Nicholas Constantine Metropolis This is a great write up on the nuts and bolts of Metropolis Hastings Algorithm. I like such papers that summarize everything about an algorithm with a sound balance of rigor and simplicity. Nowadays even for a simple statistical analysis, one tends to specifying a BUGS model and run MCMC. With BUGS software, Bayes analysis has become accessible to a whole lot of data analysts. The heart of BUGS is the Gibbs sampling algorithm, which is a special case of Metropolis Hastings Algorithm.

Most of us would have come across Occam’s razor principle in the context of variable selection, the essence of which is, “parsimony wins”. However not many would have heard about “Occam’s window” that is relevant in the context of Model selection, i.e. choosing a set of models out of an ocean of potential models. In the stats literature, Occam’s window appears under Bayesian Model Selection. In this post, I will try to summarize some of the main points from this fantastic paper by Adrian Raftery.

Link : The Journal of Finance( Sep, 1995 ) As early as 1997, the US financial markets comprised blue chip stocks traded by specialists at NYSE , other stocks traded at NASDAQ by specialists and a small scale electronic system. Fast forward to 2012, the US market comprises 40 trading destinations. There are four public exchanges – NYSE, NASDAQ, Direct Edge and BATS. Inside each of these exchanges there are various destinations.

Via : Journal of Financial Economics (May 1996) Usually the first multivariate time series model that one comes across is a VAR model. It is a logical progression from modeling a univariate ARMA process. Most of the textbooks that introduce VAR start off with the Standard VAR and then go at length in to procedures such as estimating the parameters, hypothesis testing for the number of lags to consider, innovation accounting topics such as Impulse Response Decomposition, Forecast error variance decomposition.

This paper by Hasbrouck is about estimating trading costs from transaction prices. One of the classic models used for estimating trading costs is the Roll model. For a plain version of Roll model where the price increments are modeled in a univariate sense, an estimate for the costs is given by a formula that involves square root of negative auto correlation. In cases where there is a positive autocorrelation between the transaction prices, the formula loses its power.