I have two datasets for G10 currencies (USD, CAD, EUR, NOK, SEK, CHF, GBP, AUD, JPY, and NZD) relating to Total investment flows into each currency for investors, representing 80% of industry AUM (Assets Under Management).
- Daily FX Rate for each expressed in terms of USD.
- Daily Flows - amounts invested by the group of investors into each individual CCY -e.g. an entry AUD 1,000,000,000 means AUD 1B. was invested into AUD on that day.
I want to build a model to predict one-week out G10 currency movements using raw flows only, i.e. assuming only Flows Data determine FX Rate (which is quite unrealistic). Flows are known between 1 and 2 weekdays after the date they are shown under in the file, i.e. there is a lag.
Additionally, what additional data could be used to improve the model?
Thanks a lot :)
A common method used to forecast exchange rates involves gathering factors that might affect currency movements and creating a model that relates these variables to the exchange rate. The factors used in [removed] models are typically based on economic theory, but any variable can be added if it is believed to significantly influence the exchange rate.
As an example, suppose that a forecaster for a Canadian company has been tasked with forecasting the USD/CAD exchange rate over the next year. They believe an econometric model would be a good method to use and has researched factors they think to affect the exchange rate. From their research and analysis, they conclude the factors that are most influential are: the [removed] between the U.S. and Canada (INT), the difference in GDP [removed] (GDP), and income growth rate (IGR) differences between the two countries. The econometric model they come up with is shown as:
After the model is created, the variables INT, GDP, and IGR can be plugged in to generate a forecast. The coefficients a, b, and c will determine how much a certain factor affects the exchange rate and direction of the effect [removed] This method is probably the most complex and time-consuming approach, but once the model is built, new data can be easily acquired and plugged in to generate quick forecasts.
Forecasting exchange rates is a very difficult task, and it is for this reason that many companies and investors simply [removed] their [removed] . However, those who see value in forecasting exchange rates and want to understand the factors that affect their movements can use these approaches as a good place to begin their research.