scholarly journals EUR/RON EXCHANGE RATE PREDICTION BASED ON BOX-JENKINS TECHNIQUE

2019 ◽  
Vol 1 (2) ◽  
pp. 31-41
Author(s):  
Costică Ionela ◽  
Boitan Iustina Alina
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Model Specification ◽  
Initial Time ◽  
Main Research ◽  
Rate Analysis ◽  
Initial Time Series ◽  
The Exchange Rate

The aim of this study consists in analyzing the importance of the exchange rate forecast using the Box-Jenkins models, also known as Auto Regressive Integrated Moving Average (ARIMA) models. The first part of the paper presents the main research in this field, which can be classified in two categories (studies applying classical methods, such as Box-Jenkins models and studies which rely on sophisticated prediction tools), and summarizes the main findings of some of the studies applying Box-Jenkins models. In the second part of the paper we performed a EUR/RON exchange rate analysis and forecasting, based on testing several AR, MA and ARMA candidate processes, in order to find out the best fitting model specification.  We adopted the following strategy: i) an initial time series had been used for testing various model specifications, identify the best performing one and making a forecast of the EUR/RON exchange rate; ii) after comparing the accuracy of this forecast with the real level recorded by the exchange rate at end of May 2018, we conducted a second forecast, for the period May 2019 – November 2019. The initial time series employed has daily frequency and covers the timeframe July 4, 2005 – December 5, 2017, while the second time series used covers the period July 4, 2005 – May 6, 2019. The empirical findings have passed the goodness-of-fit tests and show a good predictive power. The first forecast performed for a six month period (December 2017 – May 2018) has indicated a slow pace, persistent increase of the EUR/RON exchange rate, which was confirmed by the expectations of market participants (financial analysts, banks’ research departments). The second forecast, which covers the period May 2019 – November 2019, indicates a similar rising trend and the ongoing depreciation of the national currency.

scholarly journals Forecasting The Exchange Rate Between Euro And USD: Probabilistic Approach Versus ARIMA And Exponential Smoothing Techniques

2012 ◽  
Vol 28 (2) ◽  
pp. 171 ◽  
Author(s):  
Paraschos Maniatis
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Probabilistic Approach ◽  
Laplace Distribution ◽  
Exponential Smoothing ◽  
Initial Time ◽  
Font Size ◽  
Smoothing Techniques ◽  
Initial Time Series ◽  
The Exchange Rate

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none;" class="MsoNoSpacing"><span style="color: black; font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt; mso-themecolor: text1; mso-ansi-language: EN-US;">This study attempts to model the exchange rate between Euro and USD using univariate models- in particular ARIMA and exponential smoothing techniques. The time series analysis reveals non stationarity in data and, therefore, the models fail to give reliable predictions. However, differencing the initial time series the resulting series shows strong resemblance to white noise. The analysis of this series advocates independence in data and distribution satisfactorily close to Laplace distribution. The application of Laplace distribution offers reliable probabilities in forecasting changes in the exchange rate.</span></p><span style="font-family: Times New Roman; font-size: small;"> </span>

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

scholarly journals A forecasting the consumer price index using time series model

2021 ◽  
Vol 107 ◽  
pp. 10002
Author(s):  
Volodymyr Shinkarenko ◽  
Alexey Hostryk ◽  
Larysa Shynkarenko ◽  
Leonid Dolinskyi
Keyword(s):  
Time Series ◽  
Price Index ◽  
Consumer Price Index ◽  
Exponential Smoothing ◽  
Time Series Model ◽  
Initial Time ◽  
Autocorrelation Functions ◽  
Minimum Error ◽  
Initial Time Series

This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.

scholarly journals The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia

2021 ◽  
Vol 16 (3) ◽  
pp. 197-210
Author(s):  
Utriweni Mukhaiyar ◽  
Devina Widyanti ◽  
Sandy Vantika
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Series Regression ◽  
Vector Autoregressive ◽  
Daily Data ◽  
The Impact

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

Research on a Method of Fault Signal Extraction Based on Improved Algorithm

2012 ◽  
Vol 217-219 ◽  
pp. 2692-2696
Author(s):  
Ying Wang ◽  
You Rong Li ◽  
Xiao Qin Zhu ◽  
Pan Lin ◽  
Yue Sheng Luo
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Partial Information ◽  
Initial Time ◽  
Time Signal ◽  
Shock Attenuation ◽  
Information Time ◽  
Initial Time Series ◽  
Fault Information ◽  
Sequential Decomposition

Considering the difficulty of diagnosis signal de-noising and feature extraction problems, according to the characteristics of periodicity and shock attenuation respond of mechanical fault vibration signals, a method of improved sequential decomposition algorithm is proposed, it transforms an initial time series into a group of two-dimensional time series, prominent time series partial information, time series decomposition is reversible, can be used for filtering and feature extraction of time signal. Through the simulation and experiments, the validity of method for highlighting partial feature information of the signal is verified, helping to extract weak fault information in strong background noise environment.

scholarly journals The Exchange Rate Analysis of Fish Cultivators (A Case Study in Bayongbong Garut Indonesia)

