Efficient Estimation of Linear Asset Pricing Models with Moving-Average Errors

This paper explores in depth the nature of the conditional moment restrictions implied by log-linear intertemporal capital asset pricing models (ICAPMs) and shows that the generalized instrumental variables (GMM) estimators of these models (as typically implemented in practice) are inefficient. The moment conditions in the presence of temporally aggregated consumption are derived for two log-linear ICAPMs. The first is a continuous time model in which agents maximize expected utility. In the context of this model, we show that there are important asymmetries between the implied moment conditions for infinitely and finitely-lived securities. The second model assumes that agents maximize non-expected utility, and leads to a very similar econometric relation for the return on the wealth portfolio. Then we describe the efficiency bound (greatest lower bound for the asymptotic variances) of the CNN estimators of the preference parameters in these models. In addition, we calculate the efficient CNN estimators that attain this bound. Finally, we assess the gains in precision from using this optimal CNN estimator relative to the commonly used inefficient CMN estimators.

Winter is possibly not coming : mitigating financial instability in an agent-based model with interbank market

We develop a macroeconomic agent-based model to study how financial instability can emerge from the co-evolution of interbank and credit markets and the policy responses to mitigate its impact on the real economy. The model is populated by heterogenous firms, consumers, and banks that locally interact in dfferent markets. In particular, banks provide credit to firms according to a Basel II or III macro-prudential frameworks and manage their liquidity in the interbank market. The Central Bank performs monetary policy according to dfferent types of Taylor rules. We find that the model endogenously generates market freezes in the interbank market which interact with the financial accelerator possibly leading to firm bankruptcies, banking crises and the emergence of deep downturns. This requires the timely intervention of the Central Bank as a liquidity lender of last resort. Moreover, we find that the joint adoption of a three mandate Taylor rule tackling credit growth and the Basel III macro-prudential frame-work is the best policy mix to stabilize financial and real economic dynamics. However, as the Liquidity Coverage Ratio spurs financial instability by increasing the pro-cyclicality of banks’ liquid reserves, a new counter-cyclical liquidity buffer should be added to Basel III to improve its performance further. Finally, we find that the Central Bank can also dampen financial in- stability by employing a new unconventional monetarypolicy tool involving active management of the interest-rate corridor in the interbank market

Sensoring a Generative System to Create User-Controlled Melodies

[EN] The automatic generation of music is an emergent field of research that has attracted the attention of countless researchers. As a result, there is a broad spectrum of state of the art research in this field. Many systems have been designed to facilitate collaboration between humans and machines in the generation of valuable music. This research proposes an intelligent system that generates melodies under the supervision of a user, who guides the process through a mechanical device. The mechanical device is able to capture the movements of the user and translate them into a melody. The system is based on a Case-Based Reasoning (CBR) architecture, enabling it to learn from previous compositions and to improve its performance over time. The user uses a device that allows them to adapt the composition to their preferences by adjusting the pace of a melody to a specific context or generating more serious or acute notes. Additionally, the device can automatically resist some of the user’s movements, this way the user learns how they can create a good melody. Several experiments were conducted to analyze the quality of the system and the melodies it generates. According to the users’ validation, the proposed system can generate music that follows a concrete style. Most of them also believed that the partial control of the device was essential for the quality of the generated music

Di-ANFIS: an integrated blockchain–IoT–big data-enabled framework for evaluating service supply chain performance

