Financial crises prediction is an essential topic in finance. Designing an efficient Early Warning System (EWS) can help prevent catastrophic losses resulting from financial crises. We propose different EWSs for predicting potential market instability conditions, where market instability refers to large asset price declines. A logit regression EWS is employed to predict future large price losses and Early Warning Indicators (EWIs) based on the realized variance (RV) and price-volatility feedback rate are considered. The latter EWI is supposed to describe the ease of the market in absorbing small price perturbations. Our study reveals that, while RV is important in predicting future price losses in a given time series, the EWI employing the price-volatility feedback rate can improve prediction further.

An Early Warning System for identifying financial instability / Allaj, Erindi; Sanfelici, Simona. - (2022).

An Early Warning System for identifying financial instability

Erindi Allaj
Membro del Collaboration Group
;
Simona Sanfelici
Membro del Collaboration Group
2022

Abstract

Financial crises prediction is an essential topic in finance. Designing an efficient Early Warning System (EWS) can help prevent catastrophic losses resulting from financial crises. We propose different EWSs for predicting potential market instability conditions, where market instability refers to large asset price declines. A logit regression EWS is employed to predict future large price losses and Early Warning Indicators (EWIs) based on the realized variance (RV) and price-volatility feedback rate are considered. The latter EWI is supposed to describe the ease of the market in absorbing small price perturbations. Our study reveals that, while RV is important in predicting future price losses in a given time series, the EWI employing the price-volatility feedback rate can improve prediction further.
An Early Warning System for identifying financial instability / Allaj, Erindi; Sanfelici, Simona. - (2022).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/2913611
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