Haiqiang Chen

Generic Consistency of the Break-Point Estimators Under Specification Errors in a Multiple-Break Model

This paper considers the estimation of multiple-structural-break models under specification errors. A common example in economics is that the true model is measured in level, but a linear-log model is estimated. We show that, under specification errors, if there are more than one break points and if a single-break model is estimated, the estimated break point is consistent for one of the true break points. This consistency result applies to models with multiple regressors where some or all of the regressors are misspecified. Another important contribution of this paper is that we have constructed a Sup-Wald test whose limiting distribution is not affected by model misspecification. Using this robust test, we show that the break points can be estimated sequentially one at a time. Simulation evidence and an empirical application are provided. Copyright

A principal-component approach to measuring investor sentiment

The usefulness of the investor sentiment measure in the stock market has received increasing attention in recent years. Various measures of investor sentiment have been proposed over the last two decades. Early studies, such as Lee et al. (1991), Swaminathan (1996) and Neal and Wheatley (1998), use closed-end fund discounts as a proxy for market sentiment. Since both closed-end funds and small stocks are mainly owned by individual investors, the sentiment affects the returns of small stocks in the same way as it influences closed-end funds. Otoo (1999) studies the survey-based Michigan consumer confidence index and finds a strong positive relationship between consumer sentiment and stock prices. Kumar and Lee (2002) construct a measure of individual investor sentiment based on the buy–sell imbalance and show that it has incremental explanatory power for small stocks, value stocks and stocks with low institutional ownership. Fan and Yao (2003) propose a composite index which contains the information of various market indicators.x Brown and Cliff (2004) use a vector autoregressive framework to explain the mutual influence between the sentiment measure, market returns and volatility. It is shown that investor sentiment measures have little explanatory power for short-term stock returns. Baker and Wurgler (2006) construct a measure of stock market sentiment based on the common variation of the closed-end fund discount, NYSE share turnover, the number of IPOs, the average first-day returns on IPOs, equity share in new issues and dividend premium. They show that stocks with highly subjective valuations and

Are Chinese Stock Market Cycles Duration Independent

This paper studies the duration properties of the Chinese stock market cycle. We find evidence for duration dependence in both A-share and B-share markets for whole cycles. The results reject the random-walk hypotheses for both markets. For half cycles, evidence of duration dependence for expansions in the Shanghai A-share market is found. For the Shenzhen B-share market, there is little evidence of duration dependence for half cycles. Although the B-share market is less liquid as compared to the A-share market, the results of this study suggest that the B-share market is more efficient than the A-share market. An important implication is that the quality of market participants plays an important role in the duration property of the Chinese stock market.

An investigation of duration dependence in the American stock market cycle

This paper investigates the duration dependence of the US stock market cycles. A new classification method for bull and bear market regimes based on the crossing of the market index and its moving average is proposed. We show evidence of duration dependence in whole cycles. The half cycles, however, are found to be duration independent. More importantly, we find that the degree of duration dependence of the US stock market cycles has dropped after the launch of the NASDAQ index.

Estimation and Inference for Varying-coefficient Models with Nonstationary Regressors using Penalized Splines

This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel-based methods, but enjoys computation advantages. Utilizing the mixed model representation of penalized splines, we develop a likelihood ratio test statistic for checking the stability of the regression coefficients. We derive both the exact and the asymptotic null distributions of this test statistic. We also demonstrate its optimality by examining its local power performance. These theoretical findings are well supported by simulation studies.

An empirical study on the threshold cointegration of Chinese A and H cross-listed shares

We investigate the dynamic relationship between the prices of Chinese A and H market cross-listed shares using the Enders–Siklos threshold cointegration approach. Our data are the daily closing prices of the Hang Seng China AH (A) index and the Hang Seng China AH (H) index from 4 January 2006 to 1 November 2013. We find a threshold cointegration between these two indices, instead of the linear cointegration well established in the literature. The short-term adjustment to the equilibrium shows an asymmetric effect according to the price deviation from the equilibrium. Moreover, using a Granger causality test, we find a bi-directional causality between these two markets, indicating a close relationship between them. A pairs trading rule, based on the estimated threshold cointegration model, demonstrates the usefulness of our results as it generates a significantly higher return than a naive buy-and-hold trading rule.

Robust Estimation and Inference for Threshold Models with Integrated Regressors

This paper studies the robust estimation and inference of threshold models with integrated regressors. We derive the asymptotic distribution of the profiled least squares (LS) estimator under the diminishing threshold effect assumption that the size of the threshold effect converges to zero. Depending on how rapidly this sequence converges, the model may be identified or only weakly identified and asymptotic theorems are developed for both cases. As the convergence rate is unknown in practice, a model-selection procedure is applied to determine the model identification strength and to construct robust confidence intervals, which have the correct asymptotic size irrespective of the magnitude of the threshold effect. The model is then generalized to incorporate endogeneity and serial correlation in error terms, under which, we design a Cochrane-Orcutt feasible generalized least squares (FGLS) estimator which enjoys efficiency gains and robustness against different error specifications, including both I(0) and I(1) errors. Based on this FGLS estimator, we further develop a sup-Wald statistic to test for the existence of the threshold effect. Monte Carlo simulations show that our estimators and test statistics perform well.

Estimation and Inference of Threshold Regression Models with Measurement Errors

Abstract An important assumption underlying standard threshold regression models and their variants in the extant literature is that the threshold variable is perfectly measured. Such an assumption is crucial for consistent estimation of model parameters. This paper provides the first theoretical framework for the estimation and inference of threshold regression models with measurement errors. A new estimation method that reduces the bias of the coefficient estimates and a Hausman-type test to detect the presence of measurement errors are proposed. Monte Carlo evidence is provided and an empirical application is given.

Synchronous Price Discovery of Cross-Listings

Extending from Grossman and Stiglitz (1980), we provide an asset pricing model of a synchronously traded crosslisted pair under information asymmetry. Following Garbade and Silber (1983), the model further embraces multimarket price discovery in a dynamic framework. The implications are as follows: The price sensitivity of holdings is higher for informed traders than for uninformed traders; the largest cross-border price spread occurs in the absence of arbitrageurs; price discovery is more likely in markets with a larger population of informed traders; and parity convergence accelerates with a higher price elasticity of demand of arbitrageurs.