Vincenzo Candila

Vincenzo Candila
University of Salerno | UNISA · Department of Economics and Statistics DISES

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40
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Publications

Publications (40)
Article
Full-text available
In sports journalism and among fans, there is an ongoing debate on identifying eras where the level of competition is extremely high. In tennis, a common question concerning the advent of the so-called ‘Big Three’—listed alphabetically, Novak Djokovic, Roger Federer, and Rafael Nadal—is: Did these players lead to an unprecedented high level of comp...
Preprint
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In order to meet the increasingly stringent global standards of banking management and regulation, several methods have been proposed in the literature for forecasting tail risk measures such as the Value-at-Risk (VaR) and Expected Shortfall (ES). However, regardless of the approach used, there are several sources of uncertainty, including model sp...
Article
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The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship be...
Article
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Although quantile regression to calculate risk measures is widely established in the financial literature, when considering data observed at mixed-frequency, an extension is needed. In this paper, a model is built on a mixed-frequency quantile regressions to directly estimate the Value-at-Risk (VaR) and the Expected Shortfall (ES) measures. In part...
Article
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Over the last two decades, there has been an increased attention to and awareness of corporate environmental, social, and governance (ESG) responsibilities. The asset allocation process has changed accordingly to consider these ESG responsibilities, and it has largely been recognized that private and institutional investors are sensitive to ESG fac...
Article
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The use of statistical tools for predicting the winner in tennis matches has enjoyed an increase in popularity over the last two decades and, currently, a variety of methods are available. In particular, paired comparison approaches make use of latent ability estimates or rating calculations to determine the probability that a player will win a mat...
Chapter
In this paper we introduce the use of mixed-frequency variables in a quantile regression framework to compute high-frequency conditional quantiles by means of low-frequency variables. We merge the well-known Quantile Regression Forest algorithm and the recently proposed Mixed-Data-Sampling model to build a comprehensive methodology to jointly model...
Article
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This paper is dedicated to studying and modeling the interdependence between the oil returns and exchange-rate movements of oil-exporting and oil-importing countries. Globally, twelve countries/regions are investigated, representing more than 60% and 67% of all oil exports and imports. The sample period encompasses economic and natural events like...
Article
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This paper shows the effects of the COVID-19 pandemic on energy markets. We estimate daily volatilities and correlations among energy commodities relying on a mixed-frequency approach that exploits information from the number of weekly deaths related to COVID-19 in the United States. The mixed-frequency approach takes advantage of the MIxing-Data S...
Article
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Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approxim...
Article
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The 'welo' package estimates the standard and weighted Elo (WElo, Angelini et al., 2021 <doi:10.1016/j.ejor.2021.04.011>) rates in R. The current version provides these rates for tennis. In the future, new sports will be added. The 'welo' package offers a flexible tool to estimate the WElo and Elo rates, according to different systems of weights (g...
Article
Originally applied to tennis by the data journalists of FiveThirtyEight.com, the Elo rating method estimates the strength of each player based on her/his career as well as the outcome of the last match played. Together with the regression-based, point-based and paired-comparison approaches, the Elo rating is a popular method to predict the probabil...
Article
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The 'dccmidas' package estimates a variety of Dynamic Conditional Correlation (DCC) models. More in detail, the 'dccmidas' package allows the estimation of the corrected DCC (cDCC) of Aielli (2013) <doi:10.1080/07350015.2013.771027>, the DCCMIDAS of Colacito et al. (2011) <doi:10.1016/j.jeconom.2011.02.013>, the Asymmetric DCC of Cappiello et al. <...
Article
The Double Asymmetric GARCH–MIDAS (DAGM) model has the advantage of modelling volatility as the product of two components: a slow–moving term involving variables sampled at lower frequencies and a short–run part, each with an asymmetric behavior in volatility dynamics. Such a model is extended in three directions: first, by including a market volat...
