Precificação de opções sobre ações por modelos de Support Vector Regression

Resumo: Derivativos são títulos financeiros cujo valor de mercado deriva do preço de outro ativo. Dentre as modalidades de derivativos existentes, destacam-se as opções. No mercado de opções, negociam-se contratos que concedem ao seu titular o direito futuro de comprar ou vender um ativo objeto por um preço determinado no presente, chamado de preço de exercício. As opções são comercializadas no mercado mediante o pagamento de um prêmio, correspondente ao valor monetário do contrato. O valor desse prêmio sofre influência de diversos fatores e oscila de acordo com a expectativa do mercado. A determinação de preços teóricos de opções mediante modelos matemáticos permite ao investidor verificar se os preços estabelecidos pelo mercado estão superestimados ou subestimados. Essas informações influenciam diretamente nas operações realizadas pelo investidor. Desta forma, é preciso que o modelo empregado apresente alto grau de confiabilidade e seja condizente com a realidade do mercado ao qual ele se destina. Neste sentido, essa dissertação tem como objetivo estabelecer um modelo de precificação de opções baseado na técnica de Support Vector Regression (SVR), que capte a realidade do mercado brasileiro. O SVR baseia-se no aprendizado supervisionado estatístico e determina uma função de precificação a partir do reconhecimento de padrões e tendências do mercado. Para realizar a pesquisa, foram utilizados dados referentes às opções de compra americanas sobre ações da Petrobras PN negociadas na Bolsa de Valores de São Paulo, no período de novembro de 2008 a maio de 2009. Com a finalidade de validar o modelo proposto, compararam-se os resultados encontrados pelo SVR com os valores calculados pelo modelo de Black & Scholes, o qual se caracteriza por ser um dos modelos mais utilizados na área de finanças. A partir das comparações realizadas, concluiu-se que o desempenho do modelo de SVR foi superior ao de B&S na precificação de opções classificadas dentro do dinheiro, no dinheiro e fora do dinheiro. Verificou-se também que o modelo de SVR é capaz de captar os movimentos de preços do mercado

Método Grid-Quadtree para seleção de parâmetros do algoritmo support vector classification (SVC)

Orientador : Prof. Dr. Arinei Carlos Lindbeck da SilvaTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 01/06/2016Inclui referências : f. 143-149Área de concentração : Programação matemáticaResumo: O algoritmo Support Vector Classification (SVC) é uma técnica de reconhecimento de padrões, cuja eficiência depende da seleção de seus parâmetros: constante de regularização C, função kernel e seus respectivos parâmetros. A escolha equivocada dessas variáveis impacta diretamente na performance do algoritmo, acarretando em fenômenos indesejáveis como o overfitting e o underfitting. O problema que estuda a procura de parâmetros ótimos para o SVC, em relação às suas medidas de desempenho, é denominado seleção de modelos do SVC. Em virtude do amplo domínio de convergência do kernel gaussiano, a maioria dos métodos destinados a solucionar esse problema concentra-se na seleção da constante C e do parâmetro ? do kernel gaussiano. Dentre esses métodos, a busca por grid é um dos de maior destaque devido à sua simplicidade e bons resultados. Contudo, por avaliar todas as combinações de parâmetros (C, ?) dentre o seu espaço de busca, a mesma necessita de muito tempo de processamento, tornando-se impraticável para avaliação de grandes conjuntos de dados. Desta forma, o objetivo deste trabalho é propor um método de seleção de parâmetros do SVC, usando o kernel gaussiano, que combine a técnica quadtree à busca por grid, para reduzir o número de operações efetuadas pelo grid e diminuir o seu custo computacional. A ideia fundamental é empregar a quadtree para desenhar a boa região de parâmetros, evitando avaliações desnecessárias de parâmetros situados nas áreas de underfitting e overfitting. Para isso, desenvolveu-se o método grid-quadtree (GQ), utilizando-se a linguagem de programação VB.net em conjunto com os softwares da biblioteca LIBSVM. Na execução do GQ, realizou-se o balanceamento da quadtree e criou-se um procedimento denominado refinamento, que permitiu delinear a curva de erro de generalização de parâmetros. Para validar o método proposto, empregaram-se vinte bases de dados referência na área de classificação, as quais foram separadas em dois grupos. Os resultados obtidos pelo GQ foram comparados com os da tradicional busca por grid (BG) levando-se em conta o número de operações executadas por ambos os métodos, a taxa de validação cruzada (VC) e o número de vetores suporte (VS) associados aos parâmetros encontrados e a acurácia do SVC na predição dos conjuntos de teste. A partir das análises realizadas, constatou-se que o GQ foi capaz de encontrar parâmetros de excelente qualidade, com altas taxas VC e baixas quantidades de VS, reduzindo em média, pelo menos, 78,8124% das operações da BG para o grupo 1 de dados e de 71,7172% a 88,7052% para o grupo 2. Essa diminuição na quantidade de cálculos efetuados pelo quadtree resultou em uma economia de horas de processamento. Além disso, em 11 das 20 bases estudadas a acurácia do SVC-GQ foi superior à do SVC-BG e para quatro delas igual. Isso mostra que o GQ é capaz de encontrar parâmetros melhores ou tão bons quanto os da BG executando muito menos operações. Palavras-chave: Seleção de modelos do SVC. Kernel gaussiano. Quadtree. Redução de operações.Abstract: The Support Vector Classification (SVC) algorithm is a pattern recognition technique, whose efficiency depends on its parameters selection: the penalty constant C, the kernel function and its own parameters. A wrong choice of these variables values directly impacts on the algorithm performance, leading to undesirable phenomena such as the overfitting and the underfitting. The task of searching for optimal parameters with respect to performance measures is called SVC model selection problem. Due to the Gaussian kernel wide convergence domain, many model selection approaches focus in determine the constant C and the Gaussian kernel ? parameter. Among these, the grid search is one of the highlights due to its easiest way and high performance. However, since it evaluates all parameters combinations (C, ?) on the search space, it requires high computational time and becomes impractical for large data sets evaluation. Thus, the aim of this thesis is to propose a SVC model selection method, using the Gaussian kernel, which integrates the quadtree technique with the grid search to reduce the number of operations performed by the grid and its computational cost. The main idea of this study is to use the quadtree to determine the good parameters region, neglecting the evaluation of unnecessary parameters located in the underfitting and the overfitting areas. In this regard, it was developed the grid-quadtree (GQ) method, which was implemented on VB.net development environment and that also uses the software of the LIBSVM library. In the GQ execution, it was considered the balanced quadtree and it was created a refinement procedure, that allowed to delineate the parameters generalization error curve. In order to validate the proposed method, twenty benchmark classification data set were used, which were separated into two groups. The results obtained via GQ were compared with the traditional grid search (GS) ones, considering the number of operations performed by both methods, the cross-validation rate (CV) and the number of support vectors (SV) associated to the selected parameters, and the SVC accuracy in the test set. Based on this analyzes, it was concluded that GQ was able to find excellent parameters, with high CV rates and few SV, achieving an average reduction of at least 78,8124% on GS operations for group 1 data and from 71,7172% to 88,7052% for group 2. The decrease in the amount of calculations performed by the quadtree lead to savings on the computational time. Furthermore, the SVC-GQ accuracy was superior than SVC-GS in 11 of the 20 studied bases and equal in four of them. These results demonstrate that GQ is able to find better or as good as parameters than BG, but executing much less operations. Key words: SVC Model Selection. Gaussian kernel. Quadtree. Reduction Operation

