Did bubbles migrate from the stock to the housing market in China between 2005 and 2010?

Deng, Yongheng; Girardin, Éric; Joyeux, Roselyne; Shi, Shuping

doi:10.1111/1468-0106.12230

Cited by 45 publications

(10 citation statements)

References 90 publications

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“…and that for the conditional variance of the return is (6) where r x represents the house price return of the investigated city. r Mac represents the growth rate of macroeconomic variables, including money supply, short-term rate, CPI and loan balance.…”

Section: The Estimation For Virfmentioning

confidence: 99%

Risk Transfer among Housing Markets in Major Cities in China

Tsai

Chiang

2018

Sustainability

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This study explored risk transfer among the housing markets of five major cities in China, comprising three first-tier cities (i.e., Beijing, Shanghai, and Shenzhen) and two second-tier cities (i.e., Tianjin and Chongqing). House price index data from January 2001 to June 2017 and a vector autoregressive-multivariate generalized autoregressive conditional heteroscedasticity model were employed to estimate correlations among these cities related to house price returns and volatility. In addition, volatility impulse-response functions were estimated to determine interactions among housing market risk in different cities. The results revealed that first-tier cities were more likely to transfer risk to second-tier cities, and that Beijing's housing market exerted the greatest influence on risk in other cities' housing markets. To consider the influence of the 2008 global financial crisis, data collected before and after the crisis were divided into two groups for subsequent investigation. The results revealed that these cities became more closely interrelated after the financial crisis, thereby escalating the risk of impulse influences. Finally, this study evaluated the influences of macroeconomic impulses on the housing markets of the three first-tier cities, indicating that real estate in these three cities can protect investors against inflation. The evidence presented in this paper can serve as a reference for the Chinese government regarding risk control.

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Section: The Estimation For Virfmentioning

confidence: 99%

Risk Transfer among Housing Markets in Major Cities in China

Tsai

Chiang

2018

Sustainability

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“…Deng et al in a study to investigate whether bubbles migrated from stock to housing market in China between 2005 and 2010 used the recursive explosive-root method proposed by Phillips et al to detect and date speculative bubbles in both markets [21]. The study again proceeded to implement the Greenaway-McGrevy and Phillips test to detect whether there was migration between the two types of bubbles.…”

Section: Testing Real Estate Bubblesmentioning

confidence: 99%

Investigating the Existence of a Bubble in the Kenyan Real Estate Market

Njoroge¹

2018

IJDSA

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In the Kenyan real estate industry, laws of economics seem to be violated. The demand for houses has been increasing tremendously despite the oversupply. This violates the laws of economics indicating a possibility of a real estate bubble. The study aimed at estimating short term and long term real estate price dynamics in Kenya using co-integration tests. Secondly, the study aimed at identifying the presence of a Kenyan real estate bubble using the forward-recursive Generalized Augmented Dickey-Fuller test (GSADF) and finally measured the size of the bubble at a given time relative to other key macroeconomic variables. The study utilized quarterly data on house prices and rental prices in Kenya and macroeconomic determinants from the year 2004 to 2017 September. Stationarity test revealed that the variables were stationary in their first difference I (1). Cointegration test revealed that there was no long term and short term house price dynamics between house prices and the macroeconomic variables at a lag of 4 determined through AIC, SIC and HQ criterion. Again a Granger causality test was performed and the results revealed that the macroeconomic variables did not Granger-cause house prices and vice versa. To investigate the presence of a Kenyan real estate bubble, cointegration test, and GSADF were performed and the results indicated the existence of a bubble in the Kenyan real estate. Two time period bubbles were . Finally, the bubble sizes were measured and were found to be 15% each in the two periods.

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“…We show both asymptotically and in finite samples that this procedure also has detective power against the new data generating process for a market crash and that it can consistently estimate the dates of the crash. The crash detection capability of the PSY procedure has been noted in several recent empirical articles by the authors (PSY, 2015a; PS, ; Shi, ; Deng et al ., ) and by many other researchers considering stock prices, exchange rates and other financial time series where abrupt crashes and sustained collapses have occurred (see, e.g. Yiu and Jin, ; Fantazzini, ; Hu and Oxley, ; Hu and Oxley, ; Wegener, Kruse and Basse, ).…”

Section: Introductionmentioning

confidence: 98%

Detecting Financial Collapse and Ballooning Sovereign Risk

Phillips

Shi²

2019

Oxf Bull Econ Stat

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This paper proposes a new model for capturing discontinuities in the underlying financial environment that can lead to abrupt falls, but not necessarily sustained monotonic falls, in asset prices. This notion of price dynamics is consistent with existing understanding of market crashes, which allows for a mix of market responses that are not universally negative. The model may be interpreted as a martingale composed with a randomized drift process that is designed to capture various asymmetric drivers of market sentiment. In particular, the model is capable of generating realistic patterns of price meltdowns and bond yield inflations that constitute major market reversals while not necessarily being always monotonic in form. The recursive and moving window methods developed in Phillips, Shi and Yu (2015a,b; PSY), which were designed to detect exuberance in financial and economic data, are shown to have detective capacity for such meltdowns and expansions. This characteristic of the PSY tests has been noted in earlier empirical studies by the present authors and other researchers but no analytic reasoning has yet been given to explain why methods intended to capture the expansionary phase of a bubble may also detect abrupt and broadly sustained collapses. The model and asymptotic theory developed in the present paper together explain this property of the PSY procedures. The methods are applied to analyse S&P 500 stock prices and sovereign risk in European Union countries over 2001–16 using government bond yields and credit default swap (CDS) premia. A pseudo real‐time empirical analysis of these data shows the effectiveness of the monitoring strategy in capturing key events and turning points in market risk assessment.

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Did bubbles migrate from the stock to the housing market in China between 2005 and 2010?

Cited by 45 publications

References 90 publications

Risk Transfer among Housing Markets in Major Cities in China

Risk Transfer among Housing Markets in Major Cities in China

Investigating the Existence of a Bubble in the Kenyan Real Estate Market

Detecting Financial Collapse and Ballooning Sovereign Risk

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