Eva Rytter Sunesen (Department of Economics, University of Copenhagen)
Abstract
This paper offers two refinements of the traditional risk measure based on the volatility of growth. First, we condition GDP growth on structural characteristics of the host country that move only slowly and therefore can be partly predicted by an investor. Second, we adjust conditional risk for the systematic components due to the global and regional interdependence between alternative investment locations. The decomposition of conditional risk into its systematic and idiosyncratic components reveals that not only are African countries on average characterised by a larger conditional risk than Asian and Latin American countries, but the idiosyncratic risk factor also represents a larger share than in other developing countries. As a final contribution, we search the empirical literature on foreign direct investment and risk in order to determine which of the suggested risk measures provide the best description of idiosyncratic risk. Using a general-to-specific methodology, we find that both economic and political risk factors are important elements in the investment decision. We also find that commercial risk factors applied in the literature so far are poor determinants of idiosyncratic risk.
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Publisher Info
Paper provided by University of Copenhagen. Department of Economics in its series Discussion Papers with number
06-20.
Length: 18 pages Date of creation: Oct 2006 Date of revision: Handle: RePEc:kud:kuiedp:0620
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Find related papers by JEL classification: E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles F21 - International Economics - - International Factor Movements and International Business - - - International Investment; Long-Term Capital Movements O16 - Economic Development, Technological Change, and Growth - - Economic Development - - - Financial Markets; Saving and Capital Investment C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data
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