A stable and robust model to estimate the fair value of FX pairs
Easy access to experts at a fraction of the global cost
- A leading European buy-side bank wanted to develop a fair value model for major FX pairs to check if any currency pair was undervalued or overvalued relative to its long-run fair value
- The bank also wanted to develop a quantitative method to predict the time it would take for the actual FX price to converge with its fair value
- Data collection from public sources and other data subscriptions
- Data cleaning for model input
- Data manipulation and transformation for model development
- Data maintenance for future iterations and model validation
- Model fitting
- Use of Behavioural Equilibrium Exchange Rate (BEER) approach.
- Identification of four significant predictors for FX rates.
- Cross-section OLS regression for individual FX pairs.
- Panel regression for G10 currencies.
- Use of Vector Error Correction Model (VECM) to calculate speed of convergence to fair value and provide forecasts of fair value FX rates
- Model development using R and Python
- Results interpretation and publishing
- Helped client publish the findings of the model in research papers and other publications.
- Maintained the model to keep it up to date and in line with econometric assumptions.
- Tested tweaks in the model to improve forecasting accuracy.
- A stable and robust model to estimate the fair value of FX pairs and provide direction on FX price movements in the medium to longer term.
- Easy access to experts at a fraction of the global cost.
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