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While each financial crisis has its own characteristics there is now widespread recognition that crises arising from sources such as financial speculation and excessive credit creation do inflict harm on the real economy. Detecting speculative market conditions and ballooning credit risk in real time is therefore of prime importance in the complex exercises of market surveillance, risk management, and policy action. This chapter provides an R implementation of the popular real-time monitoring strategy proposed by Phillips et al. (2015a,b), along with a new bootstrap procedure designed to mitigate the potential impact of heteroskedasticity and to effect family-wise size control in recursive testing algorithms. This methodology has been shown effective for bubble and crisis detection (Phillips and Shi, 2017; Phillips et al., 2015a,b) and is now widely used by academic researchers, central bank economists, and fiscal regulators. We illustrate the effectiveness of this procedure with applications to the S&P financial market and the European sovereign debt sector. These applications are implemented using the psymonitor R package (Phillips et al., 2018) developed in conjunction with this chapter.
|Title of host publication||Financial, macro and micro econometrics using R|
|Editors||Hrishikesh D. Vinod, C. R. Rao|
|Place of Publication||Amsterdam, Netherlands|
|Number of pages||20|
|Publication status||Published - 2020|
|Name||Handbook of Statistics|
- Real-time detection
- Recursive evolving test
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