An early warning system for financial crisis using a stock market instability index
This paper proposes to utilize a stock market instability index (SMII) to develop an early warning system for financial crisis. The system focuses on measuring the differences between the current market conditions and the conditions of the past when the market was stable. Technically the system evaluates the current time series against the past stable time series modelled by an asymptotic stationary autoregressive model via artificial neural networks. Advantageously accessible to extensive resources, the system turns out better results than the conventional system which detects similarities between the conditions of the current market and the conditions of previous markets that were in crisis. Therefore, it should be considered as a more advanced tool to prevent financial crises than the conventional one. As an empirical example, an SMII for the Korean stock market is developed in order to demonstrate its potential usefulness as an early warning system.
early warning systems stable time series asymptotic stationary autoregressive model artificial neural networks