Vector Autoregressive (VAR) Modeling and Projection of DSE (original) (raw)
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Economics and Finance in Indonesia
This research examines ivhether there is a causal relationship between the Rupiah exchange rate and the composite stock price index. The Vector Autoregressive (VAR) method is applied to analyze daily time series data from January 24"', 2001 to June 18"', 2004. It shows that the series are non-stationary and become stationary on the first difference or I (I). Although they have the same integration order, neither variable is co-integrated based on the Augmented Engle Granger Method and Johansen's Co-integration Test. Consequently, the modeling technique used in this study (VAR) is applied to the first difference level. From the VAR model, it was found that the Rupiah exchange rate is affected by both the past exchange rate and the stock price index (ceteris paribus). J)i contrast, the stock price index is only affected by past index movements. These results are supported by innovative accounting calciUated by both Variance Decompositions (VDCs) and Impulse Response Function (IRE). We conclude that the index can be a leading indicator for the exchange rale following the Portfolio Balance Approach.
Analysis of Volatility and Forecasting General Index of Dhaka Stock Exchange
The purpose of the present study is to empirically examine the performance o f d ifferent kinds of volatility modeling and their forecasting performance for the general index of an emerging stock market, namely Dhaka Stock Exchange fro m the period December 06, 2010 to March 12, 2013. We main ly used Box-Jenkins modeling strategy thereafter the volatility model. The descriptive statistics, correlogram, unit root test, ARMA, ARCH, GA RCH, TARCH, EGA RCH and several model selection criteria are used in this study. The Butterworth filter is used for removing the noise of the re turn series of general index. All the parameters in this study are estimated through Maximu m Likelihood method. The descriptive statistics show general index decrease slightly overtime with positively skewed and leptokurtic. The return series fo llo ws ARMA(1,1) model with volatility provide evidence of the superiority of GA RCH(1,1) and GARCH(2,1) over the all order of other GA RCH models. Finally, we found that the fitted model on filtered general index of Dhaka Stock Exchange are ARMA(1,1) with GA RCH(1, 1) and GA RCH(2,1) model. Th is model can be used for future policy imp lication through its accurate forecast.
A Vector Autoregressive (VAR) Model for the Turkish Financial Markets
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A Vector Auto-Regressıve (VAR) Model for the Turkish Financial Markets
2011
In this paper, we develop a vector autoregressive (VAR) model of the Turkish financial markets for the period of June 15 2006-June 15 2010 and forecasts ISE100 index, TRY/USD exchange rate, and short-term interest rates. The out-ofsample forecast performance of the VAR model is compared with the results from the univariate models. Moreover, the dynamics of the financial markets are analyzed through Granger causality and impulse response analysis.
Application of Vector Autoregressive Model
Time series analysis is a specific way of analysing a sequence of data points collected over an interval of time. The variables of interest in the time series can broadly classified into univariate or multivariate. If we use the univariate class models to analyse and forecast the data which is having inter-connection, it will mislead the investors. Thus it is very important to handle the relationships between the different stock prices under study than ignoring it and carrying out a univariate time series analysis. In this work, we analysed the daily closing price of shares of State Bank of India (SBI), Axis bank & Industrial Credit and Investment Corporation of India bank (ICICI bank) which are listed under NSE (National Stock exchange) dated from 02-01-2017 to 31-12-2021. The study revealed that multivariate model gives more accurate forecast values than those of univariate model.
Forecasting the General Index of Dhaka Stock Exchange
2019
The Dhaka Stock Exchange (DSE) is an emerging stock exchange located in the capital city of Bangladesh. This present study focuses on finding a predictive model for the DSE general index. According to the Box-Jenkins methodology, ARIMA (2, 2, 1) model was found well fitted from a set of different possible ARIMA models. But the diagnostic tests such as ACF plot of residuals, standardized residual plot, shows that our model forecasts mean of the series pretty good though, we need to consider the volatility of the series to get the more accurate forecast of the data set. Conditional variance model, eGARCH (1, 1) was found as the best fits to our DSE data. The R package rugarch is used to fit the model.
Forecasting Economic Indicators of Bangladesh using Vector Autoregressive (VAR) Model
International Journal of Statistics and Economics, 2021
VAR model is an economic model that is useful for the analysis of multivariate time series. This model is widely used for determining the brawny nature of monetary time series and forecasting. This study aimed to presage economic indicators of Bangladesh by the appropriate vector autoregressive model. Secondary data were collected from monthly economic trends book publications of the Statistics Department of Bangladesh bank, depicting monthly time series of the three economic indicators such as total exports, total imports, and exchange rate from January 2007 to January 2020. Appropriate Vector Autoregressive model with a maximum of 4 lags was selected based on some information criteria. The Granger causality analysis indicated that exports were not outright concerned with the exchange rate only the imports as well as there exists bi-directional causality between exports and imports. The empirical model also forecasts total exports, total imports, and the exchange rate of Bangladesh from February 2020 to November 2020 that will help policymakers to plan more appropriately to improve the balance of trade.
Modeling and Forecasting Jakarta Stock Exchange: Stock Market Volatility
SSRN Electronic Journal, 2006
For both risk management and portfolio selection purposes, modeling the linkage across financial markets is crucial, especially among neighboring stock markets. In investigating the dependence or co-movement of three or more stock markets in different countries, researchers frequently use co-integration and causality analysis. Nevertheless, they conducted the causality in mean tests but not the causality in variance tests. This paper examines the co-integration and causal relations among three major stock exchanges in Southeast Asia, i.e Jakarta Stock Exchange, Singapore Stock Exchange, and Kuala Lumpur Stock Exchange. It employs the recently developed techniques for investigating unit roots, co-integration, time-varying volatility, and causality in variance. For estimating market risk of portfolio, this paper employs Valueat-Risk with delta-normal approach.
This paper investigates the predictability of Dhaka Stock Exchange (DSE) of Bangladesh by proving the market is not weak form efficient and then predicts the monthly index and the return series by using the Autoregressive Integrated Moving Average (ARIMA) process. Through different formal tests on the dataset, the best fitted model selected was ARIMA (3,1,2) for the index series and ARMA(3,1) for the return series. The forecasted values indicate that the market will remain stable with no extreme shocks in near future extending to 2015 The maximum growth possibility of the market indicated in the model is in the year of 2011 specially from September to November; where 2012 remains moderate; and 2013 to 2015 remains in low growth. For the validity of the forecast the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error(MAPE) and Theil U statistics are checked.
2011
Since independence in 1971, Bangladesh has achieved considerable economic development. With the growth of technology in Bangladesh, the Dhaka stock exchange has become accessible to common people in addition to traditional brokers: the involvement of non-professional Bangladeshis in the Dhaka share market has increased in the past several years. The Dhaka stock exchange generates reports on the latest DSE index, top gainer companies, top loser companies and monthly reviews on the Dhaka stock market, thereby enabling people to invest in the market more readily. In this paper, we have identified ARIMA (2, 1, 0) model as the best one to forecast the month ended General index of Dhaka Stock Exchange, Bangladesh. Monthly General Index of Dhaka Stock Exchange (DSE) from the Fiscal year January 31'1996 to July 31'2010 is used for this paper. We have also found that the GARCH (1, 1) model a specified set of parameters is the best fit for our concerned data set.