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Purpose – The purpose of this paper is to examine recent developments pertaining to China’s shadow banking sector. Shadow banking has the potential not only to be a beneficial contributor to continued economic growth, but also to contribute to systematic instability if not properly monitored and regulated. An assessment is made in this paper as to whether shadow banking is beneficial or harmful to China’s economic growth. Design/methodology/approach – The authors start with providing an overview of shadow banking from a global perspective, with information on its recent growth and importance in selected countries. The authors then focus directly on China’s shadow banking sector, with information on the various entities and activities that comprise the sector. Specifically, the authors examine the interconnections between shadow banking and regular banking in China and the growth in shadow banking to overall economic growth, the growth in the money supply and the growth in commercial bank assets. Findings – Despite the wide range in the estimates, the trend in the size of shadow banking in China has been upward over the examined period. There are significant interconnections between the shadow banking sector and the commercial banking sector. Low deposit rate and high reserve requirement ratios have been the major factors driving its growth. Shadow banking has been a contributor, along with money growth, to economic growth. Practical implications – The authors argue that shadow banking may prove useful by diversifying China’s financial sector and providing greater investments and savings opportunities to consumers and businesses throughout the country, if the risks of shadow banking are adequately monitored and controlled. Originality/value – To the authors’ knowledge, this paper is among the few to systematically evaluate the influence of shadow banking on China’s economic growth.

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We propose factor-based out-of-sample forecast models for the financial stress index and its 4 sub-indices developed by the Bank of Korea. We employ the method of the principal components for 198 monthly frequency macroeconomic data to extract multiple latent factors that summarize the common components of the entire data set. We evaluate the out-of-sample predictability of our models via the ratio of the root mean squared prediction errors and the Diebold-Mariano-West statistics. Our factor models overall outperform the random walk model in forecasting the financial stress indices for up to 1-year forecast horizon. Our models also perform fairly well relative to a stationary autoregressive model especially when the forecast horizon is short, which is practically useful because financial crises often occur abruptly with no systemic warning signals. Parsimonious models with small number of factors perform as well as bigger models. Overall, our findings imply that not only financial data but also real activity variables can help out-of-sample forecast the vulnerability in the financial markets.