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followers: 35.0 following: 31.0 repos: 49.0 gists: 2.0

Name: Carlos Trucios Maza

Type: User

Company: University of Campinas

Bio: Assistant Professor @IMECC/UNICAMP

Location: Brazil

Blog: ctruciosm.github.io

Hi everyone 👋

I am Assistant Professor at University of Campinas (Department of Statistics) - Brazil. About myself...

I am a peruvian based in Brazil since 2010.
I hold a PhD in Statistics from University of Campinas (UNICAMP) - Brazil.
I usually code in R, sometimes in Matlab and occasionally in Python.
I am curious about Julia Lang
I am always interested in new statistical/econometrics methods and open for collaborative work.
I really like Time Series Analysis and its applications in Economics and Finance (thanks Luiz Hotta). You can see more about my research interests as well as my list of publications here.
I strongly believe that education changes our lives.

I am currently working on high-dimensional time series (specifically on General Dynamic Factor Models -- GDFM), risk measures forecasting procedures, machine learning porfolio allocation strategies and a little bit in text mining.

Feel free to contact me

Carlos Trucios Maza's Projects

23semest

aca124

Material de apoio para a matéria Métodos Quantitativos em Administração (ACA124) FACC/UFRJ

academic-website-template

Jekyll website template for personal academic or research group web pages.

arbitrage_research_hudson_thames

basicstats

bayesian-stats-with-r

Material for a workshop on Bayesian stats with R

binancer

R client to Binance API

brazilianhrp

Portfolio Allocation Strategies Performance. BDAQ UFRJ Project

computational-finance-course

Here you will find materials for the course of Computational Finance

credit-risk-anaytics-the-r-companion

The rep contains the R files from Credit Risk Analytics: the R Companion, from H Scheule, D Rosch an B Baesens. Unfortunately, when I bought the book, I could not find the R files accompanying the book. So I decided to create the R files from the snippets of codes in the book. Hope you find the files useful. I also think this is an excellent book for those with an experience in both credt risk models and programming in R. If instead you want to learn R or credit risk, this is not the best book for you.