Modeling financial crisis period: A volatility perspective of Credit Default Swap market

Kim, Kyungwon

doi:10.1016/j.physa.2013.06.016

Cited by 5 publications

(1 citation statement)

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“…The crisis classifier consists of two key components, namely the switching ARCH (SWARCH) model (Hamilton and Susmel, 1994) and two-peak (or valley-of-two-peaks) method (Rosenfeld and De La Torre, 1983). Instead of focusing on the return horizon, the proposed classifier tackles the problem from the perspective of the volatility (Rodriguez, 2007;Kim, 2013;Fink et al, 2016;BenSaïda, 2018;BenMim and BenSaïda, 2019). The switching ARCH (SWARCH) model is adopted to label crisis/noncrisis episodes with high/low volatility regimes that imply market turbulence/tranquility (Hamilton and Susmel, 1994;Hamilton and Gang, 1996;Ramchand and Susmel, 1998;Edwards and Susmel, 2001).…”

Section: Introductionmentioning

confidence: 99%

An integrated early warning system for stock market turbulence

Wang

Zong

2020

Expert Systems with Applications

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This study constructs an integrated early warning system (EWS) that identifies and predicts stock market turbulence. Based on switching ARCH (SWARCH) filtering probabilities of the high volatility regime, the proposed EWS first classifies stock market crises according to an indicator function with thresholds dynamically selected by the two-peak method. An hybrid algorithm is then developed in the framework of a long short-term memory (LSTM) network to make daily predictions that alert turmoils. In the empirical evaluation based on ten-year Chinese stock data, the proposed EWS yields satisfying results with the test-set accuracy of 96.6% and on average 2.4 days of forewarned period. The model's stability and practical value in the real-time decision-making are also proven by the cross-validation and back-testing.

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