The asymmetric dissemination of information among financial firms in the financial market reflects their asymmetric response to the dissemination of both positive and negative information. However, it is worth studying whether this asymmetry will intensify or alleviate under different financial market conditions. Based on high-frequency minute stock price data of Chinese A-share listed financial firms from July 2020 to July 2023, we decompose the good and bad information, as well as the positive and negative volatility information in the return series. We utilize the quantile cross-spectral correlation method to construct an information overflow network at monthly intervals. We use the MVMQ-CAViaR model to estimate the value at risk (VaR) for various quantiles and build a risk spillover network that incorporates both positive and negative tail risk information, using the quantile dynamic SIM-COVAR-TENET model. We calculated the network dissemination efficiency of both good and bad information, including average speed, speed deviation, densest speed, and depth, to explore the changes in the asymmetry of good and bad information dissemination under different financial market conditions. We get that when the financial market is booming, financial firms’ asymmetric response to good and bad information will increase, and the firms will pay more attention to bad information. When the financial market declines, the asymmetric response of financial firms to good and bad information is diminished, and their sensitivity to both positive and negative information is heightened. In addition, the dissemination of bad information by firms in the five sub-financial industries across various markets exacerbates the asymmetric response of other financial firms to good and bad information. More importantly, the release of positive return information, negative volatility information, and highly negative tail risk information by the real estate financial firms all impact the asymmetric response of financial firms to good and bad information in a prosperous financial market. In recessionary financial markets, financial regulators can strategically release positive information to mitigate the decline in the financial market. Conversely, in a booming financial market, financial regulators should be cautious of the negative impact that bad information can have on financial firms, particularly in relation to the excessive growth of the real estate sector and the potential chain reaction of significant adverse events.

This paper aims to empirically explore the cyclical attributes of dynamic correlation shifts between the stock and real estate market, and the factors that influence this correlation during different periods of the economic cycle. Our research uncovers a significant structural shift in the correlation towards the end of 2012. By taking into account macroeconomic growth, regulatory policies, financial market conditions, and developments within both the stock and real estate markets, we investigate the time-varying characteristics of these factors' influence. The results highlight the pronounced cyclical asymmetry of these influential factors. Currently, the wealth effect in China's stock and real estate markets has significantly diminished, and the credit-price effect has vanished. A marked seesaw relationship is evident between the two markets. This outcome supports that various restrictions imposed on the real estate market have reduced its investment appeal.

The asymmetric dissemination of information among financial firms in the financial market reflects their asymmetric response to the dissemination of both positive and negative information. However, it is worth studying whether this asymmetry will intensify or alleviate under different financial market conditions. Based on high-frequency minute stock price data of Chinese A-share listed financial firms from July 2020 to July 2023, we decompose the good and bad information, as well as the positive and negative volatility information in the return series. We utilize the quantile cross-spectral correlation method to construct an information overflow network at monthly intervals. We use the MVMQ-CAViaR model to estimate the value at risk (VaR) for various quantiles and build a risk spillover network that incorporates both positive and negative tail risk information, using the quantile dynamic SIM-COVAR-TENET model. We calculated the network dissemination efficiency of both good and bad information, including average speed, speed deviation, densest speed, and depth, to explore the changes in the asymmetry of good and bad information dissemination under different financial market conditions. We get that when the financial market is booming, financial firms’ asymmetric response to good and bad information will increase, and the firms will pay more attention to bad information. When the financial market declines, the asymmetric response of financial firms to good and bad information is diminished, and their sensitivity to both positive and negative information is heightened. In addition, the dissemination of bad information by firms in the five sub-financial industries across various markets exacerbates the asymmetric response of other financial firms to good and bad information. More importantly, the release of positive return information, negative volatility information, and highly negative tail risk information by the real estate financial firms all impact the asymmetric response of financial firms to good and bad information in a prosperous financial market. In recessionary financial markets, financial regulators can strategically release positive information to mitigate the decline in the financial market. Conversely, in a booming financial market, financial regulators should be cautious of the negative impact that bad information can have on financial firms, particularly in relation to the excessive growth of the real estate sector and the potential chain reaction of significant adverse events.

