Vine copula modelling of dependence and portfolio optimization with application to mining and energy stock return series from the Australian market

This thesis models the dependence risk profile, investment risk and portfolio allocation features of seven 20-stock portfolios from the mining, energy, retail and manufacturing sectors of the Australian market in the context of the 2008-2009 global financial crisis (2008-2009 GFC) and pre-GFC, GFC, post-GFC and full sample period scenarios revolving around it. The mining and energy portfolios are the base of the study, while the retail and manufacturing are considered for benchmarking purposes. Pair vine copula models including canonical vines (c-vines), drawable vines (d-vines) and regular vines (r-vines) are fitted for the analysis of the portfolios’ multivariate dependence and their underlying sectors’ dependence risk dynamics. Besides, linear and nonlinear optimization methods threaded with the variance, mean absolute deviation (MAD), minimizing regret (Minimax), conditional Value-at-Risk (CVaR) and conditional Drawdown-at-Risk (CDaR) risk measures are implemented to examine the portfolios’ investment risk and optimal portfolio allocation features. The vine copula modelling of dependence aims at examining the dependence risk profile of the portfolios in specific market conditions; studying the changes of the portfolios’ dependence structure between pairs of period scenarios; and recognizing the vine copula models that best account for the portfolios’ multivariate dependence. The multiple risk measure-based portfolio optimization seeks to identify the least and most investment risky portfolios, single out the portfolio that offers the best risk-return trade-off and recognize the stocks in the portfolios that are good candidates for investment. This thesis’ main contributions stem from the “copula counting technique” and “average model convergence” perspectives proposed to handle, analyse and interpret the portfolios’ dependence structure and portfolio allocation features. The copula counting technique aside from simplifying the analysis and interpretation of the assets’ dependence structure, it enables an in-depth and comprehensive analysis of their underlying dependence risk dynamics in specific market conditions. The average model convergence addresses the optimal stock selection and investment confidence problems underlying any type of portfolio optimization, and faced by investors when having to select stocks from a wide array of optimal investment scenarios, in a more objective manner, through model convergence and model consensus. Both, the copula counting technique and average model convergence are new concepts that introduce new theory to the pair vine copula and multiple risk measure-based portfolio optimization literatures. The research findings stemming from the vine copula modelling of dependence indicate that the each of the portfolios modelled has dependence risk features consistent with specific market conditions. Out of the seven portfolios modelled the gold mining and retail benchmark portfolios are found to have the lowest dependence risk in times of financial turbulence. The iron ore-nickel mining and oil-gas energy portfolios have the highest dependence risk in similar market conditions. Out of the energy portfolios the coal-uranium is significantly less dependence risky, relative to the oil-gas. Out of the mining portfolios the iron ore-nickel is the most dependence risky, while the gold portfolio has the lowest dependence risk. The retail benchmark portfolio is significantly less dependence risky than the manufacturing benchmark portfolio in both, tranquil periods and non-tranquil periods. In terms of investment risk, the oil-gas energy portfolio is the most risky. The “copula counting technique” is acknowledged for simplifying the analysis and interpretation of the portfolios’ dependence structure and their sectors’ dependence risk dynamics. The average model convergence provides an alternative avenue to identify stocks with large weight allocations and high return relative to risk. The research findings and empirical results are interesting in terms of theory and practical financial applications. Portfolio managers, risk managers, hedging practitioners, financial market analysts, systemic risk and capital requirement agents, who follow the trends of the Australian mining, energy, retail and manufacturing sectors, may find the obtained results useful to design investment risk and dependence risk-adjusted optimization algorithms, risk management frameworks and dynamic hedging strategies that best account for the downside risk the mining and energy sectors face during crisis periods to the pair vine copula and multiple risk measure-based portfolio optimization literatures. The research findings stemming from the vine copula modelling of dependence indicate that the each of the portfolios modelled has dependence risk features consistent with specific market conditions. Out of the seven portfolios modelled the gold mining and retail benchmark portfolios are found to have the lowest dependence risk in times of financial turbulence. The iron ore-nickel mining and oil-gas energy portfolios have the highest dependence risk in similar market conditions. Out of the energy portfolios the coal-uranium is significantly less dependence risky, relative to the oil-gas. Out of the mining portfolios the iron ore-nickel is the most dependence risky, while the gold portfolio has the lowest dependence risk. The retail benchmark portfolio is significantly less dependence risky than the manufacturing benchmark portfolio in both, tranquil periods and non-tranquil periods. In terms of investment risk, the oil-gas energy portfolio is the most risky. The “copula counting technique” is acknowledged for simplifying the analysis and interpretation of the portfolios’ dependence structure and their sectors’ dependence risk dynamics. The average model convergence provides an alternative avenue to identify stocks with large weight allocations and high return relative to risk. The research findings and empirical results are interesting in terms of theory and practical financial applications. Portfolio managers, risk managers, hedging practitioners, financial market analysts, systemic risk and capital requirement agents, who follow the trends of the Australian mining, energy, retail and manufacturing sectors, may find the obtained results useful to design investment risk and dependence risk-adjusted optimization algorithms, risk management frameworks and dynamic hedging strategies that best account for the downside risk the mining and energy sectors face during crisis periods

