scholarly journals Future Spot Rate: The Implications in Indonesia

2020 ◽  
Vol 20 (1) ◽  
pp. 155
Author(s):  
R Adisetiawan ◽  
Pantun Bukit ◽  
Ahmadi Ahmadi
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Empirical Evidence ◽  
Forward Rate ◽  
Spot Rate ◽  
Forward Rates ◽  
Currency Exchange ◽  
Short Period ◽  
The Exchange Rate

Investors, multinational companies and governments require a rate forecasting to make informed decisions about the hedging of debts and receivables, funding and short-term investments, capital budgeting and long-term financing. The process of making forecasting from market indicators, known as market-based forecasting, is usually developed based on spot rates and forward rates. The current spot rate can be used as forecasting, as the exchange rate reflects the market estimate of the spot rate in a short period of time. The forward rate is used in forecasting, as the exchange rate reflects the market estimate of the spot rate at the end of the forecasting period. Based on the research conducted by Chiang (1986) of the samples used, empirical evidence indicates spot rates and forward rates are significant as predictors of future spots. Empirical evidence suggests that spot rates provide better forecasting results compared to forward rates. The research uses regression models for market-based forecasting methods. The variables used in this study are spot rates, forward rates and future spots. The samples used are from Bank Indonesia for spot rates in January – March 2019 and future spot in April – June 2019, and from Jakarta Futures exchange for forward rates in January – March 2019. The Stochastic and Chow Test models are selected and their use has been evaluated using quality and precise testing measures. Based on the sample period used, empirical evidence suggests that spot rates and forward rates are significant in predicting future spots for EUR, JPY and AUD currencies. Current spot rates provide better forecasting results in predicting Future spot compared to the forward rate. Both the 15Ft">  and 15St">  coefficient are sensitive to new information from the variation of the coefficient and time, it can increase the forecasting of the equation to each currency exchange rate used. The study states that variables from time series should be effectively utilized and utilized in predicting currency exchange rates, as this research demonstrates the absence of dependence on time series Can be concluded that foreign exchange rates in each country follow a pattern that is not stationary. The spot Euro exchange rate turns out to be statistically more accurate with an error rate of 0.004144% forecasting with the value of regression coefficient of Euro exchange rate is a Future Spot = 21.504,88 – 0.341229Spot + 15et+1"> .

scholarly journals Web Intelligent for Forecasting Exchange Rate Currency using Clever Extraction Agent Combine with Financial Data Mining

2021 ◽  
Vol 15 ◽  
pp. 1-7
Author(s):  
Khammapun Khantanapoka
Keyword(s):  
Data Mining ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Financial Data ◽  
Exchange Rate Forecasting ◽  
Currency Exchange ◽  
The Exchange Rate ◽  
The Individual ◽  
The Impact ◽  
Pattern Index

From the current economic climate results in fluctuations of currency exchange rates in all countries. Since the most countries use USD as the reference exchange rate. The exchange rate will change from day to day so variety of factors which affect the exchange rate forecasting in the exchange rates in advance are critical to evaluate for the impact of the economic system of each country. It is important for investment decisions, exports, and profitability in the money market. It was reported on website (www) in the daily exchange rate changes. We use clever search agent (CSA) gather information from financial website generate to financial data mining. Kohonen Neural Networks is the method to determine similarity of internet documents using pattern index of financial document. And Ontology Structure of Sentence is the method to determine keyword using pattern index of financial content. Both are important components of Financial Data Mining. It is analyzed for exchange rate forecasting about USD/ Pounds. Our experimental forecast exchange rates for currency's USD / Great Britain Pounds by compare three algorithms as fallows GA, Meiosis Genetic Algorithms (MGA). This research propose new algorithm is called Dash Predator Swarm Optimization (DP2SO) which are accurate in prediction than other methods in generation of Genetic algorithm (GA) 35.83-41.52% which it depend on the accuracy of the information in each factor which are important finance dataset. It will present the future trends of exchange rate to the individual website.

scholarly journals The Impact of Exchange Rate Movements on Firm Value in Visegrad Countries

2017 ◽  
Vol 65 (6) ◽  
pp. 2105-2111 ◽  
Author(s):  
Jana Šimáková
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Firm Value ◽  
Market Values ◽  
Exchange Rate Exposure ◽  
Positive Tendency ◽  
Short Period ◽  
The Exchange Rate ◽  
The Impact ◽  
Visegrád Countries

