Finanza Imprenditoriale: Teoria del Portafoglio e Costo del Capitale

This thesis focuses on the limits that may prevent an entrepreneur from maximizing her value, and the benefits of diversification in reducing her cost of capital. After reviewing all relevant literature dealing with the differences between traditional corporate finance and entrepreneurial finance, we focus on the biases occurring when traditional finance techniques are applied to the entrepreneurial context. In particular, using the portfolio theory framework, we determine the degree of under-diversification of entrepreneurs. Borrowing the methodology developed by Kerins et al. (2004), we test a model for the cost of capital according to the firms' industry and the entrepreneur's wealth commitment to the firm. This model takes three market inputs (standard deviation of market returns, expected return of the market, and risk-free rate), and two firm-specific inputs (standard deviation of the firm returns and correlation between firm and market returns) as parameters, and returns an appropriate cost of capital as an output. We determine the expected market return and the risk-free rate according to the huge literature on the market risk premium. As for the market return volatility, it is estimated considering a GARCH specification for the market index returns. Furthermore, we assume that the firm-specific inputs can be obtained considering new-listed firms similar in risk to the firm we are evaluating. After we form a database including all the data needed for our analysis, we perform an empirical investigation to understand how much of the firm's total risk depends on market risk, and which explanatory variables can explain it. Our results show that cost of capital declines as the level of entrepreneur's commitment decreases. Therefore, maximizing the value for the entrepreneur depends on the fraction of entrepreneur's wealth invested in the firm and the fraction she sells to outside investors. These results are interesting both for entrepreneurs and policy makers: the former can benefit from an unbiased model for their valuation; the latter can obtain some guidelines to overcome the recent financial market crisis.

Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.

Modellazione Tridimensionale del Processo di Combustione di un Razzo a Propellente Solido

La regolazione dei sistemi di propulsione a razzo a propellente solido (Solid Rocket Motors) ha da sempre rappresentato una delle principali problematiche legate a questa tipologia di motori. L’assenza di un qualsiasi genere di controllo diretto del processo di combustione del grano solido, fa si che la previsione della balistica interna rappresenti da sempre il principale strumento utilizzato sia per definire in fase di progetto la configurazione ottimale del motore, sia per analizzare le eventuali anomalie riscontrate in ambito sperimentale. Variazioni locali nella struttura del propellente, difettosità interne o eterogeneità nelle condizioni di camera posso dare origine ad alterazioni del rateo locale di combustione del propellente e conseguentemente a profili di pressione e di spinta sperimentali differenti da quelli previsti per via teorica. Molti dei codici attualmente in uso offrono un approccio piuttosto semplificato al problema, facendo per lo più ricorso a fattori correttivi (fattori HUMP) semi-empirici, senza tuttavia andare a ricostruire in maniera più realistica le eterogeneità di prestazione del propellente. Questo lavoro di tesi vuole dunque proporre un nuovo approccio alla previsione numerica delle prestazioni dei sistemi a propellente solido, attraverso la realizzazione di un nuovo codice di simulazione, denominato ROBOOST (ROcket BOOst Simulation Tool). Richiamando concetti e techiche proprie della Computer Grafica, questo nuovo codice è in grado di ricostruire in processo di regressione superficiale del grano in maniera puntuale, attraverso l’utilizzo di una mesh triangolare mobile. Variazioni locali del rateo di combustione posso quindi essere facilmente riprodotte ed il calcolo della balistica interna avviene mediante l’accoppiamento di un modello 0D non-stazionario e di uno 1D quasi-stazionario. L’attività è stata svolta in collaborazione con l’azienda Avio Space Division e il nuovo codice è stato implementato con successo sul motore Zefiro 9.

Bank Governance and the Bail-in in the EU. A Law & Finance analysis on the role of bail-inable creditors

This dissertation assesses the impact of the EU Directive on Bank Recovery and Resolution (BRRD) on bank corporate governance and investigates a fundamental question. Can the resolution framework for distressed banks enhance the quality of banks’ decision making? According to the Directive, the Resolution Authority can impose losses on bank’s creditors in case of distress through a bail-in. Bail-inable creditors become residual claimants of the bank, contingent on its distress. The first part of the dissertation establishes an analytical framework for bank governance, starting from the problem of what can be defined as “good governance” in banking. The dissertation hypothesizes that governance regulation represents a necessary link between the incentives of corporate constituencies and the goals of substantive regulation. The second part builds upon this analytical framework and carries out a positive analysis encompassing three channels of debt governance; namely, price internalisation of risk; contractual arrangements and the discrete impact of different type of creditors. The existence of a resolution framework should incentivise bail-inable creditors to better discipline the borrowing bank; yet, the design of both the capital and resolution regulation largely foreclose such possibility to creditors. Against this backdrop, the third part of the dissertation moves to normative considerations. The approach to this normative part combines and complements the study of cash flow rights of the management with the study of the voting rights to bail-inable creditors. On the cash flow side, the dissertation proposes to include bail-inable debt as part of the variable remuneration for bank risk-takers. On the voting right, the proposal is to grant a limited basket of ex-ante governance rights to bail-inable creditors. Such a unified approach is rather uncommon in the literature, where cash flow rights and voting rights are often approached separately whereas those complement each other in the dissertation.