Optimal risk minimization of Australian energy and mining portfolios under multiple measures of risk

Australia’s 2000’s decade saw the sharpest rise in mining investments arising from developing Asian emerging economies’ high demand for commodities like coal, iron ore, nickel, oil and gas which drove up prices to a historic level (Connolly & Orsmond, 2011). As of December 2012, 39 % and 9 % of the Australian Securities Exchange’s stocks were of the mining (coal and uranium stocks are included in this category) and energy (e.g. oil, gas and renewable energy stocks) sectors respectively, and investors recently have been considering separate portfolio positions in energy and mining stocks (Jennings, 2010). Facts of these nature set the stage for the task of selecting an optimal portfolio of stock securities where the fundamental questions faced by every investor, individual or institutional, are: a) what is the optimal point in time to go long in the investment position?, b) what are the optimal amounts to invest in every asset of a portfolio? and, c) when is the optimal time to short the portfolio investment position? The focus of the present study is on b) within a one period ahead forecast scenario. Understanding the price and volatility movements of stock securities taking as a basis of study their own dynamics and co-dynamics is a complex task that may be better addressed through a multilateral modelling approach. This paper, in this regard, departs from a single model application by fitting multiple risk measures to the optimization of four portfolios each consisting of 20 ASX’s stocks from the gold, iron ore-nickel, uranium-coal and oil-gas sectors. The five risk measures compared are: the variance, mean absolute deviation (MAD), minimizing regret (Minimax), conditional value at risk (CVaR), and conditional drawdown at risk (CDaR), where the last two are threshold based measures. The risk measure parameters are input into meanvariance quadratic (QP) and differential evolution (DE) portfolio problem specifications. Accurate estimations of the underlying interaction of stocks return series is a crucial element in portfolio allocation and portfolio risk management and frequentist traditional measures of dependence are rather inadequate. Here, with the objective of achieving more accuracy in the estimation of the dependence matrix, a Gaussian pair c-vine copula (PC), the regular graphical lasso (RL) and adaptive graphical lasso (AL) are fitted. Possible advantages from using these recently proposed and sophisticated techniques under model specifications where the covariance matrix is the measure of risk are indicated. The main objectives of the present study are to calculate the optimal weights to be invested in every stock of the portfolios making use of linear and nonlinear model specifications and the risk measures suggested, analyse the weight allocation differences and seek portfolio optimization advantages from using pair vine copulas and the graphical lasso in the estimation of dependence. The present multimodal approach is, therefore, expected to be more robust and as a consequence, provide more complete information that could serve for improved decision making on portfolio selection, allocation and rebalancing. Research questions are answered based on the analysis of gold portfolio outcome values, only. Findings indicate that CDaR is an important risk measure to be considered, along with other measures of risk when optimizing portfolios of stocks and no single measure of risk is suggested alone. The Gaussian pair cvine copula through the use of one different parameter in the modelling of every pair of variables’ joint distribution appears to be more sensitive in capturing data’s distribution characteristics. The adaptive graphical lasso also appears to be more perceptive when grasping the signal of the underlying interaction of the stocks. Therefore, valuable information could be drawn and inferred from applying multiple risk measures and sophisticated statistical techniques for their estimation. The weight allocation from threshold risk measures such as CVar and DaR and Minimax clearly differs from the rest. The models identified stocks with high return relative to risk and vice versa. The originality of the present study lies on the sectors of application and its multi-model nature

Dependence estimation and controlled CVaR portfolio optimization of a highly kurtotic Australian mining sample of stocks

