Examinando por Autor "Perote, Javier"
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Ítem Determining the banking solvency risk in times of COVID-19 through Gram-Charlier expansions(Universidad EAFIT, 2021-09-20) Rendón, Juan F.; Cortés, Lina M.; Perote, Javier; Instituto Tecnológico Metropolitano - ITM; Universidad EAFIT; University of SalamancaThis paper proposes risk measures for bank solvency by accurately measuring the solvency risk components. These measures consider the minimum regulatory solvency levels and banks’ risk appetite level and risk profile. For this purpose, we used semi-nonparametric statistics to model stylized facts of the risk distribution, particularly the high-order moments of the Solvency Decline Rate, the Tier Decline Rate, and the Portfolio Growth Rate variables. Additionally, these risk measures can be used to measure the risk of regulatory intervention and to define policies that establish the minimum solvency levels required by banking regulators by estimating the Quantile Risk Metrics. As a case study, we collected data on the solvency indicators of the Colombian banking system, which adapts to the standards established by the Basel Committee. According to the results, the liquidity injection measures implemented in response to the needs generated by the COVID-19 pandemic led to an increase in the levels of the risk portfolio in the Colombian banking system, which exceeded the 99th percentile of the probability distribution of monthly portfolio value changes.Ítem Firm Size and Concentration Inequality: A Flexible Extension of Gibrat’s Law(2019-03-08) Cortes, L.; Lozada, Juan M.; Perote, Javier; Cortes, L.; Lozada, Juan M.; Perote, Javier; Universidad EAFIT. Departamento de Economía y Finanzas; Finanzas y Banca (Gifyb)Ítem Firm size and economic concentration: An analysis from lognormal expansion(Universidad EAFIT, 2020-06-08) Cortés, Lina; Lozada, Juan; Perote, Javier; lcortesd@eafit.edu.co; jmlozadah@eafit.edu.coThis paper proposes a semi-nonparametric (SNP) generalization of the lognormal distribution for studying firm size and providing accurate measures for economic concentration and inequality in terms of the Gini index adjusted to flexible functional forms. The empirical application for a sample of 1,772 Colombian companies from 2002 to 2015 shows that the log-SNP provides a better fit to firm size distribution, especially for the extreme quantiles, for which lognormal distribution overestimates economic concentration. In addition, dynamic panel model estimates indicate that firm characteristics, including size, age, and leverage, are determining factors in explaining firm growth, thus rejecting Gibrat’s law.Ítem Implicit probability distribution for WTI options: The Black Scholes vs. the semi-nonparametric approach(Universidad EAFIT, 2017-12-05) Cortés, Lina M.; Mora-Valencia, Andrés; Perote, Javier; lcortesd@eafit.edu.coThis paper contributes to the literature on the estimation of the Risk Neutral Density (RND) function by modeling the prices of options for West Texas Intermediate (WTI) crude oil that were traded in the period between January 2016 and January 2017. For these series we extract the implicit RND in the option prices by applying the traditional Black & Scholes (1973) model and the semi-nonparametric (SNP) model proposed by Backus, Foresi, Li, & Wu (1997). The results obtained show that when the average market price is compared to the average theoretical price, the lognormal specification tends to systematically undervalue the estimation. On the contrary, the SNP option pricing model, which explicitly adjust for negative skewness and excess kurtosis, results in markedly improved accuracy.Ítem Implicit probability distribution for WTI options: The Black Scholes vs. the semi-nonparametric approach(2018-01-25) Cortes, L.; Mora, A.; Perote, Javier; Cortes, L.; Mora, A.; Perote, Javier; Universidad EAFIT. Departamento de Economía y Finanzas; Finanzas y Banca (Gifyb)Ítem Measuring firm size distribution with semi-nonparametric densities(Universidad EAFIT, 2017-01-16) Cortés, Lina; Mora-Valencia, Andrés; Perote, Javier; lcortesd@eafit.edu.coIn this article, we propose a new methodology based on a (log) semi-nonparametric (log- SNP) distribution that nests the lognormal and enables better fits in the upper tail of the distribution through the introduction of new parameters. We test the performance of the lognormal and log-SNP distributions capturing firm size, measured through a sample of US firms in 2004-2015. Taking different levels of aggregation by type of economic activity, our study shows that the log-SNP provides a better fit of the firm size distribution. We also formally introduce the multivariate log-SNP distribution, which encompasses the multivariate lognormal, to analyze the estimation of the joint distribution of the value of the firm’s assets and sales. The results suggest that sales are a better firm size measure, as indicated by other studies in the literature.