External coupling of building energy simulation and building element heat, air and moisture simulation

The present challenge for the building industry is to provide buildings that are safe, comfortable, energy efficient and sustainable; creating a healthy and productive environment for users. Computational building performance simulation (CBPS) can play an important role to deal with this challenge, particularly for performance indicators related to the heat, air and moisture (HAM) at the whole-building scale. These indicators are currently assessed by a large number of programs with considerable uncertainty in their results, such as building energy simulation (BES) programs and building element heat, air and moisture (BEHAM) programs. This thesis poses the hypothesis that the lack of integration between these programs represents an important source of uncertainty in whole-building HAM simulation, which can compromise, in some circumstances, the accuracy of their results. In order to test this hypothesis, this thesis proposes, implements, verifies and validates protocols to integrate BES and BEHAM programs using external coupling. These protocols, which are based on literature review and theoretical analysis of the governing equations, are implemented in prototype computer programs using numerical simulation and inter-process communication routines. The prototypes are verified by a number of techniques developed in this thesis, such as the use of emulators, one-way coupling and self-coupling. Validation is carried out using analytical solutions, inter-model comparison and experimental results reported in the literature. Coupled BES-BEHAM simulations showed improvements in the accuracy when compared to stand-alone BES or BEHAM simulations. In order to identify cases where coupled BES-BEHAM simulations provide significant improvement in the results, the coupling necessity decision procedure (CNDP) is formulated. Capabilities of coupled BES-BEHAM simulations in combination with the CNDP are demonstrated by case studies, where some capabilities and deficiencies of stand-alone programs are also evaluated. This research concludes that coupled BES-BEHAM simulation provides a viable and reliable way to perform whole-building HAM simulation. A number of additional results are also provided in this thesis, such as the solution for several coupling features addressed in the coupling protocols, the verification techniques developed and the use of TCP/IP sockets for the communication between the programs

Asymmetry and leverage in GARCH models: a News Impact Curve perspective

Models for conditional heteroskedasticity belonging to the GARCH class are now common tools in many economics and finance applications. Among the many possible competing univariate GARCH models, one of the most interesting groups allows for the presence of the so-called asymmetry or leverage effect. In our view, asymmetry and leverage are two distinct phenomena, both inspired by the seminal work of Black in 1976. We propose definitions of leverage and asymmetry that build on the News Impact Curve, allowing to easily and coherently verify if they are both present. We show that several GARCH models are asymmetric but none is allowing for a proper leverage effect. Finally, we extend the leverage definition to a local leverage effect and show that the AGARCH model is coherent with the presence of local leverage. An empirical analysis completes the paper

Bayesian SAR model with stochastic volatility and multiple time-varying weights

A novel spatial autoregressive model for panel data is introduced, which incorporates multilayer networks and accounts for time-varying relationships. Moreover, the proposed approach allows the structural variance to evolve smoothly over time and enables the analysis of shock propagation in terms of time-varying spillover effects. The framework is applied to analyse the dynamics of international relationships among the G7 economies and their impact on stock market returns and volatilities. The findings underscore the substantial impact of cooperative interactions and highlight discernible disparities in network exposure across G7 nations, along with nuanced patterns in direct and indirect spillover effects

