The geometric Lévy model (GLM) is a natural generalization of the geometric Brownian motion (GBM) model used in the derivation of the Black–Scholes formula. The theory of such models simplifies considerably if one takes a pricing kernel approach. In one dimension, once the underlying Lévy process has been specified, the GLM has four parameters: the initial price, the interest rate, the volatili...

Forward and spot exchange rates are modelled as an unrestricted bivariate autoregression from weekly data on the New York foreign exchange market for June, 1973 to April, 1980. The null hypothesis that the forward exchange rate is an unbiased estimate of the corresponding future spot exchange rate is tested by means of a nonlinear Wald test and is rejected for all six currencies considered. The...

This project presents the implementation prediction that can accuracy predict the foreign exchange rate. how the prediction accuracy can be improved by developing an ensemble model of the deep learning algorithm, Distributed Random forest and generalised linear model using sparkling water (Spark +H20). According to the researchers of literature review from 2000-2016, there are several models th...

Continuous time modified Cox-Ingersoll-Ross (1985) stochastic model is employed, combining with Hamilton (1989) type Markov regime switching framework, to study daily foreign exchange rates, where all parameter values depend on the value of a continuous time Markov chain. The Expectation-Maximization algorithm is extended, generalized, applied to a more general class of regime switching models ...

Abstract: Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting mod...

This study proposes a novel forecasting approach – an adaptive smoothing neural network (ASNN) – to predict foreign exchange rates. In this new model, adaptive smoothing techniques are used to adjust the neural network learning parameters automatically by tracking signals under dynamic varying environments. The ASNN model can make the network training process and convergence speed faster, and m...

In this chapter, the authors use an EGARCH-ECM to estimate the pass-through effects of Foreign Exchange (FX) rate changes and changes in producers’ prices for 20 U.K. export sectors. The long-run adjustments of export prices to FX rate changes and changes in producers’ prices are within the range of –1.02% (for the Textiles sector) and –17.22% (for the Meat sector). The contemporaneous PricingT...

We present a machine learning approach using the sparse grid combination technique for the forecasting of intraday foreign exchange rates. The aim is to learn the impact of trading rules used by technical analysts just from the empirical behaviour of the market. To this end, the problem of analyzing a time series of transaction tick data is transformed by delay embedding into a D-dimensional re...

Abstract-Foreign exchange market is the largest and the most important one in the world. Foreign exchange transaction is the simultaneous selling of one currency and buying of another currency. It is essential for currency trading in the international market. In this paper, we have investigated Artificial Neural Networks based prediction modelling of foreign exchange rates using five different ...