Interactivity, Fairness and Explanations in Recommendations

More and more aspects of our everyday lives are influenced by automated decisions made by systems that statistically analyze traces of our activities. It is thus natural to question whether such systems are trustworthy, particularly given the opaqueness and complexity of their internal workings. In this paper, we present our ongoing work towards a framework that aims to increase trust in machine-generated recommendations by combining ideas from three separate recent research directions, namely explainability, fairness and user interactive visualization. The goal is to enable different stakeholders, with potentially varying levels of background and diverse needs, to query, understand, and fix sources of distrust.acceptedVersionPeer reviewe

A market risk model for asymmetric distributed series of return

In this paper we propose to model short-term interest rates by taking into consideration both the asymmetric properties of returns, using Pearson’s type IV distribution, and the time-varying volatility, using GARCH models. We show that conditional skewness is negatively related to spot price interest rates and that negative conditional skewness can lead the process to generate steady returns

Volatility spillovers and price interdependencies; a dynamic non parametric approach

This paper investigates the volatility spillovers of four major equity markets using a new approach namely, the Filtered Historical Simulation approach (FHS). The FHS captures very effectively the changes and interactions in the first and second moments. A dynamic system based on Filtered Historical Simulation (FHS) and nonparametric regression is used to obtain estimates of the variance-covariance of the set of standardised residuals. This system is then used to examine dependencies in covariance changes and to carry an impulse response analysis to investigate the dynamic responses to volatility shocks

VaR without correlations for portfolio of derivative securities

We propose filtering historical simulation by GARCH processes to model the future distribution of assets and swap values. Options’ price changes are computed by full reevaluation on the changing prices of underlying assets. Our methodology takes implicitly into account assets’ correlations without restricting their values over time or computing them explicitly. VaR values for portfolios of derivative securities are obtained without linearising them. Historical simulation assigns equal probability to past returns, neglecting current market conditions. Our methodology is a refinement of historical simulation

Coherent Risk Measures Under Filtered Historical Simulation

Recent studies have strongly criticised conventional VaR models for not providing a coherent risk measure. Acerbi provides the intuition for an entire family of coherent measures of risk known as "spectral risk measures" [Spectral measures of risk: A coherent representation of subjective risk aversion. Journal of Banking and Finance 26 (7) (2002) 1505-1518]. In this study we illustrate how the Filtered Historical Simulation [Barone-Adesi, G., Bourgoin, F., Giannopoulos, K., 1998. Don't look back. Risk 11, 100-104; Barone-Adesi, Giannopoulos, K., Vosper, L., 1999. VaR without correlations for non-linear portfolios. Journal of Futures Markets 19, 583-602], can provide an improved methodology for calculating the Expected Shortfall. Thereafter, we prove that these new risk measures are spectral and are coherent as well, following Acerbi. Furthermore, we provide the statistical error formula that allows to calculate the error for our model

Modelling of Global Energy Demand in the Transportation Sector: A Country by Country Approach

This study deals with the modelling of the future global energy demand inthe transportation sector, how and in what extend this could be limited andwhich low carbon fuels and technologies could contribute to the mitigation ofthe energy demand. In the introduction of the current report, the greenhouseeffect, the global warming and the climate change are defined. Then, theactions taken and the agreements made to deal with global warming andclimate change are presented and the need for combating them is highlighted.After the introduction, a literature review is conducted in order to spotthe knowledge gap and formulate the research questions. In the literaturereview, the ICCT’s and IEA’s studies are mainly discussed due to their highquality research and the big amount of published reports. More specifically,their models, scenarios, policies and results are discussed in detail inorder to accurately define the knowledge gap and formulate the researchquestions. Thus, the goal and the main research question of this researchis to answer how could the future global energy demand be mitigated andwhich low carbon fuels and technologies could significantly penetrate intothe transportation sector.In the beginning of this research, the conceptual and the theoreticalframeworks are presented in order to assist with the outline of the thesisand create the theoretical background for the development of the model.Then, in order for the research questions to be answered, a forecasting modelcalculating the transportation future energy demand by country, transportmode, technology and energy carrier throughout the period 2015-2050 is developed.The main features that distinguish this model from models used insimilar studies are the strong focus on the diffusion of low carbon fuels andtechnologies, the use of a different diffusion model (the Bass s-curve) andthe country by country with one-year time increments approach.After the model is verified and validated through comparison with similarstudies and a sensitivity analysis, results are presented for two differentscenarios. The first scenario is called Current Policies scenario and aims toshow a potential pathway of the future global energy demand in the transportationsector that could happen if no more policies are applied after 2020and the second scenario is called Accelerated Policies scenario and its targetis to represent a pathway that could happen if new policies are adopted andstricter implementation is applied.The results of the two scenarios show that it is possible to achieve abending of the energy demand and a diffusion enhancement of low carbonfuels and technologies if new and stricter policies that motivate technologyimprovements and fossil vehicles ban are applied. In particular, the resultsindicate that the implementation of new and stricter policies, which couldlead to efficiency improvements, and to more effective diffusion of low carbonfuels and technologies could achieve reduction of the energy demand after 2029. Moreover, according to the results, electricity is expected to dominate in the transportation sector, while biofuels, hydrogen and ammonia are alsoexpected to be highly used. However, without further policy action, theglobal energy demand is expected to follow a constantly increasing trendin the future while the penetration of low carbon fuels is expected to besignificantly lower.Electrical Engineering | Sustainable Energy Technolog

Portfolio selection under VaR constraints

In this paper we show that by assuming a constant variance/covariance matrix over the holding period, the VaR limits can often be exceeded within the relevant horizon period. To minimize this risk, we formulate the problem in terms of portfolio selection and propose an innovative methodology using conditional VaR that minimizes the VaR at each point of the holding period. We rewrite the optimisation problem by taking into consideration the variability of risk on all assets eligible to be included in the portfolio. Copyright Springer-Verlag Berlin/Heidelberg 2005VaR, portfolio selection, Monte-Carlo simulation, conditional heteroskedasticity,