Search results

Housing activity is an important indicator of general economic activity, and house price movements are an important variable in international financial markets. In this chapter we utilise vector autoregressive models to examine how the interrelationship between housing activity and general economic activity has evolved in four OECD countries. Our results provide support for the hypothesis that the relationship between housing activity and general economic activity has changed in many OECD countries. For Australia, however, no such evidence was found. These results suggest that caution needs to be exercised when using previous experience to forecast both housing cycles and general economic activity.

In this paper the techniques of zero-non-zero (ZNZ) patterned vector autoregressive modelling are utilized to examine two issues associated with the European single currency – the euro. First, “Granger causality” is employed to examine the causal linkages between the euro exchange rate, the euro area money supply and the gross domestic product (GDP) growth in the euro area. Second, we examine the hypothesis that the euro has become a major influence on international stock markets by testing for the causal relationships between movements in the euro exchange rate, the U.K. pound exchange rate and the London stock market index.

Vector error-correction models (VECMs) have become increasingly important in their application to financial markets. Standard full-order VECM models assume non-zero entries in all their coefficient matrices. However, applications of VECM models to financial market data have revealed that zero entries are often a necessary part of efficient modelling. In such cases, the use of full-order VECM models may lead to incorrect inferences. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of over-parameterised full-order VECM models may weaken the power of statistical inference. In this paper, it is argued that the zero–non-zero (ZNZ) patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. For a ZNZ patterned VECM framework for time series of integrated order two, we provide a new algorithm to select cointegrating and loading vectors that can contain zero entries. Two case studies are used to demonstrate the usefulness of the algorithm in tests of purchasing power parity and a three-variable system involving the stock market.

In this chapter we use a new data weighting process to examine the relationships between stock market returns in major Southeast Asian nations. Investigation is then directed to financial integration between those ASEAN countries and the larger Asia-Pacific region.

The findings indicate that, after the Asian financial crisis, financial integration has continued in most ASEAN countries and between ASEAN countries and the larger Asia-Pacific region. Such effects can be accounted for by the forgetting factor technique. This new technique will provide revenue managers with a decision-making tool to evaluate some complex underlying relationships which managers cannot comprehend prima facie.

In this chapter, we apply an efficient subset of vector error correction model (VECM) using the forgetting factor to examine the cointegration under climate change of the time series of the gross domestic product (GDP) and the industrial production and that of the utilization and consumption of important metals such as copper and steel in some important OECD countries as well as some selected newly industrialized Asian and Latin American countries. Both the long-term and the short-term dynamic relations among these variables are examined and the implications are discussed.

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.

Conventional methods to test for long-term PPP based on the theory of cointegration are typically undertaken in the framework of vector error correction models (VECM). The standard approach in the use of VECMs is to employ a model of full-order, which assumes nonzero entries in all the coefficient matrices. But, the use of full-order VECM models may lead to incorrect inferences if zero entries are required in the coefficient matrices. Specifically, if we wish to test for indirect causality, instantaneous causality, or Granger non-causality, and employ “overparameterised” full-order VECM models that ignore entries assigned a priori to be zero, then the power of statistical inference is weakened and the resultant specifications can produce different conclusions concerning the cointegrating relationships among the variables. In this paper, an approach is presented that incorporates zero entries in the VECM analysis. This approach is a more straightforward and effective means of testing for causality and cointegrating relations. The paper extends prior work on PPP through an investigation of causality between the U.S. Dollar and the Japanese Yen. The results demonstrate the inconsistencies that can arise in the area and show that bi-directional feedback exists between prices, interest rates and the exchange rate such that adjustment mechanisms are complete within the context of PPP.

This paper applies the variable forgetting factor and the fixed forgetting factor to financial time-series analysis, and establishes the linkage for the first time between the variable forgetting factor approach and kernel smoothing. We then demonstrate the use of the proposed variable forgetting factor approach to undertake forecasting of the Euro's exchange rates and the CRSP monthly net asset values (NAV). For both applications, the findings show that the kernel bandwidth so determined can improve the forecasting performance.

The aim is to investigate the effects of the first financial restructuring (FFR) on productivity growth, technical progress and efficiency change, using data from 42 commercial banks in Taiwan from 2001 to 2004.

Data envelopment analysis is applied to compute the Malmquist index of productivity change.

It is found that Taiwan commercial banks on average experienced a 117.39 percent increase in productivity growth, of which is 2.11 percent is due to efficiency change and 115.28 percent to technical progress over the four year period. In addition, during the four year period, a 1 percent reduction in the nonperforming loan ratio resulted in 1.85 percent growth in productivity; a 1 percent increase in the capital adequacy ratio led to 2.15 percent growth in productivity.

It can be concluded that after the FFR, the productivity growth, technical progress, and efficiency change all improve, with the lower nonperforming loan ratio contributing to this improved performance.

The study provides evidence of the productivity change of the banking industry in Taiwan in response to the FFR. It also contributes additional empirical evidence on the impact of a reform on bank productivity in a developing country.

This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.

The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables.

The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market.

To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.