The Zonal and Seasonal CO2 Marginal Emissions Factors for the Italian Power Market

This paper estimates the seasonal and zonal CO2 marginal emissions factors (MEFs) from electricity production in the Italian electricity system. The inclusion of the zonal configura- tion of the Italian wholesale power market leads to a complete measurement of marginal emission factors which takes into account the heterogeneous distribution of RES power plants, their penetration rate and their variability within the zonal power generation mix. This article relies on a flexible econometric approach that includes the fractional cointe- gration methodology to incorporate the typical features of long-memory processes into the estimation of MEFs. We find high variability in annual MEFs estimated at the zonal level. Sardinia reports the highest MEF (0.7189 tCO2/MWh), followed by the Center South (0.7022 tCO2/MWh), the Center North (0.4236 tCO2/MWh), the North (0.2018 tCO2/ MWh) and Sicily (0.146 tCO2/MWh). The seasonal analysis also shows a large variability of MEFs in each zone across time. The heterogeneity of results leads us to recommend that policymakers consider the zonal configuration of the power market and the large seasonal variability related to carbon emissions and electricity generation when designing incentives for renewable energy sources expansion and for achieving emission reduction targets

Assessment of Lake Orta sediments phytotoxicity after the liming treatment

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Establishment of Corbicula fluminea (O.F. M?ller, 1774) in Lake Maggiore: a spatial approach to trace the invasion dynamics

We analysed the dynamics of the invasive Asian basket clam Corbicula fluminea in Lake Maggiore (Italy), recorded for the first time in August 2010. In order to reveal the extent of its dispersal, we monitored 30 locations along the lake for presence/absence of clams. This assessment of population structure, density and biomass is based on quantitative samples collected along the southern shoreline at four sites with diverse types of habitats. In the present study, the on-going process of Corbicula invasion was analysed from a spatial and temporal perspective. We compared density and size structure of the population among the sites (spatial distribution). We attempted to trace the colonization dynamics of the clams, so the invasion dynamics were tentatively reconstructed from spatial distribution of size /age groups and the contribution of the last recruited cohort to total population density along the lake littoral zone. Results from our surveys conducted in 2010-2011 have demonstrated that the Asian clam was well-established in the lake, thus about one-third of the lake (i.e. the southern basin) was already colonized by C. fluminea in 2011. Size frequency distribution in autumn 2011 further illustrated reproduction events and new recruitments. Population densities in Lake Maggiore were among the highest ever recorded in an Italian lake. Both the rapid spread of Corbicula in the littoral area and the relatively high densities, even at the most recently invaded sites, infer the potential ecosystem impacts associated with a dominant invasive species. Data reported here are not intended to be exhaustive since they concern only two years of investigations, so more detailed studies on both the ecology and invasive habits of this new alien species in Lake Maggiore are planned. The spatial approach used in the present study may clarify the dynamics of this invasion. Future monitoring might help us to disentangle the effects of spatial variability versus temporal succession during the establishment of the invasive species

