For many applications of machine learning the goal is to predict the value of a vector c given the value of a vector x of input features. In a classification problem c represents a discrete class label, whereas in a regression problem it corresponds to one or more continuous variables. From a probabilistic perspective, the goal is to find the conditional distribution p(c|x). The most common app...

For many decision making problems under uncertainty, it is crucial to develop risk-averse models and specify the decision makers’ risk preferences based on multiple stochastic performance measures (or criteria). Incorporating such multivariate preference rules into optimization models is a fairly recent research area. Existing studies focus on extending univariate stochastic dominance rules to ...

C/C++ code relying on preprocessing can be quite complex to analyze. This is often due to free preprocessing variables set at compile time. In general, preprocessing selectively compile parts of the source code based on the values of preprocessing variables which may be free. In this case, the relations between these parts can only be represented by conditional expressions using the free variab...

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A two-sided Weibull is developed to model the conditional financial return distribution, for the purpose of forecasting Value at Risk (VaR) and conditional VaR. A range of conditional return distributions are combined with four volatility specifications to forecast tail risk in four international markets, two exchange rates and one individual asset series, over a four year forecast period that ...

Conditional Value-at-Risk Constrained Optimization of a Power Portfolio Published in International Journal of Applied Decision Sciences • A predictive and risk analytics tool guiding cash management of warranty reserves, covering contingent liabilities. • Finding a balance between excess reserves causing opportunity cost, and not enough reserves necessitating emergency funds. • The decision mak...

The concept of Conditional Value-at-Risk (CVaR) is used in various applications in uncertain environment. This paper introduces CVaR norm for a random variable, which is by de nition CVaR of absolute value of this random variable. It is proved that CVaR norm is indeed a norm in the space of random variables. CVaR norm is de ned in two variations: scaled and non-scaled. L-1 and L-in nity norms a...

Financial news carry information about economical figures and indicators. However, these texts are mostly unstructured and consequently hard to be processed in an automatic way. In this paper, we present a representation formalism that supports a linguistic composition for machine learning tasks. We show an innovative approach to structuring financial texts by extracting principal indicators. C...

This paper examines a new approach for credit risk optimization. The model is based on the Conditional Value-at-Risk (CVaR) risk measure, the expected loss exceeding Value-at-Risk. CVaR is also known as Mean Excess, Mean Shortfall, or Tail VaR. This model can simultaneously adjust all positions in a portfolio of financial instruments in order to minimize CVaR subject to trading and return const...

This paper provides an investigation of the economic value of multivariate volatility forecasting ability using a testing framework in which the quality of competing methods is assessed from a conditional investment perspective. In doing this we are able to provide a novel means by which the benefits of using a particular set of volatility forecasts can be assessed. The results associated with ...