Ítem Measuring firm size distribution with semi-nonparametric densities(2017-01-16) Lina M. Cortés; Mora-Valencia, A.; Perote, Javier; Lina M. Cortés; Mora-Valencia, A.; Perote, Javier; Universidad EAFIT. Departamento de Economía y Finanzas; Finanzas y Banca (Gifyb)In this article, we propose a new methodology based on a (log) semi-nonparametric (log- SNP) distribution that nests the lognormal and enables better fits in the upper tail of the distribution through the introduction of new parameters. We test the perforÍtem Modeling electricity price and quantity uncertainty: An application for hedging with forward contracts(Universidad EAFIT, 2020-06-08) Trespalacios, Alfredo; Cortés, Lina; Perote, Javier; lcortesd@eafit.edu.coEnergy purchases/sales in liberalized markets are subject to price and quantity uncertainty, which should be jointly modeled by relaxing the unreliable normality assumption for capturing risk. In this paper, we consider the spot price and energy generation to follow a bivariate semi-nonparametric distribution defined in terms of the Gram-Charlier expansion. This distribution allows to jointly model not only mean, variance, and correlation, but also skewness, kurtosis, and higher-order moments. Based on this model, we propose a static hedging strategy for electricity generators that participate in a competitive market where hedging is carried out through forward contracts that include a risk premium in their valuation. For this purpose, we use Monte Carlo simulation and consider information from the Colombian electricity market as the case study. The results show that the volume of energy to be sold under long-term contracts depends on each electricity generator and the risk assessment made by the market in the Forward Risk Premium. The conditions of skewness, kurtosis, and correlation, as well as the type of risk indicator to be employed, affect the hedging strategy that each electricity generator should implement.Ítem Modeling the electricity spot price with switching regime semi-nonparametric distributions(Universidad EAFIT, 2019-11-22) Trespalacios, Alfredo; Cortés, Lina M.; Perote, Javier; alfredotrespalacios@itm.edu.coSpot prices of electricity in liberalized markets feature seasonality, mean reversion, random short-term jumps, skewness and highly kurtosis, as a result from the interaction between the supply and demand and the physical restrictions for transportation and storage. To account for such stylized facts, we propose a stochastic process with a component of mean reversion and switching regime to represent the dynamics of the spot price of electricity and its logarithm. The short-term movements are represented by semi-nonparametric (SNP) distributions, in contrast to previous studies that traditionally assume Gaussian processes. The application is done for the Colombian electricity market, where El Niño phenomenon represents an additional source of risk that should be considered to guarantee long-term supply, sustainability of investments and efficiency of prices. We show that the switching regime model with SNP distributions for the random components outperforms traditional models leading to accurate estimates and simulations, and thus being a useful tool for risk management and policy making.Ítem The productivity of top researchers: A semi-nonparametric approach(Universidad EAFIT, 2016-03-02) Cortés, Lina M.; Perote, Javier; Mora-Valencia, Andrés; lcortesd@eafit.edu.coResearch productivity distributions exhibit heavy tails because it is common for a few researchers to accumulate the majority of the top publications and their corresponding citations. Measurements of this productivity are very sensitive to the field being analyzed and the distribution used. In particular, distributions such as the lognormal distribution seem to systematically underestimate the productivity of the top researchers. In this article, we propose the use of a (log)semi-nonparametric distribution (log-SNP) that nests the lognormal and captures the heavy tail of the productivity distribution through the introduction of new parameters linked to high-order moments. To compare the results, we use research performance data on 140,971 researchers who have produced 253,634 publications in 18 fields of knowledge (O’Boyle and Aguinis, 2012) and show how the log-SNP distribution provides more accurate measures of the performance of the top researchers in their respective fields of knowledge.