The SARS-CoV-2 Spike protein disrupts human cardiac pericytes function through CD147-receptor-mediated signalling:a potential non-infective mechanism of COVID-19 microvascular disease

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a broad range of clinical responses including prominent microvascular damage. The capacity of SARS-CoV-2 to infect vascular cells is still debated. Additionally, the SARS-CoV-2 Spike (S) protein may act as a ligand to induce non-infective cellular stress. We tested this hypothesis in pericytes (PCs), which are reportedly reduced in the heart of patients with severe coronavirus disease-2019 (COVID-19). Here we newly show that the in vitro exposure of primary human cardiac PCs to the SARS-CoV-2 wildtype strain or the α and δ variants caused rare infection events. Exposure to the recombinant S protein alone elicited signalling and functional alterations, including: (1) increased migration, (2) reduced ability to support endothelial cell (EC) network formation on Matrigel, (3) secretion of pro-inflammatory molecules typically involved in the cytokine storm, and (4) production of pro-apoptotic factors causing EC death. Next, adopting a blocking strategy against the S protein receptors angiotensin-converting enzyme 2 (ACE2) and CD147, we discovered that the S protein stimulates the phosphorylation/activation of the extracellular signal-regulated kinase 1/2 (ERK1/2) through the CD147 receptor, but not ACE2, in PCs. The neutralisation of CD147, either using a blocking antibody or mRNA silencing, reduced ERK1/2 activation, and rescued PC function in the presence of the S protein. Immunoreactive S protein was detected in the peripheral blood of infected patients. In conclusion, our findings suggest that the S protein may prompt PC dysfunction, potentially contributing to microvascular injury. This mechanism may have clinical and therapeutic implications

Managing chronic myeloid leukemia for treatment-free remission: a proposal from the GIMEMA CML WP

Several papers authored by international experts have proposed recommendations on the management of BCR-ABL1+ chronic myeloid leukemia (CML). Following these recommendations, survival of CML patients has become very close to normal. The next, ambitious, step is to bring as many patients as possible into a condition of treatment-free remission (TFR). The Gruppo Italiano Malattie EMatologiche dell'Adulto (GIMEMA; Italian Group for Hematologic Diseases of the Adult) CML Working Party (WP) has developed a project aimed at selecting the treatment policies that may increase the probability of TFR, taking into account 4 variables: the need for TFR, the tyrosine kinase inhibitors (TKIs), the characteristics of leukemia, and the patient. A Delphi-like method was used to reach a consensus among the representatives of 50 centers of the CML WP. A consensus was reached on the assessment of disease risk (EUTOS Long Term Survival [ELTS] score), on the definition of the most appropriate age boundaries for the choice of first-line treatment, on the choice of the TKI for first-line treatment, and on the definition of the responses that do not require a change of the TKI (BCR-ABL1 6410% at 3 months, 641% at 6 months, 640.1% at 12 months, 640.01% at 24 months), and of the responses that require a change of the TKI, when the goal is TFR (BCR-ABL1 >10% at 3 and 6 months, >1% at 12 months, and >0.1% at 24 months). These suggestions may help optimize the treatment strategy for TFR

The longevity-associated BPIFB4 gene supports cardiac function and vascularization in ageing cardiomyopathy

Aims The ageing heart naturally incurs a progressive decline in function and perfusion that available treatments cannot halt. However, some exceptional individuals maintain good health until the very late stage of their life due to favourable gene–environment interaction. We have previously shown that carriers of a longevity-associated variant (LAV) of the BPIFB4 gene enjoy prolonged health spans and lesser cardiovascular complications. Moreover, supplementation of LAV-BPIFB4 via an adeno-associated viral vector improves cardiovascular performance in limb ischaemia, atherosclerosis, and diabetes models. Here, we asked whether the LAV-BPIFB4 gene could address the unmet therapeutic need to delay the heart’s spontaneous ageing. Methods and results Immunohistological studies showed a remarkable reduction in vessel coverage by pericytes in failing hearts explanted from elderly patients. This defect was attenuated in patients carrying the homozygous LAV-BPIFB4 genotype. Moreover, pericytes isolated from older hearts showed low levels of BPIFB4, depressed pro-angiogenic activity, and loss of ribosome biogenesis. LAV-BPIFB4 supplementation restored pericyte function and pericyte-endothelial cell interactions through a mechanism involving the nucleolar protein nucleolin. Conversely, BPIFB4 silencing in normal pericytes mimed the heart failure pericytes. Finally, gene therapy with LAV-BPIFB4 prevented cardiac deterioration in middle-aged mice and rescued cardiac function and myocardial perfusion in older mice by improving microvasculature density and pericyte coverage. Conclusions We report the success of the LAV-BPIFB4 gene/protein in improving homeostatic processes in the heart’s ageing. These findings open to using LAV-BPIFB4 to reverse the decline of heart performance in older people