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 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.

scholarly journals The co-movement between copper prices and the exchange rate of five major commodity currencies

2013 ◽  
Vol 6 (3) ◽  
Author(s):  
Corlise Le Roux ◽  
Gideon Els
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
South African ◽  
The United States ◽  
Copper Price ◽  
United States Dollar ◽  
Correlation And Regression Analysis ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Commodity Currencies

In this study, the relationship between movements in the exchange rates of five commodity currencies (Australia, Canada, Chile, China, and South Africa) in terms of the United States Dollar (USD) and the spot USD copper price was analysed. Correlation and regression analysis (including the use of lagged variables) was used to investigate these relationships. It was found that four of the five commodity currency exchange rates have a strong co-movement relationship with copper price (i.e. the Australian Dollar, Canadian Dollar, Chilean Peso, and the South African Rand). The only exchange rate that does not have a co-movement relationship with copper prices is the Chinese Yuan. This article is based on a master’s minor dissertation study.

Forecasting Technique for the Production Plan of the Local Earthenware Industries of Thailand

2014 ◽  
Vol 577 ◽  
pp. 1279-1282
Author(s):  
Weerapol Namboonruang ◽  
N Amdee
Keyword(s):  
Time Series ◽  
Classical Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Time Series Models ◽  
Production Plan ◽  
Absolute Percentage Error ◽  
The Mean

The purpose of this work is to compare the forecasting of time series models between two different models. One is the classical model and another is the Box-Jenkins model. The data are calculated using the circulation of Angbuaand Ahongwhich are the local earthenware products from Ratchaburi province, Thailand. Results show that the mean absolute percentage error (MAPE) of Angbua and Ahong are 17.80, 36.12 and 16.38,17.21 respectively. Also,prediction using the Box-Jenkins Model by ARIMA form of both products are (1, 0, 0) and (1, 1, 1). From this work the Box-Jenkins Model shows more appropriate method than the classical model considered by the less error.

scholarly journals Understanding Exchange Rates: A Weakening U.S. Dollar--Good, Bad, or Indifferent for Florida Farmers and Agribusinesses?

EDIS ◽  
1969 ◽  
Vol 2005 (3) ◽  
Author(s):  
Edward A. Evans
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Resource Economics ◽  
Cooperative Extension Service ◽  
South Florida ◽  
Extension Service ◽  
University Of Florida ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Us Dollar

This publication explains the concept of fluctuating currency exchange rates, defines common terms used (such as strengthening or weakening of the dollar), discusses factors that determine the exchange rate, considers the potential implications of a weak U.S. dollar on U.S. and South Florida agriculture in general, and makes a few suggestions regarding what farmers and agribusinesses can do to protect themselves from currency fluctuations. This is EDIS document FE546, a publication of the Department of Food and Resource Economics, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL. Published April 2005. FE546/FE546: Understanding Exchange Rates: A Weakening US Dollar—Good, Bad, or Indifferent for Florida Farmers and Agribusinesses? (ufl.edu)

scholarly journals An Analysis of Time-Series Models for Age-Specific Mortality Rates in India

2020 ◽  
Vol 5 (8) ◽  
pp. 1133-1140
Author(s):  
Aritra Sen ◽  
Shalmoli Dutta
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Current Trend ◽  
Mortality Rates ◽  
Percentage Error ◽  
Time Series Models ◽  
Annual Data ◽  
Registration System ◽  
The Mean ◽  
Best Fit

