A aplicação de métricas de risco na EDP é fundamental não só para apoio à decisão sobre operações de Trading no mercado de electricidade e de combustíveis, como também, na tomada de decisões no âmbito do Plano de Negócios com base numa avaliação periódica do nível de risco de...

The catastrophic failures of risk management systems in 2008 bring to the forefront the need for accurate and flexible estimators of market risk. Despite advances in the theory and practice of evaluating risk, existing measures are notoriously poor predictors of loss in high-quantile events. To...

The proper forecasting of listed companies' earnings is crucial for their appropriate pricing. This paper compares forecast errors of different univariate time-series models applied for the earnings per share (EPS) data for Polish companies from the period between the last financial crisis of...

This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm, known as the kernel-regularized least squares...

The authors use the Financial Stress Index created by the International Monetary Fund to predict the likelihood of financial stress events for five developed countries: Canada, France, Germany, the United Kingdom and the United States. They use a semiparametric panel data model with...

The objective of this paper is to propose an early warning system that can predict the likelihood of the occurrence of financial stress events within a given period of time. To achieve this goal, the signal extraction approach proposed by Kaminsky, Lizondo and Reinhart (1998) is used to monitor...

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...

The purpose of this paper is to investigate whether a dynamic Value at Risk model and high frequency realized volatility models can improve the accuracy of 1-day ahead VaR forecasting beyond the performance of frequently used models. As such, this paper constructs 60 conditional volatility...

Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR). We provide a comprehensive look at the problem by considering...

We present in this paper an alternative approach to determining and predicting the fluctuations in the daily prices and stock returns of a first-generation bank in the Nigerian Stock Market (NSM). The approach uses a three-state Markov to estimate the expected duration of the asset returns in...