Currency jumps and crashes may have link to earthquakes

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There could be a link between currency fluctuations and earthquakes, a new study has suggested.

Published in Science Direct is the study that demonstrates using a Japanese model a possibility to predict currency risk based on seismic activity. Scientists behind the study say there are some similarities between the idea of self-excitement – the Hawkes self-exciting processes – and the cause-and-effect relationships of sudden changes on the financial market.

According to researchers behind the study, foreign exchange market fluctuations or ups and downs in the stock market always stem from external (exogenous) or internal (endogenous) causes. As a rule, external causes are not predictable, but they can have a strong impact on the stock exchange. For example, this happened on the first trading day after the attacks of September 11, 2001, when the Dow Jones index dropped by 7.1%.

Researchers applied the Hawkes method on the S&P500 stock index and USD/RUB currency pair to show that Japanese approach predicts financial risks as successfully as the models which are traditionally used by analysts for these same purposes.

In their experiment, researchers used the data of the Moscow Stock Exchange on USD/RUB currency pair for the period from January 26, 1999 to April 10, 2017. Scientists onte that accuracy of the prediction using Hawkes processes is about 40%. Only 112 of the 277 events observed were successfully forecasted and this is because Hawkes process has the ability to predict subsequent events, but not the first.

Furthermore, the foreign exchange market reacted quickly to external events, adjusting to new prices, such that there were no large consecutive shocks, as in the case of an earthquake. The shocks quickly came to an end, and the market ‘calmed down.’

In general, according to the researcher, the accuracy of prediction using the ETAS model is not inferior to the accuracy of other popular econometric models used for financial risk forecasting. This was shown by comparing the forecasts generated by econometric models using the same S&P500 data.

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