GARCHNet: Value-at-Risk Forecasting with GARCH Models Based on Neural Networks.

This paper proposes a new GARCH specification that adapts the architecture of a long-term short memory neural network (LSTM). It is shown that classical GARCH models generally give good results in financial modeling, where high volatility can be observed. In particular, their high value is often praised in Value-at-Risk.

Role of innovation in pharmaceutical regulation: A proposal for principles to evaluate EU General Pharmaceutical Legislation from the innovator perspective.

Because the EU General Pharmaceutical Legislation is under review, the EFPIA Innovation Board developed evaluation principles for the policy proposals and key considerations on how the regulatory framework can support innovation while ensuring only safe, efficacious and quality medicines are authorized. The evaluation principles are anchored on actions to promote: agile adoption of new methodologies with soft law tools; continued emphasis on regulatory science to inform policies; a cost/benefit assessment of the new regulation to ensure they have an overall positive impact; and mitigation of any negative externalities or unintended effects for any type of innovation or products. The evaluation principles are intended to guide the impact assessment of the pharmaceutical legislation in the EU but the principles can be applied globally.

The Advantage of Case-Tailored Information Metrics for the Development of Predictive Models, Calculated Profit in Credit Scoring.

This paper compares model development strategies based on different performance metrics. The study was conducted in the area of credit risk modeling with the usage of diverse metrics, including general-purpose Area Under the ROC curve (AUC), problem-dedicated Expected Maximum Profit (EMP) and the novel case-tailored Calculated Profit (CP). The metrics were used to optimize competitive credit risk scoring models based on two predictive algorithms that are widely used in the financial industry: Logistic Regression and extreme gradient boosting machine (XGBoost).

A Probability-Based Models Ranking Approach: An Alternative Method of Machine-Learning Model Performance Assessment.

Performance measures are crucial in selecting the best machine learning model for a given problem. Estimating classical model performance measures by subsampling methods like bagging or cross-validation has several weaknesses. The most important ones are the inability to test the significance of the difference, and the lack of interpretability.