Essays in Financial Econometrics

The present doctoral thesis covers different aspects in the financial econometrics area. In particular, the research focuses on the heterogeneous agents in the market (rational and behavioural), the performance measures related to this type of agents and, more generally, the asset evaluation within a portfolio selection framework. Further, the time varying dependence among the financial markets is also considered. In general, the financial markets represent one of the main indicators for the dynamics of the business cycle as noted by Siegel (1991). Viceversa, Hamilton and Lin (1998), for example, found that economic recessions are the main factor that leads the fluctuations in the volatility of stock returns. Therefore, there is evidence for an interdependence relationship among the economic cycle and the financial markets. In this context, it is interesting to analyze the markets by looking at investors as decision makers in the asset selection process. Moreover, the time varying dependence among the financial markets could imply a change in the portfolio in term of diversification, with effects on investors' portfolios. The first chapter presents a rational learning model which considers the information coming to a HARA investor from a behavioural counterpart. The main goal is to investigate this component's effect, in terms of utility function, on asset evaluation during the allocation process. This heterogeneous framework has two types of agents with two different utility functions, a rational agent with a hyperbolic absolute risk aversion (HARA) utility function and the second with a general behavioural utility function. To compare the assets, each agent uses the concept of performance measure related to utility functions. The higher the measure, the higher the expected utility of a given asset. The HARA agent is a rational learner agent. The rational learning is defined as the process undertaken through Bayesian updating of the prior beliefs. The prior beliefs derive by the utility function of the rational agent and the updating process of beliefs takes place through the presence of the behavioural counterpart. The choice is conditioned by adopting an Herding behaviour, which is the tendency for an investor to abandon her own information to imitate the behaviour of other investors. Therefore, the rational investor is conditioning her choice towards behavioural investors to give rise to the positive feedback effect. This effect has been documented by Scharfstein and Stein (1990) on fund manager, Grinblatt et al. (1995) in mutual fund behaviour and by Devenow and Welch (1996) on forecasts made by financial analysts. The rational learner agent adopts a positive feedback strategy through herding behaviour to improve her investment. In this regard, the two components are blended in a Bayesian manner. The model is built analogously to Black-Litterman model to obtain the aggregated measure adjusted by a weighting factor. The goal in the application of the model is to check if the positive feedback effect exists. The work shows that conditioning the choice of the HARA investor towards a behavioural direction improves the selection amongst the assets. The empirical analysis is performed on all the assets present in the NASDAQ stock exchange from December 1989 to February 2012. This chapter is a solo paper. The second chapter declines with a different purpose the model developed in the first chapter. In this context, two categories of agents are considered, one rational with a risk adverse utility function and one with an S-shaped loss averse value function similar to Kahneman and Tversky (1979). Agents take investment decisions in the same way by ranking the alternative assets according to their performance measures. We assume that a type of agent is endowed with an S-shaped loss averse value function. This produces the intuitive and empirically validated prediction that the attitude of undertaking risky investments changes according to the fluctuations of the financial market. According to this assumption, in periods of (financial and economic) recession, financial agents are attracted by more risky investments that might generate, with some positive probability, returns that compensate previous (observed) losses. On the other hand, in periods of expansion, financial agents are more reluctant to undertake a risky investment that might reduce, with some positive probability, previous (observed) capital gains. In this chapter the model estimates the relative weight of the behavioural component in the financial market. The empirical analysis is based on monthly data on the components of the S&P 500 index from January 1962 to April 2012. The relative weight of the behavioural category over the rational's one has an intuitive explanation: the higher the value of the weighting factor, the higher is the weight of the behavioural component in the aggregated measure. The estimated value of the weighting factor is obtained by maximizing the cumulated return of the one hundred most performing assets of the mixture ranking. Intuitively, the weighting factor captures the extent to which the financial market should have moved from the ordering of the rational category to the ranking of the behavioural agents to maximize the return of the "best" one hundred assets. By choosing a selection of one hundred assets, we capture the systemic dimension of the financial market. The results confirm the existence of a significant behavioural component, which is more likely to emerge during recessions. A strong correlation emerges between the estimated relative weight series and the VIX index, which implies that the estimates substantially explain financial expectations. This is a joint paper with Professors Massimiliano Caporin and Luca Corazzini (University of Padova). The third chapter introduces a novel criterion for performance measure combination designed to be used as an equity screening algorithm. The combination criterion follows the general idea of linearly combining existing performance measures with positive weights. These weights are determined by means of an optimisation problem. The underlying criterion function explicitly takes into account the risk-return trade-off potentially associated with the equity screens, evaluated on a historical and rolling basis. By construction, and due to the rolling window evaluation approach, the methodology provides performance combination weights that can vary over time, thus allowing for changes in preferences across performance measures. The proposed approach is implicitly robust to the dynamic features of the returns densities, as these will affect the evaluation of performance measures that are the inputs of our screening algorithm. The final product of the linear combination of performance measure is a composite performance index, which can then be used to create asset screens. We present an empirical application that illustrates the use of the screening algorithm in a simplified portfolio allocation. This is a joint paper with Professors Monica Billio (Ca' Foscari University of Venice) and Massimiliano Caporin. The fourth chapter examines the financial contagion using a regime switching approach with vine copulas. Vine Copulas allows us to model easily a multivariate framework with the use of the pair-copula decomposition introduced by Aas et al. 2009). The marginals are modelled by the GARCH process with long memory volatility{in mean as introduced by Christensen et al. (2010). In particular, this model well captures the long{range dependence characterizing financial time series, allowing for asymmetric effects in the GARCH equation and for the news impact in the mean. Moreover, we decided to use Copula functions to model the dependence structure across variables. The final purpose is to use a long memory GARCH process to filter the marginal series and then to use a regime switching approach among different copula families to model the dependence structure. Diebold and Inoue (2001) highlight that these two approaches can lead to misleading results. In fact, long memory can easily be confused with structural changes and viceversa. In our case, we are looking at long memory and regime switching in a complementary way, since we use them on different dimensions. Vine Copula families are considered to build the multivariate dependence structure with Pair-Copula construction methodology (Aas et al., 2009). In the empirical analysis, we focus on the main European countries (Germany, France, Italy, Spain and Netherlands) to detect contagion (and financial integration). In the thesis, a preliminary version of the paper is included in which we filtered the series using the exponential GARCH process. This is a joint paper with Professor Bent Jesper Christensen (CREATES - Aarhus University)

