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 :)