Marco Aurelio G.

I graduated in economics in 2016. Next year I began studying for my master's degree application exam. My objective was to learn statistics and finance. So I was accepted and started my master's degree in 2018. Unfortunately, the institution I picked did not have a focus on statistics and finance. During my last semester, my frustration and problems with my advisor left me somewhat unmotived to finish my dissertation. So I went my master's degree. Since my graduation days, I have studied coding. I always thought that having the ability to gather, process, and analyze large amounts of data would be valuable in today's world. My first coding experience was with MATLAB and C/C#. MATLAB, my brother used to work with it, and I used to fiddle around to learn how to code ( did nothing special ). C/C#, I did a two-week tutorial I found online. Although I never used these two program languages later, they helped teach me some concepts in programming. While in my graduation, I started using the program languages R and Python. R I used due to its extensive statistical packages support, and the second to its more general usability. During My master's degree, I found myself doing some paid R coding. Usually, it was simple data inference, like running some simple code to find the best linear regression model with variable permutation. Furthermore, I did have some coding experience with Python in helping some research groups in reading SQL databases and scraping online data. After I exited my master's degree, early 2020, I did freelance coding data analysis ( ex.: Inflation prediction models ), machine learning ( ex.: House prediction models in Rio de Janeiro ), and data scraping. I have a Github page with examples github.com/marcoaurelioguerrap ( Although still in Portuguese, my native language. ). If you need statistical models for prediction, inference, or data to be scraped, or organized I might be helpful. Other knowledge I have are Risk Management ( VaR, ES calculations ), stock and options market backtesting, options price models ( BS, Monte Carlo, and SABR ), test for forecast Bias, regressions ( OLS, Logit, probit, Tobit, panel data, time series, GMM, ARIMA, GARCH ). less