Uncertainties due to the use of surface averaged wind pressure coefficients

A common practice, adopted by several building energy simulation (BES) tools, is the use of surface averaged wind pressure coefficients (Cp) instead of local Cp values with high resolution in space. The aim of this paper is to assess the uncertainty related to the use of surface averaged data, for the case of a cubic building with two openings. The focus is on wind-driven ventilation and infiltration, while buoyancy is not taken into account. The study is performed using published empirical data on pressure coefficients obtained from wind tunnel tests. The method developed to calculate the uncertainty is based on comparison of: the flow rate calculated using the averaged values (fAV), and the one calculated using local values (fLOC). The study considers a large number of combinations for the opening positions in the facade. For each pair of openings (i), the values of fLOC_i and fAV_i are calculated. Based on the ratio between fLOC_i and fAV_i the relative error (ri) is calculated. The relative error is presented statistically, providing probability density graphs and upper and lower bounds for the confidence interval (CI) of 95%. For this CI, the conclusion is that 0.24 fAV <fLOC <4.87 fAV

External coupling between BES and HAM programs for whole building simulation

This paper discusses a procedure for the two-way runtime external coupling between Building EnergySimulation (BES) and building envelope Heat, Air and Moisture (HAM) programs for enhanced wholebuilding simulation. The coupling procedure presented here involves a description of the relevant physical phenomena at the interface between the programs, domain overlaps, coupling variables, coupling strategy and types of boundary condition. The procedure is applied using the programs ESP-r and HAMFEM, where the implementation and verification issues are discussed. This work concludes that the coupling between BES and HAM programs is feasible, and it can potentially enhance the accuracy in whole-building simulation

Capability and deficiency of the simplified model for energy calculation of commercial buildings in the Brazilian regulation

This paper provides a preliminary assessment on the accuracy of the Brazilian regulation simplified model for commercial buildings. The first step was to compare its results with BESTEST. The study presents a straightforward approach to apply the BESTEST in other climates than the original one (Denver, Colorado, USA). The second step consisted on applying the simplified model for common buildings, and compare the results with those obtained using a state of the art building energy simulation (BES) program. Significant errors were found when comparing the simplified model with BESTEST and the common buildings analyzed

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

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