Via TP (Techcrunch) : Today, Silicon Valley is the hottest place for quants to be – though people with this skill set are often referred to now as data scientists. A similar confluence of factors — data, technology and algorithms — has combined to enable a new class of transformational opportunities. These opportunities are not limited to just financial services; they are showing up in every sector of the economy.

The following document contains a brief summary of the paper titled, “Autoregressive Conditional Duration - A New Model for Irregularly Spaced Transaction Data” by Engle and Russell. ACD-Summary Takeaway : The paper models the duration between transactions.With the ease of availability of HF data, there needs to be a model that captures irregularly spaced timestamps. It is obvious that neither a standard Poisson process nor a non-homogeneous Poisson process is going to be a good fit.

Gregory LaBerta, a Special Agent at FBI has come up with a 35 page document that gives a detailed description of the trades executed by Navinder Singh Sarao, who is accused of playing a major role in the “Flash crash” on May 6, 2010. I am surprised with this new finding as it comes out of nowhere and that too after 5 looong years. For the last 5 years, there has been a debate; the majority view being that the whole HFT community played a big role in the “Flash crash”.

The paper, written by Yosihiko Ogata, titled, ``On Lewis Simulation Method for Point Process", gives a detailed procedure to simulate univariate and multivariate point processes. The following document contains the algo and necessary R code for simulating univariate Hawkes' self exciting process. The good thing about this algo is that is based on “thinning” and hence it is not a computationally expensive task. Simulation of univariate Hawkes via Thinning

T.Ozaki’s paper titled, “Maximum likelihood estimation of Hawkes' self-exciting point processes”, deals with the univariate Hawkes’ process. The paper gives a detailed method to obtain the ML estimates of the process. In order to verify the ML estimates, the author simulates the process and compares the true vs. estimated parameters. I found a typo in one of the expressions for the gradient. In any case, R provides nlm function, that computes the gradient and hessian numerically.