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This paper examines the relationships between the CAC40 index, the Dow Jones index and the Euro/USD exchange rate using daily data over the period 1999-2008. We find that these variables are I(1) nonstationary series, but they are fractionally cointegrated: equilibrium errors exhibit slow mean reversion, responding slowly to shocks. Therefore, with...
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The principle aim of this investigation is the study of fluctuations of oil prices impacts on economic growth of oil-dependent countries with respect to institutional quality. For this purpose we use panel cointegration methodology and error-correction model for 32 oil abundant countries covering the period 1975-2010. The result implies that fluctu...
Monetary policy remains one of the channels for controlling high inflation. Effective control of headline inflation using monetary policy measures depends on the degree of relationship that exists between the variables. In the light of this, the paper explores the relation between monetary policy and headline inflation with the aim of determining t...
The paper examines demand for real money balances in six West Africa countries, over the 1985-2014 periods using panel Cointegration technique. The result shows that long run money demand is positively related to real income, inflation rate and inversely related to interest rate spread, real effective exchange rate and US real interest rate. Long r...
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... Aloy et al. (2010) used fractional integration and co-integration in stock prices and exchange rates. They found that these variables are integrating order one nonstationary Sinha (2014) combined ANN and ARIMA model for time series forecasting. ...
Agricultural datasets are mostly nonlinear, nonstationary and leptokurtic in nature. These properties of dataset pose a variety of problems in forecasting. Precise forecasting helps both farming community and policy makers to undertake informed decisions. Literature suggests that each of the forecasting models has their own limitations. A single forecasting model is not able to handle problems like nonstationary and nonlinearity simultaneously. Accordingly, the present study proposes three different hybrid models i.e. empirical mode decomposition based support vector regression (EMD-SVR), time-delay neural network with error correction term (TDNN-ECT) and multivariate adaptive regression splines based artificial neural network (MARS-ANN) models. The novelty of these models lies in the fact that they can handle both nonstationary and nonlinear features of dataset simultaneously. In EMD-SVR model, the nonstationary and nonlinear dataset is decomposed into different intrinsic mode functions and final residue through EMD method. Then the decomposed components are forecasted using SVR model and finally, all forecasted values are summed up to produce the final forecast. In the second model, TDNN-ECT uses the error correction term from the two co-integrated series as auxiliary variable. The auxiliary information in the form of ECT improves the forecasting accuracy. Further, selection of important input variables is a crucial step in determining the accuracy of any forecasting model. Hence, MARS-ANN hybrid model was developed in which the MARS algorithms was employed to extract important factors determining crop yield and the extracted factors were used for yield prediction using ANN methodology. The performance of proposed hybrid models is evaluated with individual forecasting models using three different agricultural datasets. The performance measures like RMSE, MAD, MAPE and ME are used to evaluate the model. The results indicated that the performance of the proposed hybrid models are substantially superior as compared to the individual forecasting model.
Key words: Co-integration, Nonlinearity, Nonstationary, EMD, SVR, TDNN, and MARS.
This paper looks into the continuous trends, steadiness and associations of gender equality and global indices. We examine the level of integration of each group of information from a fractional standpoint for the years 2014-2021. The findings demonstrate that all the individual series are remarkably consistent, with levels of integration close to 1 and no evidence of mean reverting behaviour. Looking at potential cointegrating relationships among the two variables, if we use a classical method based on the two steps approach of Engle and Granger (1987) the order of integration of the estimated errors is very close to 1 showing no evidence of cointegration at all. However, using the more powerful fractional CVAR (FCVAR) approach, the results strongly support the hypothesis of cointegration, finding evidence of long run comovements between the two series. Investment strategies and policy implications of these results are reported at the end of the manuscript.
In the context of globalization, due to the accelerated process of economic integration of countries and financial markets, the interdependence of the world's leading financial markets is more than obvious. This paper investigates the interdependence of stock exchange indices from leading capital markets in the world: USA, European Union and Asia. Our intention is to determine the direction of causality between the observed capital markets, as well as whether and in what way shocks in one market are transmitted to other markets. Research methodology includes stationarity testing, the existence of cointegration, the application of the Vector Autoregressive Model (VAR) which is complemented by the Granger causality test and the Impulse Response Function (IRF) analysis. The results of the research are as follows. Johansen's cointegration test showed that there is no long-term equilibrium relationship between the observed markets, while Granger's test showed that there is mutual causality between the capital markets of Germany and the United States. As for the Japanese index, previous events in Germany and the United States are statistically significant, but previous events on the Tokyo Stock Exchange cannot explain movements in Germany and the United States. According to the results of the IRF analysis, shocks that may occur in the US market have an almost identical impact on all observed markets. On the other hand, disturbances on the Japanese market are not transmitted to the German and American market, i.e. remain in Japan.
Economic growth is a quantitative index to determine the level of economic progress that can be measured by gross domestic product (GDP). Various literatures aim to solve problems regarding patterns of economic growth as well as about the determinants wherein the analysis volatility models are used. Some indicators of economic growth are money supply, inflation, interest rates, and exchange rates. Economic variables are recorded as time series data with different periods, and hence temporal aggregation is required to analyze. Economic time series data showing the existence of long memory and structural breaks, which leads to the difficulty on explaining the data only with a general model. Therefore, both long memory models and structural break are proposed to be taken into account in the model. In this analysis, the residual from a linear combination of economic variables follows a long memory process indicating a cointegration relationship between these variables. This paper models GDP volatility based on money supply, inflation, interest rate and exchange rate volatility in Indonesia, Philippines, Malaysia, and Singapore using the Fractional Cointegration model with long memory and structural break. The analysis shows that structural break is only found in the volatility of GDP of Indonesia, Malaysia, and Singapore while the nature of long memory is found in almost all economic variables’ volatility. Furthermore, we found the cointegration model, stationary and persistent noise in all countries.
This paper develops asymptotic econometric theory to help understand data generated by a present value model with a discount factor near one. A leading application is to exchange rate models. A key assumption of the asymptotic theory is that the discount factor approaches 1 as the sample size grows. The finite sample approximation implied by the asymptotic theory is quantitatively congruent with modest departures from random walk behavior with imprecise estimation of a well-studied regression relating spot and forward exchange rates.