Service supply chain management is a complex process because of its intangibility, high diversity of services, trustless settings, and uncertain conditions. However, the traditional evaluating models mostly consider the historical performance data and fail to predict and diagnose the problems’ root. This paper proposes a distributed, trustworthy, tamper-proof, and learning framework for evaluating service supply chain performance based on Blockchain and Adaptive Network-based Fuzzy Inference Systems (ANFIS) techniques, named Di-ANFIS. The main objectives of this research are: 1) presenting hierarchical criteria of service supply chain performance to cope with the diagnosis of the problems’ root; 2) proposing a smart learning model to deal with the uncertainty conditions by a combination of neural network and fuzzy logic, 3) and introducing a distributed Blockchain-based framework due to the dependence of ANFIS on big data and the lack of trust and security in the supply chain. Furthermore, the proposed six-layer conceptual framework consists of the data layer, connection layer, Blockchain layer, smart layer, ANFIS layer, and application layer. This architecture creates a performance management system using the Internet of Things (IoT), smart contracts, and ANFIS based on the Blockchain platform. The Di-ANFIS model provides a performance evaluation system without needing a third party and a reliable intermediary that provides an agile and diagnostic model in a smart and learning process. It also saves computing time and speeds up information flow.Service supply chain management is a complex process because of its intangibility, high diversity of services, trustless settings, and uncertain conditions. However, the traditional evaluating models mostly consider the historical performance data and fail to predict and diagnose the problems’ root. This paper proposes a distributed, trustworthy, tamper-proof, and learning framework for evaluating service supply chain performance based on Blockchain and Adaptive Network-based Fuzzy Inference Systems (ANFIS) techniques, named Di-ANFIS. The main objectives of this research are: 1) presenting hierarchical criteria of service supply chain performance to cope with the diagnosis of the problems’ root; 2) proposing a smart learning model to deal with the uncertainty conditions by a combination of neural network and fuzzy logic, 3) and introducing a distributed Blockchain-based framework due to the dependence of ANFIS on big data and the lack of trust and security in the supply chain. Furthermore, the proposed six-layer conceptual framework consists of the data layer, connection layer, Blockchain layer, smart layer, ANFIS layer, and application layer. This architecture creates a performance management system using the Internet of Things (IoT), smart contracts, and ANFIS based on the Blockchain platform. The Di-ANFIS model provides a performance evaluation system without needing a third party and a reliable intermediary that provides an agile and diagnostic model in a smart and learning process. It also saves computing time and speeds up information flow

0028/2009 - Problemas na Elicitação de Requisitos: Uma visão de pesquisa/literatura

A primeira fase na engenharia de requisitos é a elicitação de requisitos, na qual as informações sobre as necessidades do cliente são adquiridas, sendo crucial e crítica e podendo comprometer todas as etapas subseqüentes do desenvolvimento. O presente relatório apresenta um levantamento dos problemas que ocorrem durante a elicitação de requisitos citados na literatura da área

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Deep learning for real-time traffic signal control on urban networks

Real-time traffic signal controls are frequently challenged by (1) uncertain knowledge about the traffic states; (2) need for efficient computation to allow timely decisions; (3) multiple objectives such as traffic delays and vehicle emissions that are difficult to optimize; and (4) idealized assumptions about data completeness and quality that are often made in developing many theoretical signal control models. This thesis addresses these challenges by proposing two real-time signal control frameworks based on deep learning techniques, followed by extensive simulation tests that verifies their effectiveness in view of the aforementioned challenges. The first method, called the Nonlinear Decision Rule (NDR), defines a nonlinear mapping between network states and signal control parameters to network performances based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past. The NDR is implemented and tested within microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization procedure aiming to reduce delay, CO2 and black carbon emissions. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in optimizing different traffic and environmental objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The second method, called the Advanced Reinforcement Learning (ARL), employs the potential-based reward shaping function using Q-learning and 3rd party advisor to enhance its performance over conventional reinforcement learning. The potential-based reward shaping in this thesis obtains an opinion from the 3rd party advisor when calculating reward. This technique can resolve the problem of sparse reward and slow learning speed. The ARL is tested with a range of existing reinforcement learning methods. The results clearly show that ARL outperforms the other models in almost all the scenarios. Lastly, this thesis evaluates the impact of information availability and quality on different real-time signal control methods, including the two proposed ones. This is driven by the observation that most responsive signal control models in the literature tend to make idealized assumptions on the quality and availability of data. This research shows the varying levels of performance deterioration of different signal controllers in the presence of missing data, data noise, and different data types. Such knowledge and insights are crucial for real-world implementation of these signal control methods.Open Acces

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches