We examine the interest rate pass-through in Hong Kong (HK) and Macao to see if the monetary policy transmission mechanism has been impaired since the Global Financial Crisis (GFC). Our results show that, in the post-GFC period, both the long-run and short-run interest rate pass-through from policy rates to prime rates have disappeared in Macao and weakened significantly in HK. The long-term relationship between deposit rates and policy rates no longer exists in either market while the short-term relationship has been reduced significantly. The results indicate that the effectiveness of the monetary policy in HK and Macao has been seriously undermined after the GFC and alternative monetary policy tools were needed.

There is mixed evidence of a positive relationship between the stock market risk and return. We reexamine this critical implication of asset pricing theory using fresh data from China's stock market, which is largely segmented from the rest of the global financial market. Using formal variable selection methods and a comprehensive set of predictor variables, we identify conditional market variance, scaled market prices, and inflation as crucial determinants of equity premiums. The estimated simple risk-return relationship exhibits downward omitted variable bias, which underlines the importance of considering multiple factors to explain the variation in equity premiums. We cannot wholly attribute the three-factor conditional equity premium model to data mining, as Guo, Sanni, and Yu (2022) select the same model for the U.S. stock market. These findings challenge existing asset pricing models and provide valuable guidance for future theoretical research.

Secular declines in global sovereign yields are concentrated in short event windows around U.S. monetary policy announcement dates. Cumulative changes in sovereign yields during FOMC announcement dates contain critical information for explaining the persistent variations in the yields, predicting future yields and excess bond returns, and determining interest rate expectations and term premia. We build a dynamic term structure model with shifting endpoints to study the effects of U.S. monetary policy on world yield curves. Our findings highlight that U.S. monetary policy drives the secular declines in global interest rates by reducing expected interest rates.

Secular declines in U.S. Treasury yields are concentrated in three-day windows around FOMC announcement dates. Cumulative yield changes during these short windows explain the secular decline in yields. This factor contains essential information on excess bond returns and outperforms well-known proxies for interest rate trends in prediction regressions. We estimate a dynamic term structure model to explain these empirical facts. The model suggests that the secular declines in Treasury yields over the past three decades were primarily due to reductions in expected interest rates, mostly during the FOMC announcement windows.

This paper researches into development of China’s shadow banking during 2020-2022, a special period marked by COVID-19 and strengthened global regulation on Non-Bank Financial Intermediation (NBFI). Research focus includes balance sheet evolvement, growth dynamics, and relation with macro-finance. Its business model surprisingly resembles western peers. They both fund underserved sectors and have similar exposure to balance sheet mismatch. Massive holding of bond investment (36.6% of total asset) is funded by uninsured interbank fund and wealth management product, which makes it more closely related with banks’ balance sheet and risk contagion from NBFI to traditional commercial banks more easily. This paper then re-summarizes growth dynamics of China’s shadow banking in a “Pull-Push” framework, and proposes concept of reintermediation in respective to disintermediation. Consecutive regulation on NBFI and real estate sector kept dragging on growth of shadow banking, and rendered it in liquidity surplus, which is invested into interbank market. This paper also provides empirical evidence on relation of China’s shadow banking with macro-finance, and notes several empirical breakdowns of pre- COVID relations among economic and financial indicators. Most important breakdown is the non-functionality of monetary policy transmission channel. Besides, it continued to twist de facto financial regulatory indicators, however with fading impact.

This paper employs Autoregressive Distributed Lag (ARDL) models and monetary base growth to construct an exogenous and comprehensive monetary policy measure in China, where various monetary policy instruments co-exist, and the operational and intermediate targets are changing over time. Our methodology relies on the market equilibrium relationship instead of ad hoc policy rules and strict identiffcation assumptions, hence is robust to monetary policy frameworks in any economy. The empirical results show that the active monetary base growth (AMBG ) constructed via the ARDL models is an excellent description of the behavior of People’s Banks of China across time, and generates impacts on macro variables consistent with implications of macro theory when used in VAR analyses.

