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The purpose of this paper is to examine the long memory property of equity returns and volatility of emerging equity market by focusing on the Malaysian equity market, namely the Kuala Lumpur Stock Exchange (KLSE).

The study adopts the Fractionally Integrated GARCH (FIGARCH) model and Fractionally Integrated Asymmetric Power ARCH (FIAPARCH), focusing on the Malaysian data covering the period from April 15, 2004 to April 30, 2007.

The study finds evidence of long memory property as well as asymmetric effects in the volatility of the KLSE. The traditional ARCH/GARCH is shown to be insufficient in modeling the volatility persistence. The FIAPARCH specification outperforms the FIGARCH model by capturing both asymmetry effects and long memory in the conditional variance.

The results of this study have practical implications for the investors intending to invest in the emerging markets such as Malaysia. Understanding volatility and developing the appropriate models are important since volatility can be a measure of risk which is highly relevant in forecasting the conditional volatility of returns for portfolio selection, asset pricing, and value at risk, option pricing and hedging strategies.

This study contributes in providing the empirical evidence on the long memory property of equity returns and volatility of an emerging equity market with reliable estimation models, which is currently lacking, particularly for emerging markets.

Conducts both the cointegration test of the monetary theory of inflation and the Granger‐causality test between the variables in the system, and also develops univariate and multivariate time series models to forecast inflation rates. Quarterly time series data for Pakistan, from 1972‐2 to 1993‐4 is used for empirical investigation. Results suggest no cointegrating or long‐run relationship between the variables in the monetary model. Observes that there is some evidence of Granger‐causality running from inflation to output growth. Comparison of out‐of‐sample quarterly forecasts for the 1988‐1 to 1993‐4 period are made for univariate and vector ARMA models of inflation. States that the forecasting accuracy of the multivariate ARMA model is not statistically different from that of the univariate ARMA model.

When performing system-level developmental testing, time and expenses generally warrant a small sample size for failure data. Upon failure discovery, redesigns and/or corrective actions can be implemented to improve system reliability. Current methods for estimating discrete (one-shot) reliability growth, namely the Crow (AMSAA) growth model, stipulate that parameter estimates have a great level of uncertainty when dealing with small sample sizes. The purpose of this paper is to present an application of a modified GM(1,1) model for handling system-level testing constrained by small sample sizes.

The paper presents a methodology for incorporating failure data into a modified GM(1,1) model for systems with failures following a poly-Weibull distribution. Notional failure data are generated for complex systems and characterization of reliability growth parameters is performed via both the traditional AMSAA model and the GM(1,1) model for purposes of comparing and assessing performance.

The modified GM(1,1) model requires less complex computational effort and provides a more accurate prediction of reliability growth model parameters for small sample sizes and multiple failure modes when compared to the AMSAA model. It is especially superior to the AMSAA model in later stages of testing.

This research identifies cost-effective methods for developing more accurate reliability growth parameter estimates than those currently used.

In this paper we address the question of how telecommunications affect economic growth, by estimating the effect of direct investment in telecommunications infrastructures on aggregate output, employment, and investment in Spain. In contrast with previous studies, the problem is analyzed in a dynamic multivariate framework which allows for explicit consideration of feedbacks among all the variables. We find significant effects of the investment in infrastructures of telecommunications on aggregate output, employment and investment which extend for several years. This might justify a policy for stimulating investment in this sector.

The purpose of this paper is to examine the existence of rational speculative bubbles in the Middle East and North African (MENA) stock markets.

To complement shortcomings of the traditional bubble tests, such as unit root tests and cointegration tests, mainly relying on expectations of future steams of dividends, the authors employ fractional integration tests and duration dependence tests.

Despite recent extreme fluctuations of MENA stock markets, fractional integration tests built on autoregressive fractionally integrated moving average models do not support the possibility of bubbles in the MENA stock markets. Similarly, duration dependence tests based on nonparametric Nelson‐Aalen hazard functions not only reject the existence of bubbles but also support equality of hazard functions between domestic and the US‐based investors without regard to the rapid financial liberalization and integration in the MENA stock markets.

The reliable results of bubble tests of the MENA stock markets provide domestic and international investors as well as policy makers with invaluable benchmark to better understand the irregular and highly fluctuating stock market behaviors of the MENA stock markets compared to other developed and emerging stock markets. For domestic and international investors, the formal analysis of MENA stock markets behavior including rational speculative bubbles will help them in their portfolio decisions and hedging purposes. Similarly, the empirical results of bubble tests in the paper will be also helpful to policymakers in MENA countries to take actions to improve the functioning of these dynamic markets.

