Risk measure forecast and model have been developed in order to not only provide better but also preserve its (empirical) property especially coherent property. Whilst the widely used risk of Value-at-Risk (VaR) has shown performance benefit many applications, it is fact a measure. Conditional VaR (CoVaR), defined as mean losses beyond VaR, one alternative measures that satisfies There several ...

We propose a unified machine learning model (UMLM) for two-class classification, regression and outlier (or novelty) detection via a robust optimization approach. The model embraces various machine learning models such as support vector machine-based and minimax probability machine-based classification and regression models. The unified framework makes it possible to compare and contrast existi...

We review and develop different tractable approximations to individual chance constrained problems in robust optimization on a varieties of uncertainty sets and show their interesting connections with bounds on the conditional-value-at-risk (CVaR) measure. We extend the idea to joint chance constrained problems and provide a new formulation that improves upon the standard approach. Our approach...

We are interested in model risk control problems. We study a strategy for the trader which, in a sense, guarantees good performances whatever is the unknown model for the assets of his/her portfolio. The trader chooses trading strategies to decrease the risk and therefore acts as a minimizer; the market systematically acts against the interest of the trader, so that we consider it acts as a max...

Safe exploration is regarded as a key priority area for reinforcement learning research. With separate reward and safety signals, it natural to cast constrained learning, where expected long-term costs of policies are constrained. However, can be hazardous set constraints on the signal without considering tail distribution. For instance, in safety-critical domains, worst-case analysis required ...

Markowitz model is the first modern formulation of portfolio optimization problem. Relyingon historical return of stocks as basic information and using variance as a risk measure aretow drawbacks of this model. Since Markowitz model has been presented, many effortshave been done to remove theses drawbacks. On one hand several better risk measures havebeen introduced and proper models have been ...

Comprehensive methods must be used for portfolio optimization. For this purpose, financial data of stock companies, inputs and outputs variable, the risk measure and investor’s preferences must be considered. By considering these items, we propose a method for portfolio optimization. In this paper, we used financial data of companies for screening the stock companies. We used Conditional Value ...

background: the incidence of ulcerative colitis (uc) is rising in populations with western-style diet, rich in fat and protein, and low in fruits and vegetables. in the present study, we aimed to evaluate the association between dietary protein intakes and the risk of developing incident uc.   methods : sixty two cases of uc and 124 controls were studied using country-specific food frequency qu...

Decision markets both predict and decide the future. They allow experts to predict the effects of each of a set of possible actions, and after reviewing these predictions a decision maker selects an action to perform. When the future is independent of the market, strictly proper scoring rules myopically incentivize experts to predict consistent with their beliefs, but this is not generally true...