Company’s involvement in global activities through international trade is the primary source of their foreign exchange exposure. Many empirical studies suggest the negative impact of uncertainty about the development of the exchange rate on cash flow and profitability of companies, and thus their market values. Some economic studies show that foreign revenues are positively correlated with the exchange rate exposure and in a short period, currency depreciation negatively affects the market value of listed companies. On the other hand, there are studies that show no statistically significant links between the value of the companies and exchange rates. The aim of this paper is to evaluate the effect of exchange rates on the value of companies listed on stock exchanges in the Visegrad countries. Paper applies Jorion’s model and panel data regression for the sample period 2002 – 2016. Estimations for the whole period revealed negative relationship between exchange rate and value of stock companies. The highest exposure is observed in case of Hungary and Czechia. Positive tendency can be seen in comparison of pre‑crisis and post‑crisis period. Except the case of Hungary, all markets showed decreased exchange rate exposure in time.

scholarly journals Estimation of the development of Czech Koruna to Chinese Yuan exchange rate using artificial neural networks

2019 ◽  
Vol 61 ◽  
pp. 01012 ◽  
Author(s):  
Veronika Machová ◽  
Jan Mareček
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Artificial Neural ◽  
The Exchange Rate ◽  
Exchange Rate Prediction ◽  
Chinese Yuan ◽  
Rate Development

Through time series analysis, it is possible to obtain significant statistics and other necessary data characteristics. Prediction of time series allows predicting future values based on previously observed values. The exact prognosis of the time series is very important for a number of different areas, such as transport, energy, finance, economics, etc. It is within the topic of economy that the analysis and prediction of time series can also be used for exchange rates. The exchange rate itself can greatly affect the whole foreign trade. The aim of this article is therefore to analyze the exchange rate development of two currencies by analyzing time series through artificial neural networks. Experimental results show that neural networks are potentially usable and effective for exchange rate prediction.

scholarly journals Empirical evidence against the exchange rate anomaly: the Australian case

2006 ◽  
Vol 36 (2) ◽  
pp. 237-250 ◽  
Author(s):  
Luis A. Gil-Alana
Keyword(s):  
Exchange Rate ◽  
Unit Root ◽  
Long Memory ◽  
Fractional Integration ◽  
Forward Rate ◽  
Spot Rate ◽  
Forward Rates ◽  
Widespread Agreement ◽  
Integration Techniques ◽  
Australian Case

Though there is widespread agreement that the logarithmic spot and forward rates are both integrated of order one (I(1)) variables, so that their corresponding returns are I(0) stationary, it has been recently claimed that they may be long memory. In this article, we examine this hypothesis by means of fractional integration techniques. The results based on parametric and semiparametric tests show that though fractional degrees of integration are plausible alternatives, the confidence intervals include the unit root case in both series. In addition, the hypothesis of unbiasedness of the forward rate as a forecaster for the future spot rate cannot be rejected for the Australian daily exchange rate market.

scholarly journals MODEL ESTIMASI GARCH DALAM MENGUKUR KINERJA NILAI TUKAR RUPIAH

2017 ◽  
Vol 18 (02) ◽  
pp. 52-61
Author(s):  
Imelda Saluza
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Accurate Estimation ◽  
Estimation Model ◽  
High Data ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
High Volatility ◽  
The Exchange Rate ◽  
The Impact

The exchange rate is determined by the demand and supply relationship of the currency. If the demand for a currency increases, while the supply remains or even decreases, then the exchange rate will rise vice versa. The ups and downs of exchange rates on the money market indicate the magnitude of the volatility that occurs in the currency of a State against the currencies of other countries. The volatility phenomenon indicates difficulty in analyzing the exchange rate. Increasing volatility indicates an even greater movement of currency exchange rates even if currency exchange rates experience extreme volatility resulting in economic instability both from the micro and macro sides. The high volatility seen from the pattern of price movements that occur in financial markets, and the impact that can be generated from the high volatility data is the error that will have a variance that is not constant. That is, a relatively high data variability at a time indicates the presence of heteroscedasticity. Heteroscedasticity can lead to errors in drawing a conclusion to the estimated model obtained. Therefore, we need a model that is able to solve the problem that is Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model in order to get more accurate estimation model to estimate exchange rate. From the simulation result, all data contain the volatility seen from the result of heteroscedasticity test, and obtained estimation model for all data.

