Value at risk models in finance

The main objective of this paper is to survey and evaluate the performance of the most popular univariate VaR methodologies, paying particular attention to their underlying assumptions and to their logical flaws. In the process, we show that the Historical Simulation method and its variants can be considered as special cases of the CAViaR framework developed by Engle and Manganelli (1999). We also provide two original methodological contributions. The first one introduces the extreme value theory into the CAViaR model. The second one concerns the estimation of the expected shortfall (the expected loss, given that the return exceeded the VaR) using a regression technique. The performance of the models surveyed in the paper is evaluated using a Monte Carlo simulation. We generate data using GARCH processes with different distributions and compare the estimated quantiles to the true ones. The results show that CAViaR models perform best with heavy-tailed DGP. JEL Classification: C22, G22CAViaR, extreme value theory, Value at Risk

CAViaR: Conditional Value at Risk by Quantile Regression

Value at Risk has become the standard measure of market risk employed by financial institutions for both internal and regulatory purposes. Despite its conceptual simplicity, its measurement is a very challenging statistical problem and none of the methodologies developed so far give satisfactory solutions. Interpreting Value at Risk as a quantile of future portfolio values conditional on current information, we propose a new approach to quantile estimation which does not require any of the extreme assumptions invoked by existing methodologies (such as normality or i.i.d. returns). The Conditional Value at Risk or CAViaR model moves the focus of attention from the distribution of returns directly to the behavior of the quantile. We postulate a variety of dynamic processes for updating the quantile and use regression quantile estimation to determine the parameters of the updating process. Tests of model adequacy utilize the criterion that each period the probability of exceeding the VaR must be independent of all the past information. We use a differential evolutionary genetic algorithm to optimize an objective function which is non-differentiable and hence cannot be optimized using traditional algorithms. Applications to simulated and real data provide empirical support to our methodology and illustrate the ability of these algorithms to adapt to new risk environments.

Contextualized property market models vs. Generalized mass appraisals: An innovative approach

The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016-2017. The ability to generate a "unique" functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies

EV charging stations and RES-based DG: A centralized approach for smart integration in active distribution grids

Renewable Energy Sources based (RES-based) Dispersed Generation (DG) and Electrical Vehicles (EVs) charging systems diffusion is in progress in many Countries around the word. They have huge effects on the distribution grids planning and operation, particularly on MV and LV distribution grids. Many studies on their impact on the power systems are ongoing, proposing different approaches of managing. The present work deals with a real application case of integration of EVs charging stations with ES-based DG. The final task of the integration is to be able to assure the maximum utilization of the distribution grid to which both are connected, without any upgrading action, and in accordance with Distribution System Operators (DSOs) needs. The application of the proposed approach is related to an existent distribution system, owned by edistribuzione, the leading DSO in Italy. Diverse types of EVs supplying stations, with diverse diffusion scenarios, have been assumed for the case study; various Optimal Power Flow (OPF) models, based on diverse objective functions, reflecting DSO necessities, have been applied and tried. The obtained results demonstrate that a centralized management approach by the DSO, could assure the respect of operation limits of the system in the actual asset, delaying or avoiding upgrading engagements and charges

Real Options for risk analysis in estimating the capitalization rate

A suitable cap-rate is generally determined through an analogical process in order to estimate the value of any real estate through the capitalization of the incomes. The analogy relates to the risk and duration of similar investments. There are numerous methods to rationalize the valuation of the cap-rate. Appraisals have a certain degree of uncertainty in all these methods. This paper proposes a methodology which removes any uncertainty when evaluating the cap-rate. This is achieved through the combination of the formal logic of the Ellwood’s model and the Real Options Analysis

An application of Real Option Analysis for the assessment of operative flexibility in the urban redevelopment

