Artem Kraevskiy, Artem Prokhorov, Evgeniy Sokolovskiy
arXiv 4 Apr 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2404.03319 · PDF · DOI · OpenAlex · Extracted main text
Financial markets of emerging economies are vulnerable to extreme and cascading information spillovers, surges, sudden stops and reversals. With this in mind, we develop a new online early warning system (EWS) to detect what is referred to as `concept drift' in machine learning, as a `regime shift' in economics and as a `change-point' in statistics. The system explores nonlinearities in financial information flows and remains robust to heavy tails and dependence of extremes. The key component is the use of conditional entropy, which captures shifts in various channels of information transmission, not only in conditional mean or variance. We design a baseline method, and adapt it to a modern high-dimensional setting through the use of random forests and copulas. We show the relevance of each system component to the analysis of emerging markets. The new approach detects significant shifts where conventional methods fail. We explore when this happens using simulations and we provide two illustrations when the methods generate meaningful warnings. The ability to detect changes early helps improve resilience in emerging markets against shocks and provides new economic and financial insights into their operation.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Khalfaoui, R., S. Hammoudeh, and M. Z. Rehman (2023) Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector aut… | 0.941 | 6 | 3 | 83% |
| 2 | Breiman, L (2001) Random Forests | 0.843 | 3 | 3 | 100% |
| 3 | Chen, Z. and R. Ibragimov (2019) One country, two systems? The heavy-tailedness of Chinese A- and H- share markets | 0.843 | 3 | 3 | 100% |
| 4 | Bai, J. and P. Perron (1998) Estimating and testing linear models with multiple structural changes | 0.644 | 2 | 2 | 100% |
| 5 | Bifet, A. and R. Gavalda (2007) Learning from Time-Changing Data with Adaptive Windowing | 0.644 | 2 | 2 | 100% |
| 6 | Chaudhuri, K. and Y. Wu (2003) Random walk versus breaking trend in stock prices: Evidence from emerging markets | 0.644 | 2 | 2 | 100% |
| 7 | Calvo, G. A (1998) Capital flows and capital-market crises: The Simple Economics of Sudden Stops | 0.644 | 2 | 2 | 100% |
| 8 | Friedberg, R., J. Tibshirani, S. Athey, and S. Wager (2021) Local Linear Forests | 0.644 | 2 | 2 | 100% |
| 9 | Greenwood, R., S. G. Hanson, A. Shleifer, and J. A. Sorensen (2022) Predictable Financial Crises | 0.644 | 2 | 2 | 100% |
| 10 | Hassan, T. A., J. Schreger, M. Schwedeler, and A. Tahoun (2024) Sources and Transmission of Country Risk | 0.644 | 2 | 2 | 100% |
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