Abstract
This paper explores nonlinear cointegration between Chinese mainland stock markets and Hong Kong stock market in a multivariate framework for the period January, 1998 to December, 2014 by a nonparametric method. The local linear kernel smoothing method is developed to estimate the unknown function, and the practical problem of implementation is also addressed. Then, a simple nonparametric version of a bootstrap test is adapted for testing misspecification. Furthermore, Some Monte Carlo experiments are presented to examine the finite sample performance of the proposed procedure. Finally, the stock markets data set is discussed in detail by using proposed procedures, showing that Shanghai Stock Index (SHSI) and Shenzhen Component Index (SZCI) can affect Hang Seng Index (HSI), and the influence appears to be a strong nonlinear characteristics.
Highlights
China is one of the fastest-growing economies in the world
Similar to Cai and Tiwari (2000) and Cai (2007), we suggest a nonparametric version of Akaike Information Criterion (AIC) to select the bandwidth matrices
For a predetermined sequence of H’s from a wide square range, say both horizontal axis and vertical axis from 0.1 to 0.7 with an increment 0.025, based on the AIC bandwidth selector described in Section 2.2, we compute AICðHÞ for each H and choose Hopt to minimize AICðHÞ: The performance of the proposed estimators is evaluated by the mean absolute deviation error (MADE)
Summary
China is one of the fastest-growing economies in the world. According to International Monetary Fund (IMF) and purchasing power parity (PPP), Chinese economy is the second largest in the world by nominal GDP. The relationship between stock prices and macroeconomic variables has been discussed all over the world. We investigate the local linear estimation of a nonparametric multivariate cointegration model of the form. Wang and Phillips (2009b) investigated model (1) with endogeneity, i.e., there is dependence between the nonstationary regressor Xt and the stationary error ut They show that the local constant kernel estimator is consistent and the limit distribution is mixed normal. Model (1), and derived the properties of the local linear estimator in the nonstationary setting They considered a data-driven method to select the bandwidth, and found a drastic improvement of the mean squared errors of the local linear estimator compared to the local constant estimator.
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