50 research outputs found
Continuous time volatility modelling: COGARCH versus Ornstein-Uhlenbeck models
We compare the probabilistic properties of the non-Gaussian Ornstein-Uhlenbeck based stochastic volatility model of Barndorff-Nielsen and Shephard (2001) with those of the COGARCH process. The latter is a continuous time GARCH process introduced by the authors (2004). Many features are shown to be shared by both processes, but differences are pointed out as well. Furthermore, it is shown that the COGARCH process has Pareto like tails under weak regularity conditions
Catálogo de autoridades da Rede BIM (DPHDM): Estudo de caso no tratamento de registros de pontos de acesso
Identificar possíveis problemas na alimentação da base de autoridades de assuntos da Rede BIM através da utilização do software Pergamum, e contribuir para padronização de seus pontos de acesso. A partir da revisão da literatura proposta, considera a importância da recuperação da informação para sua disseminação e ainda faz um breve levantamento dos padrões internacionais utilizados para esse fim. A metodologia utilizada envolveu a análise da base, limitada à letra “c” do alfabeto, e se propôs a detectar possíveis inconsistências existentes. Os principais problemas encontrados foram: falhas de grafia, falhas de entrada de cabeçalhos e duplicidade de assuntos. Os resultados práticos para a Rede BIM são: agilização dos serviços de catalogação e referência da Rede BIM, maior controle bibliográfico e facilidade na recuperação de itens nas pesquisas dos usuários. O Estudo de caso se propõe a contribuir com esclarecimentos quanto a equívocos cometidos, de forma a auxiliar na padronização e documentação do processo de alimentação da base de autoridades de assuntos pelos bibliotecários das 45 organizações militares, possibilitando benefícios aos serviços de catalogação e referência e finalmente, propiciando um atendimento mais prático e ágil ao usuário da Rede BIM
Modelling Dynamic Conditional Correlations in WTI Oil Forward and Futures Returns
This paper estimates the dynamic conditional correlations in the returns on WTI oil one-month forward prices, and one-, three-, six-, and twelve-month futures prices, using recently developed multivariate conditional volatility models. The dynamic correlations enable a determination of whether the forward and various futures returns are substitutes or complements, which are crucial for deciding whether or not to hedge against unforeseen circumstances. The models are estimated using daily data on WTI oil forward and futures prices, and their associated returns, from 3 January 1985 to 16 January 2004. At the univariate level, the estimates are statistically significant, with the occasional asymmetric effect in which negative shocks have a greater impact on volatility than positive shocks. In all cases, both the short- and long-run persistence of shocks are statistically significant. Among the five returns, there are ten conditional correlations, with the highest estimate of constant conditional correlation being 0.975 between the volatilities of the three-month and six-month futures returns, and the lowest being 0.656 between the volatilities of the forward and twelve-month futures returns. The dynamic conditional correlations can vary dramatically, being negative in four of ten cases and being close to zero in another five cases. Only in the case of the dynamic volatilities of the three-month and six-month futures returns is the range of variation relatively narrow, namely (0.832, 0.996). Thus, in general, the dynamic volatilities in the returns in the WTI oil forward and future prices can be either independent or interdependent over time
Conditional Correlations in the Returns on Oil Companies Stock Prices and Their Determinants
The identification of the forces that drive stock returns and the dynamics of their associated volatilities is a major concern in empirical economics and finance. This analysis is particularly relevant for determining optimal hedging strategies based on whether shocks to the volatilities of returns of oil companies stock prices, relevant stock market indexes and oil spot and futures prices are high or low, and positively or negatively correlated. This paper investigates the correlations of volatilities in the stock price returns and their determinants for the most important integrated oil companies, namely Bp (BP), Chevron-Texaco (CVX), Eni (ENI), Exxon-Mobil (XOM), Royal Dutch (RD) and Total-Fina Elf (TFE). We measure the actual co-risk in stock returns and their determinants within and between the different oil companies, using multivariate cointegration techniques in modelling the conditional mean, as well as multivariate GARCH models for the conditional variances. We focus first on the determinants of the market value of each company using the cointegrated VAR/VECM methodology. Then we specifiy the conditional variances of VECM residuals with the Constant Conditional Correlation (CCC) multivariate GARCH model of Bollerslev (1990) and the Dynamic Conditional Correlation (DCC) multivariate GARCH model of Engle (2002). The within and between DCC indicate low to high/extreme interdependence between the volatilities of companies' stock returns and the relevant stock market indexes or Brent oil prices