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.

Using the regression discontinuity design, a quasi-experiment approach, this paper establishes a causal relationship between the down payment requirement and house prices by exploiting a unique institutional background in Shanghai. In the unique setting, the required minimal down payment ratio jumps at the Inner Ring, a circular elevated highway, from 50% to 70% for a large group of buyers. With transaction level data from the largest real estate broker in Shanghai, we find that a lower required down payment ratio increases the apartment price by 138.8 thousand RMB, around 3.71% of the average transaction price.

This study contributes to the understanding of the relationship between Chinese local government debt and house prices by proposing the land channel as a novel explanatory framework. We construct a three-sector equilibrium model and demonstrate that local government debt positively affects house prices through both direct and indirect effects, with the indirect effect operating through the land market. However, the land use efficiency mitigates the positive effect of government debt on land and house prices within indirect effect. These propositions are empirically confirmed using a panel dataset of 260 cities in China from 2011 to 2019.

This paper reviews the current housing crisis in China and explores the roles of supply-demand imbalances and local governments in the real estate sector. To prevent the housing downturn from further dragging down economic growth, Beijing suspended the financing restrictions on developers imposed in August 2020. These restrictions, known as the “three red lines” that limited new borrowing by developers, led Chinese property developers to default on a record number of debt obligations and triggered the most serious housing slump China has seen since 1998. The property sector saw its value added decline by more than 5 percent in 2022, even as the overall economy grew at 3 percent. But the current dynamics in the housing market reflect a repeated pattern: Loosening financing restrictions on developers and using housing as a macroeconomic stabilization tool risks reinforcing the boom-bust housing cycle. China’s real estate sector is a systemic problem. Without serious reforms to address concerns such as supply-demand imbalances and local governments’ deep connections with real estate, housing slumps like the one in 2022 may recur.

We examine the roles of bank ownership and CEO political faction membership in facilitating or hindering the implementation of central bank policy in China. Specifically, we examine the response of China’s commercial banks to People’s Bank of China (PBC) guidelines intended to decrease mortgage lending and to slow down the rise in residential property prices. We find that both bank ownership and faction membership matter. Central government-owned banks whose CEOs are members of the specialist finance faction within the Chinese Communist Party (CCP) respond most strongly to PBC guidance, whereas provincial or city government-owned banks whose CEOs are members of a generalist faction respond least strongly. The implementation of PBC policy has real effects: in those cities where central government-owned banks with specialist CEOs constitute a larger percentage of total bank branches, house prices grew more slowly, as did the number of residential real estate transactions and the number of new listings. Where in contrast provincial and city government-owned banks with generalist CEOs dominate, the number of transactions grew faster; the rate of house price appreciation and the number of listings were however unaffected. We conclude that China’s different levels of government and the CCP’s different factions enjoy some discretion in responding to PBC guidance and that they exploit the discretion they are afforded to vary the strength of their response.

This paper provides new estimates of the housing stock, construction rates and price developments by city tier in China in order to understand where excess supply might be concentrated, and the implications of any significant contraction. We also update estimates of the size of China’s rapidly evolving real estate sector through 2021, allowing one to look at the initial impact of COVID-19, as well as extending the analysis to incorporate urban-expansion related infrastructure construction. We argue that China overall faces imbalances between supply and demand for housing stock, but the problem is significantly deeper in the generally smaller and lower income tier 3 cities, which nevertheless account for more than 60% of both China’s GDP and its housing stock.

Referring to the newspaper textual analysis method by Baker et al. (2016), this study constructs a monthly Chinese Real Estate Policy Uncertainty (REPU) index from 2001 to 2018. The index increases significantly near the promulgation of major policies. We also conduct evaluation of the index with the vector autoregression (VAR) model, which reveals that the rise of REPU indicates the decline in the growth rate of commodity housing development investment, sales area, and real estate industry added value. The REPU index is helpful to expand the understanding of policy uncertainty, and the accurate measurement of REPU is the basis for further research of its impact on China's real estate market.