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Title: Exchange rate forecasts and forecasting
Author: Marsh, Ian William
ISNI:       0000 0001 3619 4730
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 1994
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This thesis is concerned with forecasting key floating exchange rates. The first half is based on the predictions of almost two hundred forecasters, working in banks, industrial companies, chambers of commerce and specialist forecasting agencies. It demonstrates that individual forecasters interpret commonly available information differently, and that these differences of opinion translate into economically meaningful heterogeneity in forecast performance - some forecasters are significantly more accurate than others. It also shows that the dispersion of forecasts helps to explain turnover in the foreign exchange futures market. The notion that the best predictive model of the exchange rate is a random walk has stood the test of time. In chapter three we evaluate the forecasts of our panellists based on a variety of metrics, using the random walk as a benchmark. Over short horizons (three months) the random walk remains preeminent, but over a one year horizon several forecasters demonstrate an ability to outperform. In an attempt to overturn the short horizon results we combine forecasts using several techniques in chapter four, but to no avail. It would appear that we are unable to find any information that is not discounted into the current spot rate but which is relevant over short forecast intervals. The second half of the thesis builds three exchange rate models based on an augmented theory of purchasing power parity, with which we forecast key rates. The five variable, simultaneous equation models each incorporate long-run equilibria characterised by economically meaningful restrictions, and complex short term dynamics. The thesis demonstrates that these models are capable of generating fully dynamic forecasts which rank very favourably when compared to our panellists. More tellingly, it also shows that the forecasts are significantly better than a random walk over all but the shortest of horizons.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Models