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 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.

Deciphering the U.S. metropolitan house price dynamics

“…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.…”

“…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.…”

Investor behavior and cryptocurrency market bubbles during the COVID-19 pandemic

“…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.…”

Are CDS Spreads Sensitive to the Term Structure of the Yield Curve? A Sector-Wise Analysis under Various Market Conditions

“…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).…”

“…SeeShi and Phillips (2017). Detecting Financial Collapse and Ballooning Sovereign Risk, Oxford Bulletin of Economics and Statistics.…”