scholarly journals TIME SERIES MODELS FOR FORECASTING EXCHANGE RATES

2019 ◽  
Vol 4 (8) ◽  
pp. 149-160
Author(s):  
Givi Lemonjava
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Hedge Funds ◽  
Percentage Error ◽  
Time Series Models ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Official Exchange Rate ◽  
The Mean

This paper investigates the behavior of daily exchange rate of the Georgian Currency LARI (GEL) exchange rate against the USDand EUR. To forecast exchange rates there are numerous models, which tend from very simple to very complicated models for analysis of GEL/USD and GEL/EUR time series variable. The objective of this paper is to com- pare the performance of individual time series models for predictingexchange rates. We will investigate the application of following time series analysis models: moving average, ex- ponential smoothing, double exponential smoothing adjust- ed for trend, time-series decomposition models, and ARIMA class models. The forecasting ability of these models is subsequently assessed using the symmetric loss functions which are the Mean Absolute Percentage Error (MAPE), the Mean Absolute deviation (MAD), and the Mean Squared error /deviation (MSE/MSD). In some cases, predicting the direction of exchange rate change may be valuable and profitable. Hence, it is reasonable to look at the frequency of the correctpredicted direction of change by used models, for short - FCPCD. An exchange rate represents the price of one currency in terms of another. It reflects the ratio at which one currency can be exchanged with another currency. Exchange rates forecasting is a very important and challenging subject of finance market, to determine optimal government policies as well as to make business decisions. This is important for all that firms which having their business spread over different countries or for that which raise funds in different currency. Business people mainly use exchange rates forecasting results in following types of decisions like choice currency for invoicing, pricing transactions, borrowing and landing currency choice, and management of open currency positions. The forex market is made up of banks, commercial companies, central banks, investment management firms, hedge funds, and retail forex brokers and investors. Forecasting the short- run fluctuations and direction of change of the currency ex- change rates is important for all these participates. The main goal of this study is to forecast of future ex- change rate trends by using currency rates time-series, rep- resenting past trends, patterns and waves. The monetary policy of the National Bank of Georgia since 2009 have been followed the inflation targeting regime, where exchange rate regime is floating - change of exchange rate is free. The offi- cial exchange rate of the Georgian GEL against the USD is cal- culated each business day. The official exchange rate of GEL against USD is calculated as the average weighted exchange rate of the registered spot trades on the interbank market functioning within the Bloomberg trade platform. Then, the official exchange rate of GEL against other foreign currencies is determined according to the rate on international markets on the basis of cross-currency exchange rates.

scholarly journals PENGARUH PDB, INFLASI, KURS DAN TINGKAT SUKU BUNGA TERHADAP NERACA PERDAGANGAN INDONESIA

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Ilham Tri Murdo ◽  
Junaidi Affan
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Interest Rates ◽  
Empirical Evidence ◽  
Trade Balance ◽  
Quantitative Research ◽  
Insignificant Effect ◽  
Independent Variable ◽  
The Exchange Rate

Abstract   This study is to determine the extent to which the independent variable factors (GDP, Inflation, Exchange and Interest Rates) affect the dependent variable (Trade Balance) in the last 20 years. Quantitative research aims to obtain empirical evidence regarding the effect of the variables of GDP, Inflation, Exchange Rates and Interest Rates on the Trade Balance, and also to test hypotheses to strengthen or even reject the hypothesis. With the following results: GDP has a negative and insignificant effect on the Trade Balance, Inflation has a negative and significant effect on the Trade Balance, the Exchange Rate has no and no significant effect on the Trade Balance, Interest Rates have no and no significant effect on the Trade Balance and GDP, INflation , Exchange and Interest Rates together (simultaneously) have a significant and significant effect on the Trade Balance

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 Business Days Time Series Weekly Trend and Seasonality

2021 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Karlis Gutans
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Exchange Rates ◽  
Temperature Data ◽  
Adjustment Method ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Time Series Decomposition ◽  
The World

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.

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