, by analyzing modified general autoregressive conditional heteroscedasticity (GARCH)(1,1)-M models, we find evidence of a symmetric and asymmetric volatility spillover effect from the US to the China stock market in the post-break period. Third, we observe the symmetric volatility spillover effect from China to the US in the post-break period.

Term Time Employment and the Academic Performance of Undergraduates

This article outlines a framework for evaluating the decision of undergraduate students to engage in term-time employment as a method of financing higher education. We then examine the impact of work on academic achievement and find that employment has modest negative effects on student grades, with a grade point average (GPA) falling by 0.007 points per work hour. We use a unique custom dataset based on students at a traditional regional state university that provides information on student motivations and allows us to directly address some of the endogeneity problems that affect existing literature. We find that students who work for primarily financial reasons earn lower grades than students who work for career-specific skills but higher grades than those students motivated by a desire for general work experience.

Dynamic hedging performance with the evaluation of multivariate GARCH models: evidence from KOSTAR index futures

This article examines the hedging performance of the conventional Ordinary Least Squares (OLS) model and a variety of dynamic hedging models for the in-sample and out-of-sample periods of Korean daily Korea Securities Dealers Automated Quotation (KOSDAQ) STAR (KOSTAR) index futures. We employ the rolling OLS and various popular multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models to estimate and forecast the conditional covariances and variances of KOSTAR spot and futures returns. This article finds that dynamic hedging methods outperform the conventional method for the out-of-sample period. However, the simple rolling OLS is superior to all the other popular multivariate GARCH models.

The Housing Price Forecasting and the Outbreak of the Financial Crisis: Evidence of the Arima Model

This paper examines the out-of-sample forecasting performance of the SP therefore, the magnitude of the loss of mortgage-related securities could have been reduced if the model’s prediction had been considered. The model predicted extremely pessimistic one-year-ahead prices in 2008:2, 2008:3, and 2008:9, explaining the timeline of the collapse of Bear Stearns and Lehman Brothers as well as the subsequent global financial meltdown.

Predicting Stock Volatility Using After-Hours Information

We use realized volatilities based on after hours high frequency returns to predict next day volatility. We extend GARCH and long-memory forecasting models to include additional information: the whole night, the preopen, the postclose realized variance, and the overnight squared return. For four NASDAQ stocks (MSFT, AMGN, CSCO, and YHOO) we find that the inclusion of the preopen variance can improve the out-of-sample forecastability of the next day conditional day volatility. Additionally, we find that the postclose variance and the overnight squared return do not provide any predictive power for the next day conditional volatility. Our findings support the results of prior studies that traders trade for non-information reasons in the postclose period and trade for information reasons in the preopen period.

Markov Switching and Long Memory: A Monte Carlo Analysis

This article finds the close relationship between long memory and some forms of Markov-switching models. The simulation results suggest: (1) when the transition probabilities are closer to unity, it is more likely to generate long memory process; (2) magnitude of regime-switching plays an important role in generating long memory; and (3) process with switching in variance (disturbance) is much less likely to explain long-memory process than switching in mean (intercept) and autoregressive coefficient. Therefore, given the observed high persistence in financial volatility data, volatility modelling by switching in mean and AR coefficient is preferred to that by switching in variance.

A SECURITISED MARKET FOR HUMAN CAPITAL

This paper proposes a simple finance innovation with an income-contingent repayment system to supplement our current fixed-interest rate student loan system. Income-contingent repayments could be pooled and securitised while lenders could sell these lifetime equity-like human capital securities to investors who seek to diversify their existing portfolios. Without increasing the governments fiscal burden, this innovation would significantly reduce entry barriers facing finance-constrained college students in a continually rising cost environment.