Testing the Markov property with ultra high frequency financial data

This paper develops a framework to test whether discrete-valued irregularly-spaced financial transactions data follow a subordinated Markov process. For that purpose, we consider a specific optional sampling in which a continuous-time Markov process is observed only when it crosses some discrete level. This framework is convenient for it accommodates not only the irregular spacing of transactions data, but also price discreteness. Further, it turns out that, under such an observation rule, the current price duration is independent of previous price durations given the current price realization. A simple nonparametric test then follows by examining whether this conditional independence property holds. Finally, we investigate whether or not bid-ask spreads follow Markov processes using transactions data from the New York Stock Exchange. The motivation lies on the fact that asymmetric information models of market microstructures predict that the Markov property does not hold for the bid-ask spread. The results are mixed in the sense that the Markov assumption is rejected for three out of the five stocks we have analyzed.

Modelo da dinâmica de um livro de ordens para aplicações em high-frequency trading

As operações de alta frequência (High-Frequency Trading - HFT) estão crescendo cada vez mais na BOVESPA (Bolsa de Valores de São Paulo), porém seu volume ainda se encontra muito atrás do volume de operações similares realizadas em outras bolsas de relevância internacional. Este trabalho pretende criar oportunidades para futuras aplicações e pesquisas nesta área. Visando aplicações práticas, este trabalho foca na aplicação de um modelo que rege a dinâmica do livro de ordens a dados do mercado brasileiro. Tal modelo é construído com base em informações do próprio livro de ordens, apenas. Depois de construído o modelo, o mesmo é utilizado em uma simulação de uma estratégia de arbitragem estatística de alta frequência. A base de dados utilizada para a realização deste trabalho é constituída pelas ordens lançadas na BOVESPA para a ação PETR4.

Forecasting daily volatility using high frequency financial data

Aiming at empirical findings, this work focuses on applying the HEAVY model for daily volatility with financial data from the Brazilian market. Quite similar to GARCH, this model seeks to harness high frequency data in order to achieve its objectives. Four variations of it were then implemented and their fit compared to GARCH equivalents, using metrics present in the literature. Results suggest that, in such a market, HEAVY does seem to specify daily volatility better, but not necessarily produces better predictions for it, what is, normally, the ultimate goal. The dataset used in this work consists of intraday trades of U.S. Dollar and Ibovespa future contracts from BM&FBovespa.

Industry analysis of the high frequency trading industry : an assessment of the industry boundaries, environment and strategic options

O Mercado Acionário Americano evoluiu rapidamente na última década. Este tornou-se uma arquitetura aberta em que participantes com tecnologia inovadora podem competir de forma eficaz. Várias mudanças regulatórias e inovações tecnológicas permitiram mudanças profundas na estrutura do mercado. Essas mudanças, junto com o desenvolvimento tecnológico de redes de alta velocidade, agiu como um catalisador, dando origem a uma nova forma de negociação, denominada Negociação em Alta Frequência (HFT). As empresas de HFT surgiram e se apropriaram em larga escala do negócio de formação de mercado, no fornecimento de liquidez. Embora HFT tem crescido massivamente, ao longo dos últimos quatro anos, HFT perdeu rentabilidade significativamente, uma vez que mais empresas aderiram ao setor reduzindo as margens. Portanto, diante deste contexto, esta tese buscou apresentar uma breve revisão sobre a atividade de HFT, seguida de uma análise dos limites deste setor, bem como, das características do macroambiente do HFT. Para tanto, a tese realizou uma extensa revisão do histórico literário, documentos públicos qualitativos, tais como, jornais, atas de reunião e relatórios oficiais. A tese empregou um ferramental de análise, Barreiras de Entrada e Mobilidade (Porter, 1980); Modelos de Evolução Setorial (McGahan, 2004); Estrutura do Setor de Informação Intensiva (Sampler, 1998), para analisar os limites do setor de HFT. Adicionalmente, empregou as ferramentas de análise, Modelos de Evolução Setorial (McGahan, 2004) e PESTEL (JOHNSON, SCHOLES, and WHITTINGTON, 2011), para analisar o setor e o contexto que envolve o negócio de HFT. A análise concluiu que as empresas que empregam HFT para atuar e competir no mercado acionário, compoem um setor independente.

Realized volatility: evidence from Brazil

Using intraday data for the most actively traded stocks on the São Paulo Stock Market (BOVESPA) index, this study considers two recently developed models from the literature on the estimation and prediction of realized volatility: the Heterogeneous Autoregressive Model of Realized Volatility (HAR-RV), developed by Corsi (2009), and the Mixed Data Sampling model (MIDAS-RV), developed by Ghysels et al. (2004). Using measurements to compare in-sample and out-of-sample forecasts, better results were obtained with the MIDAS-RV model for in-sample forecasts. For out-of-sample forecasts, however, there was no statistically signi cant di¤erence between the models. We also found evidence that the use of realized volatility induces distributions of standardized returns that are closer to normal

Conditional alphas and realized betas

This paper proposes a two-step procedure to back out the conditional alpha of a given stock using high-frequency data. We rst estimate the realized factor loadings of the stocks, and then retrieve their conditional alphas by estimating the conditional expectation of their risk-adjusted returns. We start with the underlying continuous-time stochastic process that governs the dynamics of every stock price and then derive the conditions under which we may consistently estimate the daily factor loadings and the resulting conditional alphas. We also contribute empiri-cally to the conditional CAPM literature by examining the main drivers of the conditional alphas of the S&P 100 index constituents from January 2001 to December 2008. In addition, to con rm whether these conditional alphas indeed relate to pricing errors, we assess the performance of both cross-sectional and time-series momentum strategies based on the conditional alpha estimates. The ndings are very promising in that these strategies not only seem to perform pretty well both in absolute and relative terms, but also exhibit virtually no systematic exposure to the usual risk factors (namely, market, size, value and momentum portfolios).

Price discovery using a regime-sensitive cointegration approach

This work proposes a method to examine variations in the cointegration relation between preferred and common stocks in the Brazilian stock market via Markovian regime switches. It aims on contributing for future works in "pairs trading" and, more specifically, to price discovery, given that, conditional on the state, the system is assumed stationary. This implies there exists a (conditional) moving average representation from which measures of "information share" (IS) could be extracted. For identification purposes, the Markov error correction model is estimated within a Bayesian MCMC framework. Inference and capability of detecting regime changes are shown using a Montecarlo experiment. I also highlight the necessity of modeling financial effects of high frequency data for reliable inference.