Forecasting daily volatility using high frequency financial data

Aiming at empirical findings, this work focuses on applying the HEAVY model for daily volatility with financial data from the Brazilian market. Quite similar to GARCH, this model seeks to harness high frequency data in order to achieve its objectives. Four variations of it were then implemented and their fit compared to GARCH equivalents, using metrics present in the literature. Results suggest that, in such a market, HEAVY does seem to specify daily volatility better, but not necessarily produces better predictions for it, what is, normally, the ultimate goal. The dataset used in this work consists of intraday trades of U.S. Dollar and Ibovespa future contracts from BM&FBovespa.

Dual resource transfers and interruption in external debt service

Externai debt service requires a dual resource transfer. Trade surpluses have to be generated in order to make foreign exchange revenues available for debt repayment. In addition, with developing countries' externai debt being largely a public liability, debt service requires that resources can be effectively transferred from the private to the public sector. This paper derives a statistical model for dealing with dual constraints in the presence of binary dependent variables and applies it to the dual resource transfer problem. The results from the estimation of the model for a sample of 31 middle-income developing countries in the period of 1980 to 1990, strongly support the hypothesis that both externai and fiscal constraints are important in explaining externai debt service disruptions.