Non-causal time series models for speculative bubble prediction
Speculative bubbles in financial markets can result in dramatic damages to portfolio performances and threaten the stability of the financial system. Autoregressive and moving average processes known as non-causal time series models have proved their capacity to reproduce standardised facts from speculative bubbles such as locally explosive trajectories. On condition that their dynamics are better understood, they will allow the formulation of predictions on future bubble trajectories. However, understanding regarding the prediction of non-causal processes remains limited. The EU-funded NONCAUSALBubble project will focus on the lack of theoretical foundations for the forecasting of strong non-causal processes. The project will be based on recent developments of extreme value and alpha-stable distribution theories. Analytical assessment of crash probabilities will lead to an intuitive prediction model of bubble identification.
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