The drivers of mining stock prices are known to be several. Sharp spikes on the stocks return distribution have been linked to the presence of unusually high volatility signifying the presence of high levels of kurtosis. The accurate measurement of the stocks’ underlying co-movements for more accurate CVaR portfolio optimization requires, therefore, the utilization of sophisticated and specific-specialized techniques which could adequately capture and model these characteristics. Here this issue is addressed by applying statistical-graphical models for dependence estimation. Twenty mining stocks, out of the 801 listed in the ASX as of December 2012, have been selected for the analysis under the criteria of satisfying the eight years trading period sought, having very weak or no autocorrelation of residuals and displaying the highest kurtosis. Models’ estimations of dependence are compared and inserted into a differential evolution algorithm for non-convex global optimization in order to conduct risk controlled CVaR portfolio optimization (Ardia, Boudt, Carl, Mullen & Peterson, 2011) and be able to identify the one yielding the highest portfolio return. The findings are of relevance in portfolio allocation and portfolio risk management. Energy and mining stock markets are subjected to numerous price drivers holding complex relationships. The dynamics of production and consumption based on seasonality features, transportation and storage, weather conditions, commodity price fluctuations, currency changes, market confidence and expectations, trading speculations and the domestic and international states of the economy impact mining stock prices in particular and unobvious ways reflected in high volatility with sudden spikes in the stock’s return distribution (Pilipovic, 1998). The generation of accurate measurements of the dependence matrix of mining stock’s return series is therefore both, a non-trivial task due to the hard to decipher characteristics present in return series suffering from high levels of kurtosis (Carvalho, Lopes & Aguilar, 2010) and, a crucial element in portfolio optimization and portfolio risk management. The use of graphical techniques in this study is justified on the basis of their utility and suitableness. Graphical models such as pair c-vine copulas, the graphical lasso and adaptive graphical lasso provide, for instance, the visualization and flexibility to represent a problem in a more simplified and dissected form (Lauritzen, 1996). Graphs also appear to be naturally adequate to express the interaction of variables and thus facilitate the analysis of their dependency. The models of dependence estimation and CVaR portfolio optimization, on the other hand, are desirable due to mathematical and statistical framework they provide which may lead to satisfactory results and, their apparent ability to overcome the flaws (i.e. standardized model application to all joint distributions, restrictive and deterministic linear and monotonic modelling functions as in the Pearson and Spearman) traditional measures display when dealing with highly kurtotic data, joint distributions with stronger dependence in the tails and controlled risk non-convex portfolio optimization problems. Findings indicate that the highest portfolio returns are generated by inserting the covariance output matrix from the student-t copula into the differential optimization algorithm and, the student-t copula fitting with separate modelling of the marginal distributions appears to be the most desirable modelling choice. The portfolio return by the adaptive graphical lasso is lower than that of the student-t and is followed by the Gaussian pair c-vine copula. The regular graphical lasso produced the lowest portfolio return and the covariance matrix values were higher for models producing the highest portfolio returns implying that the models generating the lowest portfolio returns underestimated the dependence of the assets. The implications of the findings suggest that specific modelling of each marginal distribution, as compared to modelling based on a Gaussian framework, may lead to an edge in the estimations due to the distribution differences encountered on each marginal. Furthermore, the ability of the model to capture dependence in the tails, as it is the case of the student-t copula, does provide a modelling advantage too. This paper appears to be the first one in, comparing the portfolio performance of the models of dependence estimation in the context of controlled CVaR, applying the models treated to a highly kurtotic mining sample of stocks from the Australian market and modelling separately the distribution of the marginals when fitting the student-t copula

Multivariate dependence and portfolio optimization algorithms under illiquid market scenarios

We propose a model for optimizing structured portfolios with liquidity-adjusted Value-at-Risk (LVaR) constraints, whereby linear correlations between assets are replaced by the multivariate nonlinear dependence structure based on Dynamic Conditional Correlation t-copula modeling. Our portfolio optimization algorithm minimizes the LVaR function under adverse market circumstances and multiple operational and financial constraints. When we consider a diversified portfolio of international stock and commodity market indices under multiple realistic portfolio optimization scenarios, the obtained results consistently show the superiority of our approach relative to other competing portfolio strategies including the minimum-variance, risk-parity and equally weighted portfolio allocations