Asymmetric information impedes the efficiency of financial intermediation by widening the gap between lending and deposit rates. The cost of information gathering is high and often translates into high borrowing costs. Consequently, high borrowing costs may make it hard for borrowers to repay loans and increase the volume of non-performing loans – a recipe for financial instability. This study first compares the application of the simple GARCH (1,1) and BGARCH (1,1,1) models in the estimation of macroeconomic volatility and finds that the latter is more suitable for this purpose. Moreover, the choice of BGARCH (1,1,1) over the simple GARCH (1,1) implies different outcomes for Granger causality tests. This finding implies that the BGARCH (1,1,1) model minimises loss of important information when estimating macroeconomic volatility in developing countries. Second, the study uses bootstrap panel Granger causality to test the hypothesis that there is a causal relationship between financial instability and the financial intermediation spread in Southern African Customs Union (SACU). The findings support this hypothesis and underscore the importance of implementing sound macroeconomic policies for high and stable growth as well as effective monetary policy to attain and maintain low and stable prices in order to narrow the financial intermediation spread in SACU. The paper aims to discuss these issues.

This study uses bootstrap panel Granger causality to test the hypothesis that there is a causal relationship between financial instability and the financial intermediation spread in SACU.

The findings support this hypothesis and underscore the importance of implementing sound macroeconomic policies for high and stable growth as well as effective monetary policy to attain and maintain low and stable prices in order to narrow the financial intermediation spread in SACU.

Application of panel bootstrap Granger causality test to test for a casual relationship between financial intermediation spread and financial stability in the context of SACU.

Banks in the South Asian region are the fulcrum of economic growth and development as they provide means to development credit and working capital, trade and infrastructure finance and are seen as custodians of the trust in the financial system. This paper aims to study the nature of banking sector linkages for the region.

The dependence structure between deposits and lending rates individually for the banks of the South Asian countries are studied using time invariant and time varying family of copula functions. The degree of connectedness is further studied by Diebold and Yilmaz methodology.

Results indicate poor levels of banking integration in the region as the dependence parameter for both deposits and lending rates was around 0 for the sample countries, thereby confirming poor banking sector integration in the region.

Policymakers of the region are interested in the co-movements of the interest rates to understand the cross-sector risk management and any systemic risk pressures for the regional economies. Corporates in these countries are scouting out for competitive borrowing rates to lower their cost of capital.

Rationale for examining the banking sector linkages is that the South Asian countries are at different stages of economic growth and development and this region in particular is the fastest growing region in the world and has largely increased its trade integration with the world albeit having lowest levels of intra-regional trade integration.

This is a first of a kind of studies to examine the banking sector linkages in South Asia.

This paper, using Turkish stock index data, set outs to present long‐term memory effect using chaotic and conventional unit root tests and investigate if chaotic technique as wavelets captures long‐memory better than conventional techniques.

Haar and Daubechies as wavelet‐based OLS estimator and GPH and other classical models are applied in order to investigate the performance of long memory in the time series.

The results indicate that Daubechies wavelet analysis provide the accurate determination for long memory where conventional techniques does not.

The research results have both methodological and practical originality. On the theoretical side, the wavelet‐based OLS estimator is superior in modeling the behaviours of the stock returns in emerging markets where non‐linearities and high volatility exist due to their chaotic natures. For practical aims, on the other hand, the results show that the Istanbul Stock Exchange is not in the weak‐form efficient because the prices have memories that are not reflected in the prices, yet.

The purpose of this paper is to focus on whether the deviations from the cointegrating relationship possess long memory and the fractional cointegration analyses may capture a wider range of mean-reversion behaviour than standard cointegration analyses.

This paper uses a fractional cointegration technique to test the purchasing power parity (PPP).

The authors found that PPP held, but very weakly, in the long run between the Argentine, Brazil, Chile, Colombia, Indonesia, Korea, Mexico, Thailand and Venezuela and US exchange rate during our floating exchange rate period but that the deviations from it did not follow a stationary process. Nevertheless, it is also found that the deviations from PPP exists and can be characterized by a fractionally integrated process in nine out of 13 countries studied.

The findings are consistent with the consensus of the empirical literature, reviewed earlier in this paper, on PPP between Argentine, Brazil, Chile, Colombia, Indonesia, Korea, Mexico, Thailand and Venezuela and the USA.