A feasibility study of using of Dynamic Bayesian Networks in combination with ARMA modeling in exchange rate prediction is presented. A new algorithm (ARMA-DBN) is constructed and applied to the exchange rate forecast of RMB. Results show that the improved dynamic Bayesian forecast algorithm has better performance than the standard ARMA model.

Autoregressive moving average (ARMA) models are a fundamental tool in time series analysis that offer intuitive modeling capability and efficient predictors. Unfortunately, the lack of globally optimal parameter estimation strategies for these models remains a problem: application studies often adopt the simpler autoregressive model that can be easily estimated by maximizing (a posteriori) like...

The combination forecasting model IOWGA-EMD-ARMA-WNN is proposed in this paper. The randomness, periodicity and tendency of the original data are showed by EMD decomposition in EMD-ARMA model. WNN combines the advantages of wavelet analysis and BP neural network and improves the learning efficiency and forecasting accuracy. The weight of combination model is decided by forecasting precision of ...

This study empirically examined the asymmetric oil price shocks in Nigeria from 1981q1-2019q4 using Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model. The EGARCH model was employed to investigate by obtaining conditional variance estimated results. Empirical results revealed a weak indication for leverage effect and strong effect. positive egarch (L2) coeffici...

OBJECTIVES AND METHODS armA is a novel plasmid-borne 16S rRNA methyltransferase that confers high-level resistance to 4,6-disubstituted deoxystreptamines. Recently, we have isolated from a high-level broad-spectrum aminoglycoside-resistant Escherichia coli animal isolate a plasmid, pMUR050, that bore the armA gene. In order to elucidate the genetic basis for the spread of armA, we have determin...

A distinguishing feature of the intra-day time-varying volatility of financial time series is given by the presence of long-range dependence of periodic type due mainly to time-of-the-day phenomena. In this work we introduce a model able to describe the empirical evidence given by this periodic longmemory behaviour. The model, named PLM-GARCH (Periodic Long Memory GARCH), represents a natural e...

High frequency foreign exchange rate (HFFX) series are analyzed on an operational time scale using models of the ARCH class. Comparison of the estimated conditional variances focuses on the asymmetry and persistence issue. Estimation results for para-metric models connrm standard results for HFFX series, namely high persistence and no signiicance of the asymmetry coeecient in an EGARCH model. T...