Mortality is a continuous force of attrition, tending to reduce the population, a prime negative force in the balance of vital processes (Bhasin and Nag, 2004). Sample Registration System (SRS) serves as the only source of annual data on vital events on a full scale from 1969-70 in India. Few studies have examined the trends and patterns of mortality across time and regions in India (Preston and Bhat, 1984). The Under 5 Mortality Rates (U5MR) can be seen to decrease by more than half from 1970 to 2017 but in contrast little is known about the mortality patterns of the older children (5-9) and young adolescents (10-14), and not many studies have been done on their changing trends (Masquelier et al., 2018). Using the annual data for the 5-14 age, the trend of decline in the mortality patterns is studied from 1970 to 2013. The linear trend in the time series plot suggests analysis using time series models AR(p), MA(q), ARMA(p,q), Box- Jenkins ARIMA(p,d,q) and Random Walk with drift models to get the best fit to the trend of the data. The order of the time series models have been calculated by studying the ACF, PACF plots and the coefficients have been derived using the Yule-Walker equation matrix. An in-sample forecast of the years 2014-17 are taken. The Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) as a measure of accuracy is used to determine the best fit model. ARIMA(3,1,1) produced lower values making it the best-fit model. Out-of-sample forecasting was done for 2018-2025. The forecast value shows that at the current trend, India would have 0.03 deaths per 1000 population in the 5-14 age group in 2025 showing that the government’s policies and health care interventions towards realization of the MDG4 goal is working positively.

scholarly journals Modelling the Behaviour of Currency Exchange Rates with Singular Spectrum Analysis and Artificial Neural Networks

Stats ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 137-157
Author(s):  
Paulo Canas Rodrigues ◽  
Olushina Olawale Awe ◽  
Jonatha Sousa Pimentel ◽  
Rahim Mahmoudvand
Keyword(s):  
Time Series ◽  
Exchange Rates ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
The United States ◽  
Model Fit ◽  
Singular Spectrum ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Artificial Neural

A proper understanding and analysis of suitable models involved in forecasting currency exchange rates dynamics is essential to provide reliable information about the economy. This paper deals with model fit and model forecasting of eight time series of historical data about currency exchange rate considering the United States dollar as reference. The time series techniques: classical autoregressive integrated moving average model, the non-parametric univariate and multivariate singular spectrum analysis (SSA), artificial neural network (ANN) algorithms, and a recent prominent hybrid method that combines SSA and ANN, are considered and their performance compared in terms of model fit and model forecasting. Moreover, specific methodological and computational adaptations were conducted to allow for these analyses and comparisons.

Forecasting exchange rates using multivariate threshold models

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Florian Huber
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Linear Time ◽  
Time Series Models ◽  
Threshold Models ◽  
Exchange Rate Dynamics ◽  
Non Linear

AbstractThis paper investigates the ability of a broad range of non-linear time series models to forecast the EUR/USD exchange rate. Using a variant of the well-known Dornbusch (Dornbusch, R. 1976. “Expectations and Exchange Rate Dynamics.”

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 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 Bitcoin as a New Currency

2020 ◽  
Vol 20 (2) ◽  
pp. 49-65
Author(s):  
Janusz Brzeszczyński ◽  
Jerzy Gajdka ◽  
Tomasz Schabek
Keyword(s):  
Monetary Policy ◽  
Exchange Rate ◽  
Standard Deviation ◽  
Financial Crisis ◽  
Exchange Rates ◽  
Financial Market ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
National Bank

Abstract Research background: Bitcoin is the most popular financial instrument within the new cryptocurrencies class, which emerged in the wake of the financial crisis of 2007/2008. Purpose: The purpose of this paper is to provide an analysis of Bitcoin from the perspective of the Polish market investor. More specifically, the aim of the empirical research presented in this study has been twofold: (1) comparison of Bitcoin with other currencies using returns and risk captured by the standard deviation of returns and (2) assessment of the sensitivity of the BTC/PLN exchange rate to the NBP’s monetary policy announcements. Results: Bitcoin appears to be weakly related to other currency exchange rates against the Polish zloty and the monetary policy announcements of the National Bank of Poland (NBP) have, effectively, no influence on the determination of the BTC/PLN exchange rate. Novelty: We discuss extensively the Bitcoin as a new asset on the financial market and we present the investigation of the BTC/PLN reactions to the monetary policy announcements in Poland, which is a novel analysis for this instrument using the Polish market data.

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