The high variability of market prices and the uncertainty that, even in restrained timeframes, is characterizing the general economic situation, have led real estate operators to a prudent attitude, who tend to postpone or at least stagger the start of the initiatives on hold of more stable conditions. In this context it is appropriate to use evaluation tools enable to enhance the investment capacity to be adapted to possible changes of the conditions initially hypothesized. In the present research Real Options Analysis (ROA) is applied to the evaluation of an investment in urban redevelopment of a former industrial complex. The result obtained shows the efficacy of the instrument. Assuming that the entrepreneur considers affordable the implementation of the initiative if the outcome of the discounted cash flow analysis is at least equal to a threshold value calculated as a percentage of revenues, the application of ROA returns an extended NPV that meets this constraint, whereas the use of traditional NPV suggest to abandon the project idea. The binomial approach used also allows to accurately monitor the project's development, correlating it to the evolution of the market

Studenti svantaggiati e fattori di promozione della resilienza

Molti studi evidenziano l’impatto che il contesto socio-economico e diverse caratteristiche degli studenti, quali il genere e il background migratorio, hanno sul raggiungimento di adeguate competenze in matematica. Questa situazione pone un problema di equità del sistema educativo e formativo: alcuni gruppi di giovani sono infatti svantaggiati in partenza per motivi indipendenti dal loro impegno nello studio. L’obiettivo del presente lavoro è valutare la presenza di fattori, su cui è possibile un intervento da parte degli insegnanti, che consentano a studenti svantaggiati di raggiungere risultati di eccellenza. Il contributo mira a identificare fattori associati non solo a una compensazione dello svantaggio legato alle condizioni di sfondo degli studenti, ma a una vera e propria inversione delle previsioni in termini di competenze raggiunte. I risultati mostrano l’impatto che l’appartenenza geografica, il background socio-economico-culturale delle scuole e delle famiglie e il genere, nella loro interazione, esercitano nel definire situazioni di forte svantaggio di partenza per gli studenti. Allo stesso tempo, gli esiti mettono in luce il ruolo protettivo giocato da alcune strategie d’insegnamento, dalle convinzioni di autoefficacia degli studenti e da altri fattori legati a specifiche situazioni di contesto.Many studies showed the impact that the socio-economic context and students’ characteristics, such as gender and migratory background, have on mathematical performance. This situation poses a problem of equity of the educational system: some groups of young people are in fact disadvantaged for reasons independent of their commitment to the study. The aim of this paper is to evaluate the presence of factors on which teachers can intervene, allowing disadvantaged students to achieve excellence. The contribution aims to identify associated factors not only to compensate for the disadvantage associated with student background conditions, but to a real reversal of predictions in terms of skills acquired. The results show the impact that geographic membership, the socio-economic-cultural background of schools and families and gender, in their interaction, exert in defining situations of major disadvantage for students. At the same time, there is evidence for the protective role played by some teaching strategies, student self-beliefs, and other factors related to specific background situations

Real estate appraisals with Bayesian approach and Markov Chain Hybrid Monte Carlo Method: An application to a central urban area of Naples

This paper experiments an artificial neural networks model with Bayesian approach on a small real estate sample. The output distribution has been calculated operating a numerical integration on the weights space with the Markov Chain Hybrid Monte Carlo Method (MCHMCM). On the same real estate sample, MCHMCM has been compared with a neural networks model (NNs), traditional multiple regression analysis (MRA) and the Penalized Spline Semiparametric Method (PSSM). All four methods have been developed for testing the forecasting capacity and reliability of MCHMCM in the real estate field. The Markov Chain Hybrid Monte Carlo Method has proved to be the best model with an absolute average percentage error of 6.61%

Pharmacological treatment for familial amyloid neuropathy

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To assess and compare the efficacy, acceptability, and tolerability of pharmacologic disease‐modifying agents for familial amyloid neuropathy (FAP)

Realized extreme quantile: A joint model for conditional quantiles and measures of volatility with EVT refinements

We propose a new framework exploiting realized measures of volatility to estimate and forecast extreme quantiles. Our realized extreme quantile (REQ) combines quantile regression with extreme value theory and uses a measurement equation that relates the realized measure to the latent conditional quantile. Model estimation is performed by quasi maximum likelihood, and a simulation experiment validates this estimator in finite samples. An extensive empirical analysis shows that high-frequency measures are particularly informative of the dynamic quantiles. Finally, an out-of-sample forecast analysis of quantile-based risk measures confirms the merit of the REQ