We examine how the stock market returns react to the Fed’s monetary policy in an environment where there are restrictive capital controls at the country level. We document an almost opposite stock return pattern in China over the Federal Open Market Committee (FOMC) announcement cycle. Unlike the United States’ higher returns on even weeks, stock market returns are statistically and economically higher on weeks 1, 2, 3, and 5. We link the pattern to the Fed and recover a perfectly opposite odd-week regularity in sub-sample analysis. The opposite return regularities suggest that the Fed’s monetary policies have opposite impacts on the equity risk premia of the U.S. and China over the FOMC cycle. Further investigation of fund flows reveals the realized stock returns are aligned with fund flows. In particular, the international capital -the Northbound funds - invest less when the realized returns are high, and their trading flows can predict stock returns.

Central bank can use foreign exchange intervention and monetary policy tools to intervene in the currency markets,and trigger sharp changes in prices and volatility.The interventions bring huge risks to the financial market that are often neglected by risk managers and finance literature. In this paper, we use the daily information in the Chinese Yuan currency Option from 2013 to 2017 to examine the impact of price jump behavior on risk prediction.We first test for the presence of jumps by analyzing the higher moment behavior of CNYUSD exchange rate,interest rate and option implied variance. The result provides strong evidence supporting the presence of jumps in risk factors of option. This implies that PBoC interventions can be a source of jumps in returns and volatility. Next, we compares the risk prediction performance of alternative GARCH models with different conditional distribution specifications. The empirical results for the Chinese Yuan currency Option portfolio suggest that the jump model outperforms all other models,and feedback effects of jump ,long memory, and leverage effects are important to improve forecasts of risk.

In the era of big data, asset pricing is influenced by various factors, which are extracted from multi-source heterogeneous information, such as high frequency market and sentiment information, low frequency firm characteristic and macroeconomic information. Especially, low frequency policy information plays a significant role in the long-term pricing in China but it is barely investigated due to its textual form. To this end, we first extract policy variables from major national development plans (“Five-Year Plans”, “Government Work Reports”, and “Monetary Policy Reports”) using Natural Language Processing (NLP) technique and Dynamic Topic Model (DTM). However, traditional models are inadequate for mixed frequency data modeling and feature extraction. Then, we propose a mixed frequency deep factor asset pricing model (MIDAS-DF) that solves the asset pricing problems under the mixed frequency data environment through mixed data sampling (MIDAS) technique and deep learning architecture. Time-varying latent factors and factor loadings can be modeled from mixed frequency data directly in a nonlinear and data-driven way. Thus, the MIDAS-DF model is able to learn the nonlinear joint-patterns hidden in multi-source heterogeneous information. Our empirical studies of 4939 stocks on the Chinese A-share market from January 2003 to July 2022 demonstrate that low frequency policy information has profound impacts on asset pricing, which anchors the long-term pricing direction, and high frequency market and sentiment information have significant influences on stock prices, which optimize the short-term pricing accuracy, they together enhance the pricing effects. Consequently, pricing effects the MIDAS-DF model outperform the five competing models on individual stocks, various test portfolios, and investment portfolios. Our research about heterogeneous information provides implications to the government and regulators for decision-support in policy-making and our investment portfolio is of great importance for investors’ financial decisions.

We document that monetary policy has asymmetric effects on investments by large and small firms in China. Large firms’ investment are highly responsive to monetary expansions, but less affected by monetary contractions. In contrast, small firms’ investments are less responsive to monetary expansions, but significantly affected by monetary contractions. We argue that this asymmetric responses of large and small firms stem from their differential access to credits in a two-tiered banking system. Large firms borrow from the big state-owned banks, which have a strong depositor base, whereas small firms borrow mainly from small banks which does not have a large depositor base and therefore rely heavily on the inter-bank market for financing their loans to small firms. We build a DSGE model with heterogeneous banks, heterogeneous firms, and an inter-bank market that is calibrated to the Chinese data. We show that the model’s quantitative predictions about the effects of monetary policy on large and small firms are consistent with the facts we documented.