2021 ◽  
pp. 36-48
Author(s):  
Iwang Gumilar ◽  
Riandini Nur Syifa ◽  
. Rosidah ◽  
Asep Agus Handaka Suryana
Keyword(s):  
Exchange Rate ◽  
Work Experience ◽  
Production Cost ◽  
Secondary Data ◽  
Household Consumption ◽  
Primary Data ◽  
Case Study Method ◽  
Rate Analysis ◽  
The Exchange Rate

The purpose of this study is to analyze 1) the exchange rate of fish cultivators (NTPI) in Garut Regency, and 2) the factors that affect the NTPI in Garut Regency. A case study method including both primary and secondary data was used in the study. Primary data were generated from the interviews with respondents. Secondary data were obtained from the relevant agencies. This study used 45 respondents as research sample based on the purposive sampling. The data analysis used is NTPI and multiple linear regression. The results obtained in this research are the average NTPI in Bayongbong Subdistrict, which is 116%, explaining that fish cultivators' expenditures on household consumption and production cost are lower than their income. So it can be concluded that fish cultivators in Bayongbong Subdistrict are prosperous cultivators. Factors that can affect NTPI are age, education level, work experience, number of family members, income, and expenditure.

scholarly journals Wavelet Transform as an Alternative to Power Transformation in Time Series Analysis

2016 ◽  
Vol 17 ◽  
pp. 57-74
Author(s):  
Chukwudi Justin Ogbonna ◽  
C. Jeol Nweke ◽  
Eleazer C. Nwogu ◽  
Iheanyi Sylvester Iwueze
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Goodness Of Fit ◽  
Discrete Wavelet ◽  
Power Transformation ◽  
Rate Data ◽  
Forecast Performance ◽  
Constant Variance

This study examines the discrete wavelet transform as a transformation technique in the analysis of non-stationary time series while comparing it with power transformation. A test for constant variance and choice of appropriate transformation is made using Bartlett’s test for constant variance while the Daubechies 4 (D4) Maximal Overlap Discrete Wavelet Transform (DWT) is used for wavelet transform. The stationarity of the transformed (power and wavelet) series is examined with Augmented Dickey-Fuller Unit Root Test (ADF). The stationary series is modeled with Autoregressive Moving Average (ARMA) Model technique. The model precision in terms of goodness of fit is ascertained using information criteria (AIC, BIC and SBC) while the forecast performance is evaluated with RMSE, MAD, and MAPE. The study data are the Nigeria Exchange Rate (2004-2014) and the Nigeria External Reserve (1995-2010). The results of the analysis show that the power transformed series of the exchange rate data admits a random walk (ARIMA (0, 1, 0)) model while its wavelet equivalent is adequately fitted to ARIMA (1,1,0). Similarly, the power transformed version of the External Reserve is adequately fitted to ARIMA (3, 1, 0) while its wavelet transform equivalent is adequately fitted to ARIMA (0, 1, 3). In terms of model precision (goodness - of - fit), the model for the power transformed series is found to have better fit for exchange rate data while model for wavelet transformed series is found to have better fit for external reserve data. In forecast performance, the model for wavelet transformed series outperformed the model for power transformed series. Therefore, we recommend that wavelet transform be used when time series data is non-stationary in variance and our interest is majorly on forecast.

scholarly journals PENGARUH SUBSIDI BBM TERHADAP IMPOR MINYAK BUMI PERIODE 1980 – 2013

2017 ◽  
Vol 18 (1) ◽  
pp. 30
Author(s):  
Riwi Sumantyo ◽  
Puji Lestari
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Time Series Data ◽  
Data Use ◽  
Series Data ◽  
Foreign Exchange Reserves ◽  
Independent Variables ◽  
Fuel Subsidies ◽  
The Exchange Rate ◽  
The Government

The study on the effect of fuel subsidies toward oil import is a controversial topicdiscussions. This study will explore the effect of fuel subsidies on oil import by addingseveral independent variables, consist of; the number of vehichles, the exchange rateand inflation. Data use time series data from 1980-2013. The tool of analyze is OrdinaryLeast Squares Method (OLS).Based on the results show that the simultaneous testexplains that the fuel subsidies, the number of vehichles, the exchange rate, and inflationhave a significant effect on oil import. However partially, the variables of fuel subsidies,the number of vehichles, and the exchange rate have a positive and significant effecton oil import. Inflation does not affect on oil import. The coefficient of determinationuses Adjusted R-square test is about 98%. The implication of this study is governmentscan increase oil production Indonesia. The government should facilitate the licensing ofinvestment and rejuvenate the old oil wells. It aims to reduce Indonesia dependence onoil import so that it can save foreign exchange reserves.

Exchange rate analysis by nonlinear time series method

2020 ◽  
Author(s):  
Dragos-Alin Dragomir ◽  
Maria-Alexandra Paun ◽  
Vladimir-Alexandru Paun ◽  
Viorel-Puiu Paun
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Nonlinear Time Series ◽  
Series Method ◽  
Rate Analysis ◽  
Time Series Method
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