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1

Extreme risk spillovers between China and major international stock markets

100%

Qian L., Jiang Y., Long H.

Modern Finance

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2023

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vol. 1

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issue 1

30-34

EN

We examine the complex dependence structure and risk spillovers between the Chinese stock market and twelve major international markets. To this end, we employ three types of vine copulas and tests for the Granger causality in risk of Hong et al. (2009). The results indicate that the R-vine copula is the optimal model to characterize the high-dimensional dependence structure of the markets after China joined the WTO, which suggests obvious structural differences with varying degrees of mainly positive dependences. Moreover, we identify unilateral extreme risk spillovers from China to the United States, France, and Germany, and either from Japan to China. We also detect bilateral spillovers between China and the United States, Japan, as well as Australia.

2

What drives the dependence between the Chinese and global stock markets?

100%

Qian L., Jiang Y., Long H.

Modern Finance

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2023

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vol. 1

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issue 1

12-16

EN

By applying time-varying copulas and panel regression analysis, this study investigates the dependence between the Chinese and eleven international stock markets, as well as its determinants during the period 2002-2018. Our results indicate that the dependence magnitude between the Chinese stock market and major international markets varies with region. Furthermore, the dependence is negatively driven by both economic policy uncertainty differentials and interest rate differentials while positively affected by the global financial crisis and trade interdependence. Our findings are of great importance to international investors and policymakers.

3

Forecasting the equity premium: Do deep neural network models work?

100%

Zhou X., Zhou H., Long H.

Modern Finance

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2023

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vol. 1

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issue 1

1-11

EN

This paper constructs deep neural network (DNN) models for equity-premium forecasting. We compare the forecasting performance of DNN models with that of ordinary least squares (OLS) and historical average (HA) models. The DNN models robustly work best and significantly outperform both OLS and HA models in both in- and out-of-sample tests and asset allocation exercises. Specifically, DNN models generate monthly out-of-sample R2 of 3.42% and an annual utility gain of 2.99% for a mean-variance investor from 2011:1 to 2016:12. Moreover, the forecasting performance of DNN models is enhanced by adding additional 14 variables selected from finance literature.

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Extreme risk spillovers between China and major international stock markets

What drives the dependence between the Chinese and global stock markets?

Forecasting the equity premium: Do deep neural network models work?