The Brazilian Currency Turmoil of 2002: A Nonlinear Analysis

This paper investigates the main sources of instability in Brazil during the currency and financial distress episode of 2002. We test for financial contagion from the Argentine crisis and the impact of factors including IMF intervention and political uncertainty in raising the probability of crisis. The empirical investigation employs a Markov switching model with endogenous transition probabilities.Brazil; contagion; financial crises; IMF intervention; Markov switching model; political uncertainty.

Adaptive conflict-free optimization of rule sets for network security packet filtering devices

Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices

Forecasting the Short-term Value of Wind Power for Risk-aware Bidding Strategies in Single-imbalance Price Electricity Markets

The participation of wind energy in electricity markets and strategic bidding in the day-ahead market has been investigated with growing interest in recent years. However, markets adopting a single-price imbalance settlement where participants can increase their profits if they help to put the system back into balance have received very limited attention in the academic literature. In this thesis, new probabilistic models forecasting the short-term value of wind power are developed and their use in bidding in these types of markets is investigated. The proposed strategies are designed for participants who want to bid strategically in the day-ahead market to increase the value of the energy generated at a wind farm, where value is measured in terms of revenue and exposure to risk. Following an extensive analysis of the available market data, two alternative approaches are developed to generate day-ahead forecasts of the market quantities of relevance for the work. These forecasts are then combined with short-term predictions of wind power in a probabilistic framework. Bids are formulated to reflect the participant\u27s risk profile, conditioned upon the uncertainty in future wind power generation and electricity market conditions. The methodology is applied to a case study where the participation of a real wind farm in the new Irish electricity market is simulated over a test period. The benefits of the proposed models are clearly demonstrated as the strategies successfully improve the value of wind power for the participant by increasing their revenue while reducing the exposure to risk. Moreover, the market quantity forecasts developed in this work prove to be more valuable than a wind power forecast of higher accuracy for a risk-seeking participant