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Essays in international finance

Zarrabi, Nima (2016) Essays in international finance. PhD thesis, University of Essex.

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This thesis investigates three issues related to foreign exchange market. The fist issue is whether commodity prices can beat random walk benchmark by generating more accurate out-of-sample forecasts. The empirical results show that contemporaneous prices outperform the random walk at the daily frequency, however, this predictive ability disappears for monthly data. Using lagged commodity prices, we show that integrating the whole set of commodities into one large model or equally combining forecasts generated by each commodity individually improves the accuracy of the forecasts, implying outperforming the driftless random walk benchmark. The second issue is the profitability of technical trading rules in foreign exchange market and whether it is consistent with the efficient market hypothesis. The results support profitability of trading rules for different currencies. However, to determine whether one could consistently speculate in the market, we perform a persistence analysis. We construct a portfolio of outperforming rules for each currency at the end of each month and use the selected rules in the following month. These results indicate that profitability of technical trading rules are purely due to luck. The final issue is the performance of technical analysis and fundamental analysis in forecasting exchange rates. Due to parameter instability, the focus is on local forecasting performance of technical and economic models. We select models with the best performance based on three different criteria on a monthly basis and use them to generate forecasts for the next period. Our results show that if forecasts generated by selected technical and economic models are combined with equal weight, the random walk is beaten by all three criteria. These results underline the importance of considering both fundamental and technical factors in forecasting exchange rates.

Item Type: Thesis (PhD)
Subjects: H Social Sciences > HG Finance
Divisions: Faculty of Social Sciences > Essex Business School
Depositing User: Nima Zarrabi
Date Deposited: 20 Dec 2016 14:44
Last Modified: 20 Dec 2016 14:44

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