Chapter
Volatility in financial markets has both low- and high-frequency components which determine its dynamic evolution. Previous modelling efforts in the GARCH context (e.g. the Spline-GARCH) were aimed at estimating the low-frequency component as a smooth function of time around which short-term dynamics evolves. Alternatively, recent literature has in...
Preprint
Full-text available
Quantile regression is an efficient tool when it comes to estimate popular measures of tail risk such as the conditional quantile Value at Risk. In this paper we exploit the availability of data at mixed frequency to build a volatility model for daily returns with low-- (for macro--variables) and high--frequency (which may include an \virg{--X} ter...
Article
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This paper examines the volatility transmission from energy and metal commodities to six major African exporters’ stock markets (Egypt for oil and gold, Nigeria for oil and gas, South Africa for coal and gold, Tunisia for oil, Uganda for gold and Zambia for copper). Modelling commodity volatility with the Double Asymmetric GARCH-MIDAS model with a...
Article
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The rumidas package adds the MIxing Data Sampling (MIDAS, Ghysels et al. (2007)) components within the GARCH and MEM frameworks. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. The package also offers a summary tool, which synthesizes the main information of the estimated...
Article
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 empir...
Article
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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 contribu...
Preprint
Full-text available
We suggest the Doubly Multiplicative Error class of models (DMEM) for modeling and forecasting realized volatility, which combines two components accommodating low-, respectively, high-frequency features in the data. We derive the theoretical properties of the Maximum Likelihood and Generalized Method of Moments estimators. Two such models are then...
Article
In predicting conditional covariance matrices of financial portfolios, practitioners are required to choose among several alternative options, facing a number of different sources of uncertainty. A first source is related to the frequency at which prices are observed, either daily or intradaily. Using prices sampled at higher frequency inevitably p...
Article
Full-text available
Addressing the volatility spillovers of agricultural commodities is important for at least two reasons. First, during 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 o...
Article
We extend the GARCH–MIDAS model to take into account possible different impacts from positive and negative macroeconomic variations on financial market volatility: a Monte Carlo simulation which shows good properties of the estimator with realistic sample sizes. The empirical application is performed on the daily S&P500 volatility dynamics with the...
Article
The prices offered by the fixed-odd bookmakers in the tennis betting market are biased because of the favourite-longshot phenomenon. How to derive unbiased implied probabilities underlying the published odds is the focus of this study. This paper proposes a new normalization procedure that yields unbiased probabilities, regardless of the presence o...
Chapter
Forecasting conditional covariance matrices of returns involves a variety of modeling options. First, the choice between models based on daily or intradaily returns. Examples of the former are the Multivariate GARCH (MGARCH) models while models fitted to Realized Covariance (RC) matrices are examples of the latter. A second option, strictly related...
Article
Multivariate volatility models can be evaluated via direct and indirect approaches. The former uses statistical loss functions (LFs) and a proxy to provide consistent estimates of the unobserved volatility. The latter uses utility LFs or other instruments, such as value-at-risk and its backtesting procedures. Existing studies commonly employ these...
Article
Full-text available
Forecasting oil prices is not straightforward, such that it is convenient to build a confidence interval around the forecasted prices. To this end, the principal ingredient for obtaining a reliable crude oil confidence interval is its volatility. Moreover, accurate crude oil volatility estimation has fundamental implications in terms of risk manage...
Article
The evaluation of volatility forecasts is not straightforward and some issues can arise. A standard approach relies on statistical loss functions. Another approach bases the evaluation of the volatility predictions on utility functions or Value at Risk (VaR) measures. This work aims to combine the two approaches, using the VaR measures within the l...
Chapter
Many methods can be considered to select which volatility model has a better forecast accuracy. In this work a loss function approach in a Value at Risk (VaR) framework is chosen. By using high-frequency data it is possible to achieve a consistent estimate of the VaR bootstrapping the intraday increments of an asset. The VaR estimate is used to fin...

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