Enter Values
Forecast Error Formulas
Forecast Accuracy Results
| Metric | Forward Rate | Fundamental |
|---|---|---|
| MAE | -- | -- |
| RMSE | -- | -- |
| MAPE | -- | -- |
| Mean Error (Bias) ? | -- | -- |
Visual Analysis
Actual vs. Forecast Exchange Rates
Absolute Forecast Errors by Period
Period-by-Period Errors
| Period | Actual | M1 Forecast | M2 Forecast | M1 Error | M2 Error |
|---|
Model Assumptions
- Both methods are evaluated on the same set of complete periods (all three values required per row)
- Error metrics assume equal weighting across all periods (no time-decay weighting)
- MAPE requires all actual rates to be positive (division by actual rate)
- Forecast errors are computed ex-post (backward-looking evaluation, not predictive)
- No adjustment for transaction costs or bid-ask spreads
- Exchange rates are spot rates unless otherwise specified by user
For educational purposes. Not financial advice. Market conventions simplified.
Understanding Forecast Error Metrics
Why Measure Forecast Errors?
Multinational corporations rely on exchange rate forecasts for hedging decisions, capital budgeting, and cash flow management. Evaluating forecast accuracy helps firms choose between competing methods (e.g., forward rates vs. fundamental analysis) and identify systematic biases that can be corrected.
RMSE: √((1/n) × Σ(Forecastt - Actualt)²)
MAPE: (1/n) × Σ(|Errort| / Actualt) × 100
Bias: (1/n) × Σ(Forecastt - Actualt)
Positive bias = over-forecasting | Negative bias = under-forecasting
MAE vs. RMSE
MAE
Mean Absolute Error
Treats all errors equally. Best when every basis point of error matters equally regardless of size. Simple to interpret.
RMSE
Root Mean Squared Error
Penalizes large errors disproportionately. Best when occasional large misses are more costly than many small errors.
Interpreting Forecast Bias
A forecast bias near zero means errors cancel out on average but does not mean forecasts are accurate. Per Madura Ch. 9, plotting forecasted vs. realized values on a 45-degree line reveals whether a method consistently over- or under-forecasts. Points consistently above the line indicate over-forecasting; below indicates under-forecasting.
Limitations
- Equal weighting of all periods may not reflect user priorities
- No adjustment for transaction costs or bid-ask spreads
- These metrics evaluate ex-post accuracy, not predictive power
- Short evaluation windows may not be representative
- Does not perform the Madura regression-based bias test
Frequently Asked Questions
Disclaimer
This calculator is for educational purposes only and computes standard forecast error metrics (MAE, RMSE, MAPE, Bias) to compare two forecasting methods. It does not generate exchange rate forecasts, make hedging recommendations, or account for transaction costs. For actual trading and hedging decisions, consult professional tools and financial advisors.
