2019
DOI: 10.1111/obes.12307
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Detecting Financial Collapse and Ballooning Sovereign Risk

Abstract: 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 marke… Show more

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Cited by 42 publications
(5 citation statements)
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“…The (logarithmic) price–rent ratio is an easy applicable index that adheres closely to the price dynamics of the real estate market and often exhibits higher accuracy than the price‐income ratio, the growth rate of house prices or univariate regression models (Bourassa et al., 2019). For robustness check, in the Supplementary File, we perform a comparison of periods identified as bubbles with the price–rent approach to periods identified with the Phillips and Shi (2019) method. The latter consists of augmented Dickey–Fuller regressions on rolling windows to identify nonstationary behavior of timeseries, using bootstraps to estimate the empirical distribution of the tests.…”
Section: Empirical Findingsmentioning
confidence: 99%
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“…The (logarithmic) price–rent ratio is an easy applicable index that adheres closely to the price dynamics of the real estate market and often exhibits higher accuracy than the price‐income ratio, the growth rate of house prices or univariate regression models (Bourassa et al., 2019). For robustness check, in the Supplementary File, we perform a comparison of periods identified as bubbles with the price–rent approach to periods identified with the Phillips and Shi (2019) method. The latter consists of augmented Dickey–Fuller regressions on rolling windows to identify nonstationary behavior of timeseries, using bootstraps to estimate the empirical distribution of the tests.…”
Section: Empirical Findingsmentioning
confidence: 99%
“…Nevertheless, their sample is limited, highly aggregated, and potentially misses cross‐sectional dependence as bubbles are highly contagious between local house markets (see Figure 3 for an exposition of spatial clustering in momentum effects). Fabozzi and Xiao (2019) also detect bubble formation in eight U.S. cities improving on the augmented Dickey–Fuller regressions of Phillips and Shi (2019), missing the cross‐sectional dependence characteristic as they examine each city independently. Overall, our study is the first to study bubble detection under spatial heterogeneity and cross‐sectional dependence, exploiting the advantages of ML in forecasting at the detailed county level.…”
Section: Empirical Findingsmentioning
confidence: 99%
“…To set up our study, we use the Phillips et al. (2016) (hereafter PSY) test to identify the bubble date stamping during the COVID-19 pandemic, as it is proven to be a warning device in moments of crisis (Phillips and Shi, 2019). Besides, the pandemic effects are evaluated by using the daily growth in fatalities, the daily growth in newly confirmed cases and the reproduction rate or R-number.…”
Section: Introductionmentioning
confidence: 99%
“…Advancement in the credit derivative market has led to the need for an in-depth analysis of the pricing of credit risk and its relationship, not only with a single yield rate but also its time-varying components, i.e., long-, short-and medium-term factors. Similarly, there is an extended need to observe the direction and magnitude of the relationship between the decomposed yield rate factors and the sector-wise CDS premia to uncover the price dynamics among the bond and the CDS markets, especially during the market crashes 1 (Shi and Phillips 2017).…”
Section: Introductionmentioning
confidence: 99%
“…SeeShi and Phillips (2017). Detecting Financial Collapse and Ballooning Sovereign Risk, Oxford Bulletin of Economics and Statistics.…”
mentioning
confidence: 99%