918 resultados para Stock Index Futures


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Este artigo apresenta a utilização de Beta e variação de Beta dos ativos pertencentes ao Ibovespa como novos critérios para a construção de carteiras vencedoras e perdedoras em estratégias de momento. Os resultados mostram que as estratégias de momento, com base em critérios de maior ou menor Beta e de variação de Beta dos ativos pertencentes ao Ibovespa, geram retornos positivos ao longo de períodos subsequentes de 6 meses e 12 meses, porém apontam que estas estratégias, quando aplicadas e renovadas a cada mudança do principal índice bursátil brasileiro, apresentaram-se menos rentáveis do que as estratégias habituais baseadas no retorno total dos ativos no período entre 1995 a 2013.

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Neste trabalho, propomos uma especificação de modelo econométrico na forma reduzida, estimado por mínimos quadrados ordinários (MQO) e baseado em variáveis macroeconômicas, com o objetivo de explicar os retornos trimestrais do índice de ações IBRX-100, entre 2001 e 2015. Testamos ainda a eficiência preditiva do modelo e concluímos que o erro de previsão estimado em janela móvel, com re-estimação de MQO a cada rodada, e utilização de VAR auxiliar para projeção dos regressores, é significativamente inferior ao erro de previsão associado à hipótese de Random Walk para o horizonte de previsão de um trimestre a frente.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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This paper examines the measurement of long-horizon abnormal performance when stock selection is conditional on an extended period of past survival. Filtering on survival results in a sample driven towards more-established, frequently traded stocks and this has implications for the choice of benchmark used in performance measurement (especially in the presence of the well-documented size effect). A simulation study is conducted to document the properties of commonly employed performance measures conditional on past survival. The results suggest that the popular index benchmarks used in long-horizon event studies are severely biased and yield test statistics that are badly misspecified. In contrast, a matched-stock benchmark based on size and industry performs consistently well. Also, an eligible-stock index designed to mitigate the influence of the size effect proves effective.

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This study focuses on: (i) the responsiveness of the U.S. financial sector stock indices to foreign exchange (FX) and interest rate changes; and, (ii) the extent to which good model specification can enhance the forecasts from the associated models. Three models are considered. Only the error-correction model (ECM) generated efficient and consistent coefficient estimates. Furthermore, a simple zero lag model in differences which is clearly mis-specified, generated forecasts that are better than those of the ECM, even if the ECM depicts relationships that are more consistent with economic theory. In brief, FX and interest rate changes do not impact on the return-generating process of the stock indices in any substantial way. Most of the variation in the sector stock indices is associated with past variation in the indices themselves and variation in the market-wide stock index. These results have important implications for financial and economic policies.

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In this dissertation, I investigate three related topics on asset pricing: the consumption-based asset pricing under long-run risks and fat tails, the pricing of VIX (CBOE Volatility Index) options and the market price of risk embedded in stock returns and stock options. These three topics are fully explored in Chapter II through IV. Chapter V summarizes the main conclusions. In Chapter II, I explore the effects of fat tails on the equilibrium implications of the long run risks model of asset pricing by introducing innovations with dampened power law to consumption and dividends growth processes. I estimate the structural parameters of the proposed model by maximum likelihood. I find that the stochastic volatility model with fat tails can, without resorting to high risk aversion, generate implied risk premium, expected risk free rate and their volatilities comparable to the magnitudes observed in data. In Chapter III, I examine the pricing performance of VIX option models. The contention that simpler-is-better is supported by the empirical evidence using actual VIX option market data. I find that no model has small pricing errors over the entire range of strike prices and times to expiration. In general, Whaley’s Black-like option model produces the best overall results, supporting the simpler-is-better contention. However, the Whaley model does under/overprice out-of-the-money call/put VIX options, which is contrary to the behavior of stock index option pricing models. In Chapter IV, I explore risk pricing through a model of time-changed Lvy processes based on the joint evidence from individual stock options and underlying stocks. I specify a pricing kernel that prices idiosyncratic and systematic risks. This approach to examining risk premia on stocks deviates from existing studies. The empirical results show that the market pays positive premia for idiosyncratic and market jump-diffusion risk, and idiosyncratic volatility risk. However, there is no consensus on the premium for market volatility risk. It can be positive or negative. The positive premium on idiosyncratic risk runs contrary to the implications of traditional capital asset pricing theory.

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In this dissertation, I investigate three related topics on asset pricing: the consumption-based asset pricing under long-run risks and fat tails, the pricing of VIX (CBOE Volatility Index) options and the market price of risk embedded in stock returns and stock options. These three topics are fully explored in Chapter II through IV. Chapter V summarizes the main conclusions. In Chapter II, I explore the effects of fat tails on the equilibrium implications of the long run risks model of asset pricing by introducing innovations with dampened power law to consumption and dividends growth processes. I estimate the structural parameters of the proposed model by maximum likelihood. I find that the stochastic volatility model with fat tails can, without resorting to high risk aversion, generate implied risk premium, expected risk free rate and their volatilities comparable to the magnitudes observed in data. In Chapter III, I examine the pricing performance of VIX option models. The contention that simpler-is-better is supported by the empirical evidence using actual VIX option market data. I find that no model has small pricing errors over the entire range of strike prices and times to expiration. In general, Whaley’s Black-like option model produces the best overall results, supporting the simpler-is-better contention. However, the Whaley model does under/overprice out-of-the-money call/put VIX options, which is contrary to the behavior of stock index option pricing models. In Chapter IV, I explore risk pricing through a model of time-changed Lévy processes based on the joint evidence from individual stock options and underlying stocks. I specify a pricing kernel that prices idiosyncratic and systematic risks. This approach to examining risk premia on stocks deviates from existing studies. The empirical results show that the market pays positive premia for idiosyncratic and market jump-diffusion risk, and idiosyncratic volatility risk. However, there is no consensus on the premium for market volatility risk. It can be positive or negative. The positive premium on idiosyncratic risk runs contrary to the implications of traditional capital asset pricing theory.

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This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

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We estimate the shape of the distribution of stock prices using data from options on the underlying asset, and test whether this distribution is distorted in a systematic manner each time a particular news event occurs. In particular we look at the response of the FTSE100 index to market wide announcements of key macroeconomic indicators and policy variables. We show that the whole distribution of stock prices can be distorted on an event day. The shift in distributional shape happens whether the event is characterized as an announcement occurrence or as a measured surprise. We find that larger surprises have proportionately greater impact, and that higher moments are more sensitive to events however characterised.

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Purpose – On 29 January 2001, Euronext LIFFE introduced single security futures contracts on a range of global companies. The purpose of this paper is to examine the impact that the introduction of these futures contracts had on the behaviour of opening and closing UK equity returns. Design/methodology/approach – The paper models the price discovery process using the Amihud and Mendelson partial adjustment model which can be estimated using a Kalman filter. Findings – Empirical results show that during the pre-futures period both opening and closing returns under-react to new information. After the introduction of futures contracts opening returns over-react. A rise in the partial adjustment coefficient also takes place for closing returns but this is not large enough to cause over-reaction. Originality/value – This is the first study to examine the impact of a single security futures contract on the speed of spot market price discovery.