A Robust Algorithm in Sequentially Selecting Subset Time-Series Systems Using Neural Networks

In this paper a numerically robust lattice-ladder learning alogorithm is presented that sequentially selects the best specification of a subset time series system using neural networks. We have been able to extend the relevance of multi-layered neural networks and so more effectively model a greater array of time series situations. We have recognised that many connections between nodes in layers are unnecessary and can be deleted. So we have introduced inhibitor arcs - reflecting inhibitive synapses. We also allow for connections between nodes in layers which have variable strengths at different points of time by introducing additionally excitatory arcs - reflecting excitatory synapses. The resolving of both time and order updating leads to the optimal synaptic weight updating and allows for the optimal dynamic node creation/deletion within the extended neural network. The paper presents two applications that demonstrate the usefulness of the process

Causal relationship testing with applications to exchange rates

This paper undertakes two causality studies with exchange rate applications in a framework of Zero-Non-Zero (ZNZ) patterned Vector Error-Correction Modelling (VECM). The first study shows that money supply is a source of financial and economic influence on the Euro. The second gives evidence of support for Purchasing Power Parity (PPP) using monthly data between Japan and the USA. The results indicate that high-frequency finance data can reveal the existence of long-term PPP. This evidence sheds light on the adjustment mechanisms through which PPP is achieved. Also, the proposed ZNZ patterned VECM modelling allows better insights from this kind of financial time-series analysis.error correction modelling; e-finance; Granger causality; purchasing power parity; PPP; electronic finance; causal relationships; causality; exchange rates; Euro; money supply; Japan; USA; United States.

The Adjustment of the Yule-Walker Relations in VAR Modelling: The Impact of the Euro on the Hong Kong Stock Market

VAR models are increasingly being used in the analysis of relationships between financial markets. In such models, there are circumstances that require zero entries in the coefficient matrices. Such circumstances can be particularly relevant in the context of emerging markets given their characteristics. We show that a direct extension of the use of the Yule-Walker relations for fitting VAR models with zero-non-zero patterned coefficient matrices is inconsistent with statistical procedures as the resultant estimated variance-covariance matrix of the white noise process becomes non-symmetric. This inconsistency has biased consequences for financial theory. The paper provides a theoretically consistent adjustment which fits with theory. The paper applies the procedure to a vector system comprising variables from the Hong Kong stock market and foreign exchange markets. The results indicate that the euro exchange rate contains leading information for the other components in the system

Zero-non-zero Patterned Vector Error Correction Modelling for I(2) Cointegrated Time-Series with Applications in Testing PPP and Stock Market Relationships

Vector error-correction models (VECMs) have become increasingly popular in their applications to financial markets. Standard VECM models assume that the cointegrating vectors are of full rank such that they contain no zero elements. However, applications of VECM models to financial market data have revealed that zero entries are indeed possible. The existence of zero entries has not been fully discussed in cointegration theory. In such cases, the use of standard VECM models may lead to incorrect inferences. Specifically, if the underlying yes VECM and the associated cointegrating and loading vectors contain zero entries, the resultant specifications can produce different conclusions concerning the cointegrating relationships among the variables. In this paper, we provide a new efficient and effective algorithm to select cointegrating and loading vectors that can contain zero entries in the context of a VECM framework for time-series of integrated order I(2). We employ two case studies to demonstrate the usefulness of the alogrithm in tests of purchasing power parity and a three-variable system concerning the stock market

The relationship between stock markets of major developed countries and Asian emerging markets

Journal of Applied Mathematics and Decision Sciences84201-21