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.

A Metric for Money

2016 ◽  
Vol 15 (1-2) ◽  
pp. 38-59
Author(s):  
Bob Blain
Keyword(s):  
Exchange Rates ◽  
Measuring Instrument ◽  
Work Time ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
National Currencies ◽  
Price Indexes ◽  
The World ◽  
Money Unit ◽  
The International Monetary Fund

The argument advanced here is that money worldwide has no recognized metric, no measuring instrument that defines the value of national currencies. In place of a metric, economists have substituted price indexes and monetary policy. Unanchored to economic realities, money numbers have dispersed in ever-greater inequality. At the same time, the proper metric for money has been the center of gravity of currency exchange rates, unrecognized, the de facto world money metric, for as long as the International Monetary Fund has published the data to see it. That metric is work time. It now needs to be adopted de jure as the world money unit.

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.

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.

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