54 research outputs found

    Neural networks and betting strategies for tennis

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    Recently, the interest of the academic literature on sports statistics has increased enormously. In such a framework, two of the most significant challenges are developing a model able to beat the existing approaches and, within a betting market framework, guarantee superior returns than the set of competing specifications considered. This contribution attempts to achieve both these results, in the context of male tennis. In tennis, several approaches to predict the winner are available, among which the regression-based, point-based and paired comparison of the competitors’ abilities play a significant role. Contrary to the existing approaches, this contribution employs artificial neural networks (ANNs) to forecast the probability of winning in tennis matches, starting from all the variables used in a large selection of the previous methods. From an out-of-sample perspective, the implemented ANN model outperforms four out of five competing models, independently of the considered period. For what concerns the betting perspective, we propose four different strategies. The resulting returns on investment obtained from the ANN appear to be more broad and robust than those obtained from the best competing model, irrespective of the betting strategy adopted

    A comparison of the forecasting performances of multivariate volatility models

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    The consistent ranking of multivariate volatility models by means of statistical loss function is a challenging research field, because it concerns the quality of the proxy chosen to replace the unobserved volatility, the set of competing models to be ranked and the kind of loss function. The existent works only consider the ranking of multivariate GARCH (MGARCH) models, based on daily frequency of the returns. Less is known about the behaviour of the models that directly use the realized covariance (RCOV), the proxy that generally provides a consistent estimate of the unobserved volatility. The aim of this paper is to evaluate which model has the best forecast volatility accuracy, from a statistical and economic point of view. For the first point, we empirically rank a set of MGARCH and RCOV models by means of four consistent statistical loss functions. For the second point, we evaluate if these rankings are coherent with those resulting from the use of an economic loss function. The evaluation of the volatility models through the economic loss function is usually done by looking at the Value at Risk (VaR) measures and its violations. A violation occurs every time the portfolio losses exceed the VaR. To assess the performances of the volatility models from an economic point of view, different tests regarding the violations have been proposed. In this work, the unconditional and conditional tests are considered. The analysis is based on a Monte Carlo experiment that samples from a trivariate continuous-time stochastic process a vector of observation each five minutes per two years

    Corporate Governance, Investment, Profitability and Insolvency Risk: Evidence from Italy

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    The research aims to study the structural and functional characteristics of food and beverage companies, focusing on corporate governance, investment and financing decisions, innovation, profitability, and risk of insolvency. The analysis is based on a mixed type investigation method carried out on a random stratified sample of 274 firms. The empirical findings reveal that a large prevalence of companies is owned by a single person or by a limited number of partners (often of the same family). Owners and their families centralize decision-making power. The prevalence of companies made investment in innovation. The investments are mainly financed (78%) by the self-financing or by shareholders' capital. The investigation of the causal relationships that link corporate risk, profitability, and the propensity to invest and innovate with the other explanatory variables of business management highlighted further significant aspects

    Mixed-frequency quantile regression with realized volatility to forecast Value-at-Risk

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    The use of quantile regression to calculate risk measures has been widely recognized in the financial econometrics literature. When data are observed at mixed-frequency, the standard quantile regression models are no longer adequate. In this paper, we develop a model built on a mixed-frequency quantile regression to directly estimate the Value-at-Risk. In particular, the low-frequency component incorporates information coming from variables observed at, typically, monthly or lower frequencies, while the high-frequency component can include a variety of daily variables, like realized volatility measures or market indices. We derive the conditions for the weak stationarity of the daily return process suggested while the finite sample properties are investigated in an extensive Monte Carlo exercise. The validity of the proposed model is then explored through a real data application using the most important financial indexes. We show that our model outperforms other competing specifications, using backtesting and Model Confidence Set procedures

    Governance, Innovation, Profitability, and Credit Risk: Evidence from Italian manufacturing firms

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    The research focuses attention on companies in the chemical-plastic sector, investigating corporate governance, investment and financing decisions, innovation, profitability, and credit risk. The investigation method used is mixed. The data and information were extracted from official databases and from a structured questionnaire. The stratified random method was used for sampling. 178 firms are included in the sample. The results show a prevalence of companies owned by a single person or a limited number of shareholders (in some cases of the same family), where the owners centralize decision-making power. Companies have a strong propensity to invest in innovation. Investments are mainly financed by self-financing or equity capital. The analysis of the causal relationships highlights further significant aspects relating to credit risk, profitability and innovation

    Evaluation of volatility forecasts

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    2012 - 2013The modelization of risk is a hard task for many financial institutions. This explains the great interest for the volatility models during last decades. In this framework, the volatility predictions deriving from a set of models is a partly unexplored research field. A general formulation of this problem involves the volatitlity proxy, the forecasting models, the forecasting scheme used to generate the predictions and the function employed to evalue the forecasts. In this thesis, the volatility proxy is the realized volatitlity while the forecasting models are the (univariate and multivariate) Garch models and the models that models that re-paranetrize the realized volatility... [edited by author]XII n.s

    On the Volatility Spillover between Agricultural Commodities and Latin American Stock Markets

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    Addressing the volatility spillovers of agricultural commodities is important for at least two reasons. First, for the last several years, the volatility of agricultural commodity prices seems to have increased. Second, according to the Food and Agriculture Organization, there is a strong need for understanding the potential (negative) impacts on food security caused by food commodity volatilities. This paper aims at investigating the presence, the size, and the persistence of volatility spillovers among five agricultural commodities (corn, sugar, wheat, soybean, and bioethanol) and five Latin American (Argentina, Brazil, Chile, Colombia, Peru) stock market indexes. Overall, when a negative shock hits the commodity market, Latin American stock market volatility tends to increase. This happens, for instance, for the relationships from corn to Chile and Colombia and from wheat to Peru and Chile

    Neural networks and betting strategies for tennis

    No full text
    Recently, the interest of the academic literature on sports statistics has increased enormously. In such a framework, two of the most significant challenges are developing a model able to beat the existing approaches and, within a betting market framework, guarantee superior returns than the set of competing specifications considered. This contribution attempts to achieve both these results, in the context of male tennis. In tennis, several approaches to predict the winner are available, among which the regression-based, point-based and paired comparison of the competitors’ abilities play a significant role. Contrary to the existing approaches, this contribution employs artificial neural networks (ANNs) to forecast the probability of winning in tennis matches, starting from all the variables used in a large selection of the previous methods. From an out-of-sample perspective, the implemented ANN model outperforms four out of five competing models, independently of the considered period. For what concerns the betting perspective, we propose four different strategies. The resulting returns on investment obtained from the ANN appear to be more broad and robust than those obtained from the best competing model, irrespective of the betting strategy adopted
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