Jack Hw Penm

The role of higher oil prices: A case of major developed countries

The primary aim of this chapter is to examine whether the recent increase in world oil prices has affected inflation expectations and stock market returns in major OECD countries. The key findings are as follows. First, we found no evidence to support the presence of a long term relationship between oil prices and inflation expectations – measured by the difference between yields of inflation indexed and non-inflation indexed government bonds – over the sample between early 2003 and late 2006. Second, higher oil prices are found to lead to expectations of higher inflation. This evidence is stronger over the period where oil prices had been higher and signs of capacity constraints in the economy were emerging. Third, the impact of higher oil prices on stock market returns differs among countries. While higher oil prices are found to adversely affect stock market returns in the United States, the United Kingdom and France, the effects are positive in Canada and Australia as these countries are significant exporters of energy resources.

Selecting the Forgetting Factor in Subset Autoregressive Modelling

Conventional methods to determine the forgetting factors in autoregressive (AR) models are mostly based on arbitrary or personal choices. In this paper, we present two procedures which can be used to select the forgetting factor in subset AR modelling. The first procedure uses the bootstrap to determine the value of a fixed forgetting factor. The second procedure starts from this base and applies the time-recursive maximum likelihood estimation to a variable forgetting factor. In one illustration using real exchange rates, we demonstrate the effect of the forgetting factor in subset AR modelling on forecasting of non-stationary time series. In a second illustration, these two procedures are applied to time-update forecasts for a stock market index. Subset AR models not including a forgetting factor act as a set of benchmarks for assessing ex ante forecasting performance, and consistently improved forecasting performance is demonstrated for these proposed procedures. ex ante

Using the Bootstrap as an Aid in Choosing the Approximate Representation for Vector Time Series

In this article, a procedure is presented to use the bootstrap in choosing the best approximation in terms of forecasting performance for the equivalent state-space representation of a vector autoregressive model. It is found that the proposed procedure, which uses each approximants forecasting performance, can enhance considerably an approach based simply on the estimated Hankel singular values.

A ROBUST ALGORITHM IN SEQUENTIALLY SELECTING SUBSET TIME- SERIES SYSTEMS USING NEURAL NETWORKS

In this paper a numerically robust lattice-ladder learning algorithm 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 multilayered neural networks and so more effectively model a greater array of time series situations. We have recognized 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 optimal synaptic weight updating and allows for optimal dynamic node creation/deletion within the extended neural network. The paper presents two applications that demonstrate the usefulness of the process.

A subset polynomial neural networks approach for breast cancer diagnosis

Breast cancer is a very common and serious cancer for women that is diagnosed in one of every eight Australian women before the age of 85. The conventional method of breast cancer diagnosis is mammography. However, mammography has been reported to have poor diagnostic capability. In this paper we have used subset polynomial neural network techniques in conjunction with fine needle aspiration cytology to undertake this difficult task of predicting breast cancer. The successful findings indicate that adoption of NNs is likely to lead to increased survival of women with breast cancer, improved electronic healthcare, and enhanced quality of life.

Asian rupee for a common electronic financial market of India and the ASEAN region

Separate Indian and ASEAN stock markets with strong growth deriving from the information and communications technology revolution are seeking to become a more integrated common financial market for the India and the ASEAN countries. This aspiration could be thwarted if currencies and stock prices are excessively volatile. A well-integrated common electronic financial market underpinned by a common currency would reduce this problem. In this paper, we explore the necessity for and practicability of a currency union for India and the ASEAN region. The detected cointegrating relations indicate that an ASEAN and Indian currency union is likely to be developed.

An evolutionary recursive algorithm in selecting statistical subset neural network/VDL filtering.

We propose an evolutionary recursive algorithm, for the exact windowed case, to estimate subset vector discrete lag (SVDL) filters with a forgetting factor and an intercept variable. SVDL filtering is demonstrated as a basis for constructing a multi-layered polynomial neural network by Penm et al. (2000) The new proposed time update recursions allow users to update SVDL filters at consecutive time instants, and can show evolutionary changes detected in filter structures. With this new approach we are able to more effectively analyse complex relationships where the relevant financial time series have been generated from structures subject to evolutionary changes in their environment. An illustration of these procedures is presented to examine the integration between the Australian and the Japanese bond markets, and the USA and the UK bond markets, changed over the period. The proposed algorithms are also applicable to full-order vector discrete lag (VDL) filtering with a forgetting factor and an intercept.

Effectiveness of high interest rate policy on exchange rates: A reexamination of the Asian financial crisis

One of the most controversial issues in the aftermath of the Asian financial crisis has been the appropriate response of monetary policy to a sharp decline in the value of some currencies. In this paper, we empirically examine the effects on Asian exchange rates of sharply higher interest rates during the Asian financial crisis. Taking account of the currency contagion effect, our results indicate that sharply higher interest rates helped to support the exchange rates of South Korea, the Philippines, and Thailand. For Malaysia, no significant causal relation is found from the rate of interest to exchange rates, as the authorities in Malaysia did not actively adopt a high interest rate policy to defend the currency.

AN ANALYSIS OF ASIAN MARKET INTEGRATION PRE- AND POST-CRISIS

In this paper cointegrating relations between six East and Southeast Asian markets relative to a base cluster of three global markets are investigated in the framework of zero-non-zero (ZNZ) patterned vector error-correction modelling (VECM). The analysis focuses upon market relations both before and after the Asian currency crisis. The strength of integration between markets is also evaluated by extending Gewekes measurement approach within this framework. The results show that, since the crisis, estimated integration strengths have become more powerful between the Asian and global markets, with the US market leading both the Asian markets and the markets of Japan and the UK.

A note on the sequential fitting of multichannel subset autoregressions using the prewindowed case

In this correspondence, an efficient adaptive algorithm for multichannel subset autoregression identification using the prewindowed case is developed. After the initialization is carried out by the direct method, the optimum multichannel subset autoregression at each time instant is selected by employing the proposed recursions in conjunction with a model selection criterion. >