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Some results from a method for generating recurrent neural networks (RNN) for prediction of financial and macroeconomic time series are presented. In the presented method, a feedforward neural network (FFNN) is first obtained using backpropagation. While backpropagation is usually able to find a fairly good predictor, all FFNN are limited by their lack of short-term dynamic memory. RNNs, by contrast, may exhibit short-term memory due to feedback connections in the network. In the method presented here, the RNNs are generated by an evolutionary algorithm (EA). The preliminary results indicate that the evolved RNN indeed outperform, by a few per cent, the FFNN obtained through backpropagation on several time series. How- ever, it is noted that, regardless of the predictor used, the prediction error cannot be much improved over that obtained from a very simple predictor. Finally, another approach is tested as well, in which the evolved RNN gener- ate not only a prediction but also a measure of confidence in the prediction.
The present study is an attempt to evaluate the predictability of the foreign exchange volatility in thirteen countries. The data covers the period of 2005-2009. To effectively forecast the volatility in the exchange rates, a GARCH model is used. The study compares the results between crisis period and a set of normal periods. The empirical results reveal that almost all countries except Thailand witnessed non-existence of volatility shocks at least once in a three year pre-crisis period but all the sample countries had volatility shocks in the crisis period of 2008-09. This apparently indicates that forecasting can be made at least for the next day given the high degree of volatility in the crisis period. The paper also reveals that exchange rates tend to have persistent conditional heteroskedasticity, and hence, could be predicted with one lag term.
Since 2013 regulators have been investigating the activities of some of the world's largest banks around the setting of daily benchmarks for forex prices. These benchmarks are a key linchpin of world financial markets, providing standardize prices used to value global equity and bond portfolios, to hedge currency exposure, and to write and execute derivatives' contracts. The most important of these benchmarks, called the "London 4pm Fix", "the WMR Fix" or just the "Fix", is published by the WM Company and Reuters based on forex trading around 4:00 pm GMT. This paper undertakes a detailed empirical analysis of the how forex rates behave around the Fix drawing on a decade of tick-by-tick data for 21 currency pairs. The analysis reveals that the behavior of spot rates in the minutes immediately before and after 4:00 pm are quite unlike that observed at other times. Pre-and post-Fix changes in spot rates are extraordinarily volatile and exhibit strong negative serial correlation, particularly on the last trading day of each month. These statistical features appear pervasive, they are present across all 21 currency pairs throughout the decade. However, they are also inconsistent with the predictions of existing microstructure models of competitive forex trading.. Disclosure: The research reported here only uses publicly available data and was undertaken independently without compensation. I do, however, have an ongoing consulting relationship with a law firm involved in litigation related to the WRM Fix.