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.

Nonlocal office investors: anchored by their markets and impaired by their distance

Nonlocal investors purchase and sell investment property in a distant metropolitan area. In this study, we identify capital value underperformance for nonlocal investors on both sides of the transaction, when they purchase and when they sell. The commercial real estate transactions data include a national sample of office property occurring in more than 100 U.S. markets. Using propensity-score matched sample to control for selection bias, we find that nonlocal investors overpay on the purchase by an estimated 13.8 % and sell at an estimated 7 % discount. These disadvantages relative to local investors expand with the geographic distance separating investor and asset. Nonlocal investors fundamentally overvalue similar assets sold to each other relative to assets transacted between locals, and are less patient as sellers. The positive bias in overpayment is directly tied to office rent differentials between the asset and investor markets.

A family of autoregressive conditional duration models

This paper develops a family of autoregressive conditional duration (ACD) models that encompasses most specifications in the literature. The nesting relies on a Box-Cox transformation with shape parameter λ to the conditional duration process and a possibly asymmetric shocks impact curve. We establish conditions for the existence of higher-order moments, strict stationarity, geometric ergodicity and β-mixing property with exponential decay. We next derive moment recursion relations and the autocovariance function of the power λ of the duration process. Finally, we assess the practical usefulness of our family of ACD models using NYSE transactions data, with special attention to IBM price durations. The results warrant the extra flexibility provided either by the Box-Cox transformation or by the asymmetric response to shocks.

Are price limits on futures markets that cool?: evidence from the Brazilian Mercantile and Futures Exchange

This paper investigates the impact of price limits on the Brazilian futures markets using high frequency data. The aim is to identify whether there is a cool-off or a magnet effect. For that purpose, we examine a tick-by-tick data set that includes all contracts on the S˜ao Paulo stock index futures traded on the Brazilian Mercantile and Futures Exchange from January 1997 to December 1999. The results indicate that the conditional mean features a floor cool-off effect, whereas the conditional variance significantly increases as the price approaches the upper limit. We then build a trading strategy that accounts for the cool-off effect in the conditional mean so as to demonstrate that the latter has not only statistical, but also economic significance. The in-sample Sharpe ratio indeed is way superior to the buy-and-hold benchmarks we consider, whereas out-of-sample results evince similar performances.

Price discovery in dual-class shares across multiple markets

We extend the standard price discovery analysis to estimate the information share of dual-class shares across domestic and foreign markets. By examining both common and preferred shares, we aim to extract information not only about the fundamental value of the rm, but also about the dual-class premium. In particular, our interest lies on the price discovery mechanism regulating the prices of common and preferred shares in the BM&FBovespa as well as the prices of their ADR counterparts in the NYSE and in the Arca platform. However, in the presence of contemporaneous correlation between the innovations, the standard information share measure depends heavily on the ordering we attribute to prices in the system. To remain agnostic about which are the leading share class and market, one could for instance compute some weighted average information share across all possible orderings. This is extremely inconvenient given that we are dealing with 2 share prices in Brazil, 4 share prices in the US, plus the exchange rate (and hence over 5,000 permutations!). We thus develop a novel methodology to carry out price discovery analyses that does not impose any ex-ante assumption about which share class or trading platform conveys more information about shocks in the fundamental price. As such, our procedure yields a single measure of information share, which is invariant to the ordering of the variables in the system. Simulations of a simple market microstructure model show that our information share estimator works pretty well in practice. We then employ transactions data to study price discovery in two dual-class Brazilian stocks and their ADRs. We uncover two interesting ndings. First, the foreign market is at least as informative as the home market. Second, shocks in the dual-class premium entail a permanent e ect in normal times, but transitory in periods of nancial distress. We argue that the latter is consistent with the expropriation of preferred shareholders as a class.

Profundidade de mercado na BM&FBovespa: um modelo de alta frequência para estimação da profundidade de mercado da BM&FBovespa

O objetivo desse trabalho é encontrar uma medida dinâmica de liquidez de ações brasileiras, chamada VNET. Foram utilizados dados de alta frequência para criar um modelo capaz de medir o excesso de compras e vendas associadas a um movimento de preços. Ao variar no tempo, o VNET pode ser entendido como a variação da proporção de agentes informados em um modelo de informação assimétrica. Uma vez estimado, ele pode ser utilizado para prever mudanças na liquidez de uma ação. O VNET tem implicações práticas importantes, podendo ser utilizado por operadores como uma medida estocástica para identificar quais seriam os melhores momentos para operar. Gerentes de risco também podem estimar a deterioração de preço esperada ao se liquidar uma posição, sendo possível analisar suas diversas opções, servindo de base para otimização da execução. Na construção do trabalho encontramos as durações de preço de cada ação e as diversas medidas associadas a elas. Com base nos dados observa-se que a profundidade varia com ágio de compra e venda, com o volume negociado, com o numero de negócios, com a duração de preços condicional e com o seu erro de previsão. Os resíduos da regressão de VNET se mostraram bem comportados o que corrobora a hipótese de que o modelo foi bem especificado. Para estimar a curva de reação do mercado, variamos os intervalos de preço usados na definição das durações.

Agency problems and capital expenditure announcements

This article examines the market valuation of announcements of new capital expenditure. Prior research suggests that the firm's growth opportunities and cash flow position condition the market response. This study jointly examines the role of growth and cash flow, and the interaction between them. Using a new data set of Australian firms that avoids problems associated with expectations models, the results are remarkably strong and support a positive association between growth opportunities and the market valuation, in addition to supporting the role of free cash flow. The findings have implications for the relationship between general investment information and stock prices.

Tests of non-linearity using LIFFE futures transactions price data

This paper presents and implements a number of tests for non-linear dependence and a test for chaos using transactions prices on three LIFFE futures contracts: the Short Sterling interest rate contract, the Long Gilt government bond contract, and the FTSE 100 stock index futures contract. While previous studies of high frequency futures market data use only those transactions which involve a price change, we use all of the transaction prices on these contracts whether they involve a price change or not. Our results indicate irrefutable evidence of non-linearity in two of the three contracts, although we find no evidence of a chaotic process in any of the series. We are also able to provide some indications of the effect of the duration of the trading day on the degree of non-linearity of the underlying contract. The trading day for the Long Gilt contract was extended in August 1994, and prior to this date there is no evidence of any structure in the return series. However, after the extension of the trading day we do find evidence of a non-linear return structure.

Assessing influence in survival data with a cure fraction and covariates

Diagnostic methods have been an important tool in regression analysis to detect anomalies, such as departures from error assumptions and the presence of outliers and influential observations with the fitted models. Assuming censored data, we considered a classical analysis and Bayesian analysis assuming no informative priors for the parameters of the model with a cure fraction. A Bayesian approach was considered by using Markov Chain Monte Carlo Methods with Metropolis-Hasting algorithms steps to obtain the posterior summaries of interest. Some influence methods, such as the local influence, total local influence of an individual, local influence on predictions and generalized leverage were derived, analyzed and discussed in survival data with a cure fraction and covariates. The relevance of the approach was illustrated with a real data set, where it is shown that, by removing the most influential observations, the decision about which model best fits the data is changed.