COVID-19 spreading in financial networks: A semiparametric matrix regression model

Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. A new Bayesian semiparametric model for temporal multilayer networks with both intra- and inter-layer connectivity is proposed. A hierarchical mixture prior distribution is assumed to capture heterogeneity in the response of the network edges to a set of risk factors including the number of COVID-19 cases in Europe. Two layers, defined by stock returns and volatilities are considered and within and between layers connectivity is investigated. The financial connectedness arising from the interactions between two layers is measured. The model is applied in order to compare the topology of the network before and after the spreading of the COVID-19 disease

Buildings’ Energy Efficiency and the Probability of Mortgage Default: The Dutch Case

We investigate the relationship between building energy efficiency and the probability of mortgage default. To this end, we construct a novel panel data set by combining Dutch loan-level mortgage information with provisional building energy ratings provided by the Netherlands Enterprise Agency. Using the logit regression and the extended Cox model, we find that building energy efficiency is associated with a lower probability of mortgage default. There are three possible channels that might drive the results: (i) personal borrower characteristics captured by the choice of an energy-efficient building, (ii) improvements in building performance that could help to free-up the borrower’s disposable income, and (iii) improvements in dwelling value that lower the loan-to-value ratio. We address all three channels. In particular, we find that the default rate is lower for borrowers with less disposable income. The results hold for a battery of robustness checks. This suggests that the energy efficiency ratings complement borrowers’ credit information and that a lender using information from both sources can make superior lending decisions than a lender using only traditional credit information. These aspects are not only crucial for shaping future energy policy, but also have implications for the risk management of European financial institutions

A Matrix-Variate t Model for Networks

Networks represent a useful tool to describe relationships among financial firms and network analysis has been extensively used in recent years to study financial connectedness. An aspect, which is often neglected, is that network observations come with errors from different sources, such as estimation and measurement errors, thus a proper statistical treatment of the data is needed before network analysis can be performed. We show that node centrality measures can be heavily affected by random errors and propose a flexible model based on the matrix-variate t distribution and a Bayesian inference procedure to de-noise the data. We provide an application to a network among European financial institutions

COVID-19 spreading in financial networks: A semiparametric matrix regression model

Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. In this paper, we propose a new semiparametric model for temporal multilayer causal networks with both intra- and inter-layer connectivity. A Bayesian model with a hierarchical mixture prior distribution is assumed to capture heterogeneity in the response of the network edges to a set of risk factors including the European COVID-19 cases. We measure the financial connectedness arising from the interactions between two layers defined by stock returns and volatilities. In the empirical analysis, we study the topology of the network before and after the spreading of the COVID-19 disease

Performance simulation of climate adaptive building shells - Smart Energy Glass as a case study

As opposed to traditional building shells, climate adaptive building shells (CABS) do have the ability to change their properties and behavior over time. Provided they are designed and operated effectively, CABS offer the potential for energy savings without the need for compromising comfort levels. This paper explores the role that building performance simulation (BPS) can play in designing CABS. After analyzing the distinguishing characteristics of CABS, the need for BPS is introduced. The potential role of BPS is then illustrated via the case study of Smart Energy Glass. Based on a description of underlying physics, the model abstraction process is discussed first. This results in an integrated model for performance simulations that couples TRNSYS and DAYSIM. This model is empirically validated and subsequently used to evaluate the potential of Smart Energy Glass in a renovation case under various operational scenario’s. The paper concludes with some suggestions for future research and development of Smart Energy Glass

Opinion Dynamics and Disagreements on Financial Networks

We propose a new measure of disagreement based on connectedness, which generalizes the disagreement index introduced in Billio et al. (2018). Building on the lifting approach in Hendrickx (2014), we extend Billio et al. (2018) to signed networks, which allows us to consider more general consensus dynamics and disagreement with antagonistic behaviour. Synthetic and real-world financial networks of serial correlation are considered for illustrating the new measure and for studying opinion dynamics and convergence to consensus on prices for financial assets

Towards the application of distributed simulation in HAM engineering

This paper presents ongoing research about an integrated approach to perform high resolution heat, air and moisture (HAM) simulation of whole buildings. There are several HAM modelling tools, with different space and time resolution. The integrated approach establishes run-time external coupling of existing tools (building envelope HAM, BES, CFD) and utilizes the capabilities of one tool in an attempt to compensate the deficiencies of the other. The paper presents the literature review of approaches for domain integration, the physical processes as dealt with by existing tools, coupling requirements and it addresses the importance of validation and coupling necessity decision procedures

Uncertainty in airflow rate calculations due to the use of surface-averaged pressure coefficients

Mean wind pressure coefficients (Cp) are key input parameters for air infiltration and ventilation studies. However, building energy simulation and stand-alone airflow network programs usually only provide and/or use a limited amount of Cp data, which are based on several assumptions. An important assumption consists of using surface-averaged Cp values instead of local Cp values with a high resolution in space. This paper provides information on the uncertainty in the calculated airflow rate due to the use of surface-averaged Cp data. The study is performed using published empirical data on pressure coefficients obtained from extensive wind tunnel experiments. The uncertainty is assessed based on the comparison of the airflow rate () calculated using the surface-averaged Cp values (AV) and the airflow rate calculated using local Cp values (LOC). The results indicate that the uncertainty with a confidence interval of 95% is high: 0.23 AV <LOC <5.07 AV. In cases with the largest surface-averaged ¿Cp, the underestimation or overestimation is smaller but not negligible: 0.52 AV <LOC <1.42 AV. These results provide boundaries for future improvements in Cp data quality, and new developments can be evaluated by comparison with the uncertainty of the current methods

On the" mementum" of Meme Stocks

The meme stock phenomenon has yet to be explored. In this note, we provide evidence that these stocks display common stylized facts for the dynamics of price, trading volume, and social media activity. Using a regime-switching cointegration model, we identify the meme stock “mementum” which exhibits a different characterization compared to other stocks with high volumes of activity (persistent and not) on social media. Finally, we show that mementum is significant and positively related to the stock’s returns. Understanding these properties helps investors and market authorities in their decisions