Effective FX Forecasting Strategies and Results
Dive into a detailed presentation on FX forecasting, including project summaries for various countries like Japan, Korea, Mexico, and the UK. Explore different forecasting models, test forecasting skills, evaluate mean squared errors, and identify the best-performing models for each country. Gain insights from assignments given to experts in the field and analyze the results for informed decision-making in currency exchange rate forecasting.
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Presentation Transcript
FX FORECASTING PRESENTATION
TABLE OF CONTENTS 01 Project Summary 02 Japan (JYP) 03 Korea (KOW) 04 Mexico (MXN) 05 United Kingdom (GBP) 06 Conclusion
Assignments Introduction 1-Test the forecasting skills of Mr. Ritz's Ricardo Casullo, president of CFS, has been models (in-sample) and Evaluate the in- contacted by Nike Inc. to forecast exchange sample performance of the models.2-Test the rates for the next five years. Ricardo Casullo forecasting skills of Mr. Ritz's models (out-of- assigned to the project Mr. Walter Ritz, Vice sample, one-step-ahead forecasts), and Check President of CFS and manager of the the quality of the forecasts by calculating the Mean-squared error (MSE) of your forecasts Forecasting Department. Mr. Ritz is an and the ability to predict the direction of expert on currency forecast who is often exchange rates.3-. Compare your model s out- quoted in the financial press and who is a of-sample performance with the out-of-sample regular on CNBC s Power Lunch. Mr. Ritz performance of the random walk model.4- first task was to review several of Nike's Using the best model, project the exchange annual reports and industry reports. rate for the GBP and JPY for next quarter, 2023:I.
MODELS: 1-RANDOM WALK 2- FORWARD RATE 3-MONETARY APPROACH 4- AD-HOC ECONOMIC MODELS
1. Mean Squared Error for RW: 35.7939 2. Mean Squared Error for Ad-hoc Model 1: 35.7470 3. Mean Squared Error for Ad-hoc Model 2: 72.2370 JAPAN (JYP) 4. Mean Squared Error for Forward rate: 71.0456 5. Mean Squared Error for Monetary approach: 72.2880
BEST MODEL: Mean Squared Error for Ad-hoc Model 1
1. Mean Squared Error for RW: 5621.24 2. Mean Squared Error for Ad-hoc Model 1: 6253.27 3. Mean Squared Error for Ad-hoc Model 2: 5668.09 KOREA (KOW) 4. Mean Squared Error for Forward rate: 9935.08 5. Mean Squared Error for Monetary approach: 9555.60
BEST MODEL: Mean Squared Error for RW
1. Mean Squared Error for RW: 2.2500 2. Mean Squared Error for Ad-hoc Model 1: 229.1412 3. Mean Squared Error for Ad-hoc Model 2: 921.0907 MEXICO (MXN) 4. Mean Squared Error for Forward rate: 0.2908 5. Mean Squared Error for Monetary approach: 37.3291
BEST MODEL: Mean Squared Error for Forward rate
1. Mean Squared Error for RW: 0.0043 2. Mean Squared Error for Ad-hoc Model 1: 0.0061 UNITED KINGDOM (GBP) 3. Mean Squared Error for Ad-hoc Model 2: 0.0042 4. Mean Squared Error for Forward rate: 0.0052 5. Mean Squared Error for Monetary approach: 0.0051
BEST MODEL: Mean Squared Error for Ad-hoc Model 2
CONCLUSION: 1-Random Walk and Adhoc usually give the most accurate prediction 2- Random walk and Adhoc usually have the same result range 3-Different types of forecasting can give big gaps of differences in-between
THE TEAM 1- Maryam Amini 2- Fatemeh Loripour 3- Kha Dang
