Bayesian statistical analysis of daily returns runs in Brazilian stock exchange

In the analysis of financial market data, random variations in price move-ments can share non-trivial statistical properties as observed for the distri-butions of returns, with possibility of absence of autocorrelations in asset returns, volatility in blocks and presence of asymmetry between rises and falls of stock values. Asset price fluctuations are usually investigated using time series models to obtain inferences about interest rates and future fore-casts. In this study we consider the use of existing probability distributions to model run lengths and absolute historical price run returns as an alterna-tive to the use of usual time series models with applications for time series obtained from the NYSE stock exchange for three private banks located in the Brazil in the period from July 19, 2013, to July 19, 2018. We assume discrete Weibull distributions as an alternative to the exponential law com-monly used in this type of analysis. Under this modeling approach it is possible to obtain information about the structure of the market such as the probability of the stock market rising and falling daily and the magnitude of the returns.