I'm a backpacker sharing my journey into quantitative financial risks in the hopes that you don't have to suffer on your journey too.

Henrique Costa

In this article I want to update and improve an analysis I did some time ago, and published on LinkedIn, which can be accessed through this link: Credit Granting can be used as a proxy for the Financial Cycle, which tends to present a “boom” before the beginning of a recession.
<strong>
 Intro
</strong>
I will start with the definition of a cycle: a cycle is a low-frequency variation event.

Financial Cycle: Credit - Part I

<strong>
 Rambling (proxy for an introduction)
</strong>
We are experiencing the chaos of the information age, and its technological advents with voracious evolution and expansion. And in the midst of all this we have the advent of Big Data that has reached the delights of many with Data Science, Machine Learning and Artificial Intelligence. As a result, the idea of mathematical and statistical “models” became the rage, gaining visibility.

Trivia Series: Regression

Na publicação de hoje, quero trazer uma brincadeira que fiz comigo mesmo, um pequeno incentivo para voltar a publicar conteúdos sobre R (eu já estava enferrujado) entre outros assunto sobre economia. Passeando pelo instagram, encontrei um perfil que pública muita informação sobre economia em formato de visualização, o Economista Visual.

Reproduzindo gráfico do Economista Visual - Valor da cesta básica (PT-BR)

When it comes to job titles, data scientist is one of the biggest buzzwords in recent years. It is also one of the fastest growing fields of professional activity. Can an economist really be a data scientist? What skills are needed?

Can Economists Become Data Scientists?

In today’s post, I want to start a discussion about the use of mathematics and statistics in economics, and with this shocking sentence:  When a person studies economics for the first time, it is likely that they will not encounter “crazy” equations that go beyond basic mathematics.

The mathematization of Economics

<strong>
 Modeling Principles
</strong>
Almost all, if not all, applied science is based on the idea of a model. There are problems in the real world, and we would like to create a theory to solve such problems. One of the pillars or the main foundation of quantitative modeling is its effectiveness. But how can a model be effective? By demonstrating that it is simple, reproducible, and applicable in other areas of knowledge.

Trivia Series: Modelling foundations

Aqui voce pode encontrar a apresentação: Link da Apresentação  Aqui você se cadastra para participar do sorteio dos livros: Link para o Formulário  You can also reproduce this by choosing the city you want. Check out the complete code on my Github. Hey! 👋, did you find my work useful? Consider buying me a coffee ☕, by clicking here 👇🏻   Reuse   CC BY 4.0 Citation   For attribution, please cite this work as: Costa, Henrique. 2024.

Sorte e Apostas

I started this website some time ago with the aim of writing down the expansion of my knowledge and experience in the area of financial risk management. And also other things like programming with R, econometric modeling and a little about
<em>
 data science
</em>
and
<em>
 machine learning
</em>
. So I’m going to use this publication as a starting point, and start discussing texts and examples applied to financial risks.

A few words about risk management

Having decided to improve my skills in the short space of time left between my professional life and personal life, I started researching the available data science options.

For me, the choice was R or Python

I will present here some personal reflections on a concept that is considered very simple and perhaps common in economics, but which is labeled as the height of ignorance for making an assumption that cannot be observed in reality, but which in my view is a mistake. mistake.

The Ceteris Paribus Condition

Risk Taker!

[![](https://hcostax.com/blog/risk_intro/feature.jpg){.img-fluid}](feature.jpg){.lightbox
gallery="quarto-lightbox-gallery-1"}

I started this website some time ago with the aim of writing down the
expansion of my knowledge and experience in the area of financial risk
management. And also other things like programming with R, econometric
modeling and a little about *data science* and *machine learning*. So
I'm going to use this publication as a starting point, and start
discussing texts and examples applied to financial risks.

I am not going to give a general and complete description of what
financial risk is and its management processes, as this can be easily
found on sites like Wikipedia.

But risk, in the most basic sense, is the possibility that bad things
will happen. Humans have evolved to manage risks like wild animals do.
However, our awareness of risk is not always adequate for the modern
world.

Behavioral science shows that we rely too much on our instincts and
personal experience, as biases distort our thought processes.
Furthermore, even the way we frame risk decisions irrationally
influences our willingness to take risks.

Yet surprisingly sophisticated examples of risk management can be seen
early in history. In ancient times, merchants and their creditors shared
the risk by tying loan repayment to the safe arrival of shipments
through sea loans (i.e. combining loans with a type of insurance). [We
can see some references
here](https://risk-engineering.org/concept/history-of-insurance) and
[here too.](https://core.ac.uk/download/pdf/289933444.pdf)

The insurance contract separated from the loan contract as early as the
14th century in northern Italy, creating the first autonomous financial
risk transfer instrument. From the 17th century onwards, a more
methodical approach to the mathematics of risk can be traced. This was
followed by the development of exchange-based risk transfer, in the form
of agricultural futures contracts, in the 18th and 19th centuries. [Some
other references
here](https://www.jstor.org/stable/10.1086/ahr.111.2.307).

This methodical approach continued to evolve in the 20th century and
beyond, with major advances in financial theory in the 1950s; an
explosion in risk management markets from the 1970s onwards; and the
emergence of new instruments, such as cyber risk insurance, at the
beginning of the 21st century. **Risk management is an ancient art, but
a young science -- and an even younger profession**.

The way we think about risk is the biggest determinant of whether we
recognize risks, assess them appropriately, measure them using
appropriate risk metrics and manage them.

In the next publications I will bring introductory content that will
analyze risk definitions, the classic risk management process, the main
types of risk and the tools used to track risks and make decisions.

Most risk management disasters are caused by the failure to recognize
and/or adequately address one or more of these fundamental elements, not
by the failure of some sophisticated risk management technique.
Century-old financial institutions have gone bankrupt because their risk
management procedures ignored a certain type of risk, misunderstood the
links between risks, or did not follow the classic steps of the risk
management process. As we recently observed the failure of some
financial institutions, both in Brazil and around the world.

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Hey! 👋, did you find my work useful? Consider buying me a coffee ☕, by
clicking here 👇🏻
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[![](https://img.buymeacoffee.com/button-api/?text=Buy%20me%20a%20coffee&emoji=&slug%20=hcostax&button_colour=5F7FFF&font_colour=ffffff&font_family=Cookie&outline_colour=000000&coffee_colour=FFDD00)](https://www.buymeacoffee.com/hcostax)
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For attribution, please cite this work as:
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Costa, Henrique. 2023. "A Few Words about Risk Management." August 27,
2023. <https://doi.org/10.59350/51paw-dza08>.
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