Ítem The Productivity of Top Researchers: A Semi-Nonparametric Approach(2016-03-20) Cortes, L.; Mora, A.; Perote, Javier; Cortes, L.; Mora, A.; Perote, Javier; Universidad EAFIT. Departamento de Economía y Finanzas; Finanzas y Banca (Gifyb)Ítem Semi-nonparametric VaR forecasts for hedge funds during the recent crisis(Elsevier, 2014) B. Del Brio, Esther; Mora-Valencia, Andrés; Perote, Javier; Faculty of Economics and Business, Department of Business, University of Salamanca, Spain; School of Economics and Finance, Department of Finance, EAFIT University, Colombia; Faculty of Economics and Business, Department of Economics, University of Salamanca, Spain; Economía y Finanzas; Finanzas; Grupo de Investigación Finanzas y BancaThe need to provide accurate value-at-risk (VaR) forecasting measures has triggered an important literature in econophysics. Although these accurate VaR models and methodologies are particularly demanded for hedge fund managers, there exist few articles specifically devoted to implement new techniques in hedge fund returns VaR forecasting. This article advances in these issues by comparing the performance of risk measures based on parametric distributions (the normal, Student’s t and skewed-t), semi-nonparametric (SNP) methodologies based on Gram–Charlier (GC) series and the extreme value theory (EVT) approach. Our results show that normal-, Student’s t- and Skewed t- based methodologies fail to forecast hedge fund VaR, whilst SNP and EVT approaches accurately success on it. We extend these results to the multivariate framework by providing an explicit formula for the GC copula and its density that encompasses the Gaussian copula and accounts for non-linear dependences. We show that the VaR obtained by the meta GC accurately captures portfolio risk and outperforms regulatory VaR estimates obtained through the meta Gaussian and Student’s tdistributions.Ítem Uncertainty in Electricity Markets from a seminonparametric Approach(Universidad EAFIT, 2019-06-04) Trespalacios, Alfredo; Cortés, Lina M.; Perote, Javier; lcortesd@eafit.edu.coThe spot price of electricity is highly skewed and heavy-tailed, as a result of the interaction of different variables that affect that market. Such characteristics impact the design of power plants with different technologies, fuel prices, and energy demand. This paper introduces the semi-nonparametric (SNP) approach to describe the uncertainty of different variables in an electricity market, reducing the limitations that normality and parametric density functions impose. The selection of probability density functions is achieved in terms of a finite Gram– Charlier expansion fitted by the maximum likelihood criterion. The study case is the Colombian electricity market, where the SNP distribution outperforms the normal distribution for spot price, national energy demand, the climate index ONI, and the series of hydrologic inflows of the system and some rivers. The results show that risk analysis in electricity markets requires the measurement of skewness, kurtosis, and high-order moments. The flexible methodology in our study has directly applications for implementing policies on electricity markets that improve the sustainability indicators of different systems. The particular characteristics of the series under analysis should be considered as a starting point for risk analysis and portfolio choice.Ítem Uncertainty in Electricity Markets from a Seminonparametric Approach(2019-06-11) TRESPALACIOS, ALFREDO; Cortes, L.; Perote, Javier; TRESPALACIOS, ALFREDO; Cortes, L.; Perote, Javier; Universidad EAFIT. Departamento de Economía y Finanzas; Finanzas y Banca (Gifyb)Ítem VaR performance during the subprime and sovereign debt crises: An application to emerging markets(Elsevier, 2014) B. Del Brio, Esther; Mora-Valencia, Andrés; Perote, Javier; aculty of Economics and Business, Department of Business, University of Salamanca, Spain; School of Economics and Finance, Department of Finance, EAFIT University, Colombia; Faculty of Economics and Business, Department of Economics, University of Salamanca, Spain; Economía y Finanzas; Finanzas; Grupo de Investigación Finanzas y BancaHighly volatile scenarios, such as those provoked by the recent subprime and sovereign debt crises, have questioned the accuracy of current risk forecasting methods. This paper adds fuel to this debate by comparing the performance of alternative specifications for modeling the returns filtered by an ARMA-GARCH: Parametric distributions (Student's t and skewed-t), the extreme value theory (EVT), semi-nonparametric methods based on the Gram–Charlier (GC) expansion and the normal (benchmark). We implement backtesting techniques for the pre-crisis and crisis periods for stock index returns and a hedge fund of emerging markets. Our results show that the Student's t fails to forecast VaR during the crisis, while the EVT and GC accurately capture market risk, the latter representing important savings in terms of efficient regulatory capital provisions.