Time‐varying impact of housing price fluctuations on banking financial risk

2021 ◽  
Author(s):  
Jingbin Wang ◽  
Beibei Xia ◽  
Huiling Qiao
Keyword(s):  
Financial Risk ◽  
Housing Price ◽  
Time Varying ◽  
Price Fluctuations

Housing price fluctuations and financial risk transmission: a spatial economic model

2019 ◽  
Vol 51 (53) ◽  
pp. 5767-5780
Author(s):  
Fengyun Liu ◽  
Honghao Ren ◽  
Chuanzhe Liu
Keyword(s):  
Economic Model ◽  
Financial Risk ◽  
Housing Price ◽  
Price Fluctuations

scholarly journals Urban Housing Price Fluctuations and Regional Systemic Financial Risks: Panel Spatial Economic Models in Jiangsu, China

2018 ◽  
Vol 10 (10) ◽  
pp. 3452
Author(s):  
Fengyun Liu ◽  
Chuanzhe Liu ◽  
Honghao Ren
Keyword(s):  
Real Estate ◽  
Local Governments ◽  
Financial Risk ◽  
Housing Prices ◽  
Housing Price ◽  
Economic Models ◽  
Urban Housing ◽  
Financial Risks ◽  
Price Fluctuations ◽  
Contagion Effects

The regional systemic financial risks driven by escalating urban housing prices have been of great concern recently. Based on the theoretical analyses on the mechanism of formation of regional systemic financial risk driven by urban housing price fluctuations, this paper builds panel spatial economic models to empirically analyze the relationship between urban housing price fluctuations and regional systemic financial risks, in addition to their spatial linkages, in 13 cities in Jiangsu, a representative province of China. The empirical results show the following. (1) The excessive investment or speculation of local governments, banks, real estate developers, individuals, and families on the housing market stimulate the escalation in urban housing prices, leading to the systemic financial risks; (2) Urban housing prices and the land supply price of local governments have strong spatial contagion effects among cities, which will diffuse risks to adjacent cities, causing regional systemic financial risk; (3) Compared with North Jiangsu, South Jiangsu has more serious investment expansion from real estate developers and stronger spatial contagion effects, suggesting the existence of heavier regional systemic financial risks derived from housing price fluctuations; (4) North Jiangsu has slightly stronger “imitative behavior” among local governments, and fewer “substitution effects” of central cities’ demand to adjacent cities’ demand than does South Jiangsu.

scholarly journals Can RMB Exchange Rate Expectations Explain the Fluctuations of China’s Housing Prices?

2020 ◽  
pp. 211-233
Author(s):  
Chunni Wang
Keyword(s):  
Exchange Rate ◽  
Housing Prices ◽  
Housing Price ◽  
Chinese Government ◽  
Rmb Exchange Rate ◽  
Price Fluctuations ◽  
Exchange Rate Expectations ◽  
The Exchange Rate ◽  
The U.S ◽  
Rate Expectation

Unlike existing literature that has focused on the relationship between exchange rate and housing price, this paper studies the housing price fluctuations from the perspective of RMB exchange rate expectation to resolve the dilemma “guarantee housing price or exchange rate” after the sub-prime mortgage crisis. This paper shows that housing prices responded negatively to RMB appreciation expectation from 1999 to 2008, and positively from 2009 to 2019. After 2009, exchange rate expectation is the Granger causality of housing prices. After introducing the U.S. Economic Policy Uncertainty (EPU) released by Baker et al.(2016), the explanatory power of exchange rate expectations to housing price fluctuations declines but it's still significant. When EPU increased, housing prices responded negatively after a brief positive response. Besides exchange rate expectation, several unobservable factors with rich economic implications can explain the fluctuations of housing prices in China in the interval of 2006M01–2018M12. The empirical results show that the degree of Chinese government reversal intervention, interest rate spread between China and the U.S., and EPU can explain the exchange rate expectation. The government can control the degree of reversal intervention to affect the exchange rate expectation and realize the housing price control indirectly.

COMPUTATIONAL ECOSYSTEMS IN A CHANGING ENVIRONMENT

1991 ◽  
Vol 02 (03) ◽  
pp. 735-753 ◽  
Author(s):  
NATALIE GLANCE ◽  
TAD HOGG ◽  
BERNARDO A. HUBERMAN
Keyword(s):  
Adaptive Behavior ◽  
Time Varying ◽  
Price Fluctuations ◽  
Load Variations ◽  
Payoff Functions ◽  
Computational Ecosystem ◽  
Time Varying Environment ◽  
Time Periodic ◽  
Varying Environment ◽  
Do So

We study the adaptive behavior of a computational ecosystem in the presence of time-periodic resource utilities as seen, for example in the day-night load variations of computer use and in the price fluctuations of seasonal products. We do so within the context of the Huberman-Hogg model of such systems. The dynamics is studied for the cases of competitive and cooperative payoff functions with time-modulated resource utilities, and the system’s adaptability is measured by tracking its performance in response to a time-varying environment,

scholarly journals Dynamic Connectivity in a Financial Network Using Time-Varying DCCA Correlation Coefficients

2021 ◽  
Vol 6 (1) ◽  
pp. 57-75
Author(s):  
Paulo Ferreira ◽  
Oussama Tilfani ◽  
Éder Pereira ◽  
Cleónidas Tavares ◽  
Hernane Pereira ◽  
...  
Keyword(s):  
Financial Risk ◽  
Correlation Coefficients ◽  
Subprime Crisis ◽  
Time Varying ◽  
Eurozone Crisis ◽  
Sliding Windows ◽  
Cross Correlation Analysis ◽  
Dynamic Connectivity ◽  
Over Time ◽  
Do So

Abstract This paper aims to analyse the connectivity of 13 stock markets, between 1998 and 2019, with a time-varying proposal, to evaluate evolution of the linkage between these markets over time. To do so, we propose to use a network built based on the correlation coefficients from the Detrended Cross-Correlation Analysis, using a sliding windows approach. Besides allowing for analysis over time, our approach also enables us to verify how the network behaves for different time scales, which enriches the analysis. We use two different properties of networks: global efficiency and average grade, to measure the network’s connectivity over time. We find that the markets under analysis became more connected before the subprime crisis, with this behavior extending even after the Eurozone crisis, showing that during extreme events there is an increase in financial risk, as found in the international literature.

scholarly journals How Does Capital Account Liberalization Affect Systemic Financial Risks? Evidence from China

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhiyong Zheng ◽  
Jian He ◽  
Yang Bian ◽  
Chen Feng ◽  
Mengting Zhang
Keyword(s):  
Financial Risk ◽  
Capital Inflows ◽  
Credit Crunch ◽  
Capital Account ◽  
Financial Risks ◽  
Time Varying ◽  
Structural Vector Autoregression ◽  
Capital Account Liberalization ◽  
Macroeconomic Risk ◽  
Short Run

Capital account liberalization typically results in higher volumes of capital inflows and outflows for a country, yet abnormal cross-border capital flows may lead to overall financial risk accumulation, in turn causing tremendous damages to the economy. Using a time-varying parameter structural vector autoregression model with stochastic volatility (SV-TVP-SVAR), we identify time-varying effects of capital account liberalization on four types of systemic financial risks in China. Empirical results demonstrate that capital account liberalization, in the short run, can effectively curb the accumulation of macroeconomic and sudden stop risks. On the other hand, capital account liberalization may heighten credit crunch and asset bubble risks to varying degrees. We also find that some important capital account liberalization measures are double-edged: reform policies are likely to increase macroeconomic risk when optimizing the financing structure and reducing credit crunch risk.

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