Estado de México y democracia en los albores del siglo XXI

De acuerdo con su título, este libro, compuesto por seis capítulos y dos anexos, reúne textos relativos a la democracia y al Estado de México, una de las principales entidades federativas de la República Mexicana. La importancia del Estado de México en el contexto nacional es indiscutible: de las 32 entidades que integran el país, es la que tiene más habitantes y electores (el segundo y el tercer lugares en ambos sentidos son ocupados, respectivamente, por el Distrito Federal y Veracruz), en tanto que está en el segundo lugar por el tamaño de su economía (en el primero se ubica el Distrito Federal y en el tercero, Nuevo León)

Global financial crisis and dependence risk analysis of sector portfolios: a vine copula approach

We use regular vine (r-vine), canonical vine (c-vine) and drawable vine (d-vine) copulas to examine the dependence risk characteristics of three 20-stock portfolios from the retail, manufacturing and gold-mining equity sectors of the Australian market in periods before, during and after the 2008-2009 global financial crisis (GFC). Our results indicate that the retail portfolio is less risky than the manufacturing counterpart in the crisis period, while the gold-mining portfolio is less risky than both the retail and manufacturing sector portfolios. Both the retail and gold stocks display a higher propensity to yield positively skewed returns in the crisis periods, contrary to the manufacturing stocks. The r-vine is found to best capture the multivariate dependence structure of the stocks in the retail and gold-mining portfolios, while the d-vine does it for the manufacturing stock portfolio. These findings could be used to develop dependence risk and investment risk-adjusted strategies for investment, rebalancing and hedging which more adequately account for the downside risk in various market conditions

Global financial crisis and dependence risk analysis of sector portfolios: a vine copula approach

We use regular vine (r-vine), canonical vine (c-vine) and drawable vine (d-vine) copulas to examine the dependence risk characteristics of three 20-stock portfolios from the retail, manufacturing and gold-mining equity sectors of the Australian market in periods before, during and after the 2008-2009 global financial crisis (GFC). Our results indicate that the retail portfolio is less risky than the manufacturing counterpart in the crisis period, while the gold-mining portfolio is less risky than both the retail and manufacturing sector portfolios. Both the retail and gold stocks display a higher propensity to yield positively skewed returns in the crisis periods, contrary to the manufacturing stocks. The r-vine is found to best capture the multivariate dependence structure of the stocks in the retail and gold-mining portfolios, while the d-vine does it for the manufacturing stock portfolio. These findings could be used to develop dependence risk and investment risk-adjusted strategies for investment, rebalancing and hedging which more adequately account for the downside risk in various market conditions

Immune protection against Trypanosoma cruzi induced by TcVac4 in a canine model

Chagas disease, caused by Trypanosoma cruzi, is endemic in southern parts of the American continent. Herein, we have tested the protective efficacy of a DNA-prime/T. rangeli-boost (TcVac4) vaccine in a dog (Canis familiaris) model. Dogs were immunized with two-doses of DNA vaccine (pcDNA3.1 encoding TcG1, TcG2, and TcG4 antigens plus IL-12- and GMCSF- encoding plasmids) followed by two doses of glutaraldehyde-inactivated T. rangeli epimastigotes (TrIE); and challenged with highly pathogenic T. cruzi (SylvioX10/4) isolate. Dogs given TrIE or empty pcDNA3.1 were used as controls. We monitored post-vaccination and post-challenge infection antibody response by an ELISA, parasitemia by blood analysis and xenodiagnosis, and heart function by electrocardiography. Post-mortem anatomic and pathologic evaluation of the heart was conducted. TcVac4 induced a strong IgG response (IgG2>IgG1) that was significantly expanded post-infection, and moved to a nearly balanced IgG2/IgG1 response in chronic phase. In comparison, dogs given TrIE or empty plasmid DNA only developed high IgG titers with IgG2 predominance in response to T. cruzi infection. Blood parasitemia, tissue parasite foci, parasite transmission to triatomines, electrocardiographic abnormalities were significantly lower in TcVac4-vaccinated dogs than was observed in dogs given TrIE or empty plasmid DNA only. Macroscopic and microscopic alterations, the hallmarks of chronic Chagas disease, were significantly decreased in the myocardium of TcVac4-vaccinated dogs.We conclude that TcVac4 induced immunity was beneficial in providing resistance to T. cruzi infection, evidenced by control of chronic pathology of the heart and preservation of cardiac function in dogs. Additionally, TcVac4 vaccination decreased the transmission of parasites from vaccinated/infected animals to triatomines.CONACYT PROY No. 156701 UAEM PROY No. 2381/2006U National Institutes of Health/National Institute of Allergy and Infectious Diseases http://www.niaid.nih.gov/Pages/ default.aspx GRANT NUMBER (AI072538) NJG; American Heart Association http://www.heart.org/ HEARTORG/ GRANT NUMBER (0855059F) to NJG

Apoptosis induction in Jurkat cells and sCD95 levels in women's sera are related with the risk of developing cervical cancer

<p>Abstract</p> <p>Background</p> <p>Currently, there is clear evidence that apoptosis plays an important role in the development and progression of tumors. One of the best characterized apoptosis triggering systems is the CD95/Fas/APO-1 pathway; previous reports have demonstrated high levels of soluble CD95 (sCD95) in serum of patients with some types of cancer. Cervical cancer is the second most common cancer among women worldwide. As a first step in an attempt to design a minimally invasive test to predict the risk of developing cervical cancer in patients with precancerous lesions, we used a simple assay based on the capacity of human serum to induce apoptosis in Jurkat cells. We evaluated the relationship between sCD95 levels and the ability to induce apoptosis in Jurkat cells in cervical cancer patients and controls.</p> <p>Methods</p> <p>Jurkat cells were exposed to serum from 63 women (20 healthy volunteers, 21 with cervical intraepithelial neoplasia grade I [CIN 1] and 22 with cervical-uterine carcinoma). The apoptotic rate was measured by flow cytometry using Annexin-V-Fluos and Propidium Iodide as markers. Serum levels of sCD95 and soluble CD95 ligand (sCD95L) were measured by ELISA kits.</p> <p>Results</p> <p>We found that serum from almost all healthy women induced apoptosis in Jurkat cells, while only fifty percent of the sera from women with CIN 1 induced cell death in Jurkat cells. Interestingly, only one serum sample from a patient with cervical-uterine cancer was able to induce apoptosis, the rest of the sera protected Jurkat cells from this killing. We were able to demonstrate that elimination of Jurkat cells was mediated by the CD95/Fas/Apo-1 apoptotic pathway. Furthermore, the serum levels of sCD95 measured by ELISA were significantly higher in women with cervical cancer.</p> <p>Conclusion</p> <p>Our results demonstrate that there is a strong correlation between low levels of sCD95 in serum of normal women and higher apoptosis induction in Jurkat cells. We suggest that an analysis of the apoptotic rate induced by serum in Jurkat cells and the levels of sCD95 in serum could be helpful during the prognosis and treatment of women detected with precancerous lesions or cervical cancer.</p

Contribución de la producción animal en pequeña escala al desarrollo rural

La producción y el consumo de productos de origen animal han experimentado un rápido crecimiento en todo el mundo, y se prevé que continuarán aumentando. Se considera que la mayor parte del incremento en la producción provendrá de sistemas de producción en pequeña escala, que representan el medio de vida de hasta un 70% de la población rural pobre del mundo.1 La producción animal en pequeña escala se reconoce en todo el mundo como un elemento que contribuye al alivio de la pobreza en el medio rural, mediante generación de ingresos, oportunidades de ocupación y dinamismo del uso de los recursos disponibles. Por lo tanto, es de suma importancia conocer las dinámicas de estos sistemas de producción animal y su contribución al desarrollo rural en México. Investigadores y extensionistas deben priorizar las demandas de la producción animal en las comunidades rurales, ya que la producción animal en pequeña escala ha contribuido a mejorar la calidad de vida y a disminuir la vulnerabilidad de las familias productoras. En el México prehispánico la población sólo criaba xoloitzcuintle y guajolotes como animales domésticos, y complementaba en proteínas su dieta con la caza y la pesca. Sin embargo, con la llegada de los españoles en 1521 llegaron también los primeros bovinos a la Nueva España, que se reprodujeron con suma rapidez. La carne de bovino llegó a constituir una parte sustancial de la dieta alimenticia de toda la población.2 A pesar de que al inicio la producción animal era casi nula, ésta empezó a desarrollarse rápidamente y en la actualidad representa un pilar importante para el desarrollo rural en las familias campesinas de nuestro país, pues es vista como una fuente de ingreso