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Quantitative Finance Collector is a blog on Quantitative finance analysis, financial engineering methods in mathematical finance focusing on derivative pricing, quantitative trading and quantitative risk management. Random thoughts on financial markets and personal staff are posted at the sub personal blog.

Dec 12
A guest post from Anton Kwaijtaal: A CRO guide to deal with financial amnesia.

1. Don’t fear the risk of falling behind
Whether it is the risk of falling behind, peer group pressure or ill-defined incentive schemes, there exists a tendency to choose direction based on the illusion of control when there is actually too much uncertainty. Instead, questions should be asked as to whether decisions based on more or less unfounded assumptions should be made at all. Unfounded and inappropriate assumptions are dangerous because of at least two well-known biases. First, we tend to be over-confident in our ability to make financial and economic probability models. The second bias is our tendency to favour information that confirms our beliefs or hypotheses. This is called the confirmation bias. Moreover, by using hyperbolic discounting we reveal a strong tendency to make choices that are inconsistent over time. In other words, we make choices today that our future self would prefer not to make, despite using the same reasoning. Therefore, CRO’s and all other professionals should minimize their bold assumptions about how the economy works. We know much less than we think we know. Warren Buffet, the highly successful investor, sets strict restraints on using assumptions. He nevertheless makes above average profits.

2. Use real risk indicators
The volatility is wrong when you really need it. When reading this sentence most risk managers immediately think about skewness, kurtosis or perhaps about extreme losses. However, it is necessary to take it one step further. Most of the risk indicators, also in a regulatory context, are based on statistics. In most circumstances this is a second moment, named "variance" or "volatility". The volatility is however an affect heuristic driven indicator. It has no real correlation with the actual risk. The affect heuristic leads people to have a low perception of risk when we feel positive about the economy (and the other way around). However, during long periods of bull markets – driven by debt accumulation – actual risk (e.g. the probability of a deep debt crisis) increases, but our perception of risk reduces.

What you are really interested in is the consequence of market shocks when it actually goes terribly wrong. In this way you correlate risk with the probability of survival of your firm. The use of volatility is a good example of attribute substitution. A complex problem (what are the consequences of a serious meltdown) is replaced with a less complex problem (what is the observed volatility of the market over the last few months/years), at which point the answer to the less complex problem is seen as the solution to the original problem. Risk indicators should be correlated with actual risk, not with indicators such as (implied) volatility. A better risk indicator is the price to profit ratio of stocks, which reveals – in combination with debt levels – a lot about instability accumulating in an economy.
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Mar 30
This article is a guest post by Dr Timothy Johnson.

In the aftermath of the Credit Crisis it became popular to blame quants and mathematics for the Credit Crisis. In November, 2008, a former French prime minister, Michel Rocard, wrote in Le Monde that “mathematicians are guilty (unwittingly) of crimes against humanity”. More seriously, the following March, the UK’s financial regulator, the Financial Services Authority published the Turner Review on the causes and cures of the crisis where it identified one of the causes as a “misplaced reliance in sophisticated mathematics”. Wired wrote about The Formula That Killed Wall Street and the FT followed up on the Wired report.

As the dust settled, The Financial Crisis Inquiry Comission Report gave a more thoughtful analysis. They mentioned maths and quants, but only in passing. Their conclusion was that there had been a “systemic breakdown in accountability and ethics”, which had resulted in lax regulation and excessive borrowing.

In one respect the FCIC conclusions are positive for mathematicians, the Crisis wasn’t their fault. On the other hand, if the problems were rooted in ethics, then surely maths has no role in preventing future Crisis. Maths is just another tool, like a spread sheet or double entry bookkeeping. This is pretty depressing for the heirs of Newton, Euler, Riemann, Poincaré and Kolmogorov.

The mathematical study of probability is usually thought to have begun in the mid-sixteenth century, with Cardano’s Liber de Ludo Alea (‘Book on Games of Chance’), where there is the first explicit statement that the chance of rolling a six on a fair dice is 1 in 6. Shortly after making this statement, Cardano makes the perceptive observation that
These facts contribute a great deal to understanding but hardly anything to practical play.1

Cardano’s work was ignored for centuries, the problem was, despite Cardano’s status as a mathematician, his ‘Book on Games of Chance’ didn’t fit in to what modern mathematicians regard as proper mathematics. The fact is that Cardano did not see his work on probability as principally a mathematical work, but as an investigation of the ethics of gambling, a point made recently by the mathematician David Bellhouse2.
Mar 23
Quantitative trading is a myth for many people, a first impression most people have is it involves very sophisticated skills, a large sum of investing money, and several high capacity PCs, etc. Indeed it may not be as complicated as you think. Below are 10 Things the Public Need to Know About Quantitative Trading. Please feel free to leave a comment should you think there are other important things missing.

10 Things the Public Need to Know About Quantitative Trading

Guest posted by Caxton FX: a foreign exchange company that sets itself apart by offering excellent value for money and great customer service.
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Feb 13
Dr. Ernest P. Chan is an expert in the development and application of statistical models and software for trading currencies, futures, and stocks. He is the principal of QTS Capital Management, LLC., which manages a hedge fund as well as individual clients’ accounts. He also offers training to clients via workshops or individualized consulting to trade for themselves using Matlab. Dr. Ernest P. Chan is the author of the famous book "Quantitative Trading: How to Build Your Own Algorithmic Trading Business".

Ernest Chan quantitative trading

Tell us a little background info about yourself. Where are you from? What’s your education background?

I was born in Hong Kong, and I moved with my family to Toronto, Canada, when I was 17. I studied physics as an undergrad at U of Toronto, and received a Ph.D. in theoretical condensed matter physics from Cornell University. But after graduation, I never did any work in physics. I first worked as a researcher at IBM T. J. Watson Research Center’s Human Language Technologies group, where I designed statistical pattern recognition algorithms. Quite a few of my colleagues in that group moved on to become hugely successful algorithmic traders. (The current heads of Renaissance Technologies, Robert Mercer and Peter Brown, were both managers of that group.) After a few years, I too moved on to a career in finance, beginning at Morgan Stanley.

How long have you been as a quantitative trader? We know you had worked for a few big investment banks and hedge funds, what are the pros and cons of working as an independent traders and a manager of your own fund, instead of in a big firm?

Feb 7
Standing on the shoulders of giants allows us to see further, from now on we will invite experts to share with us their valuable experience and lessons.

It is our great pleasure to have Thijs van den Berg joining this week's interview session, Thijs is the manager of Sitmo B.V founded in 1998, which was initially a derivative market-making firm operating on the European Options Exchange (now Euronext), but soon building customized derivative models and risk management software development became an important activity. In 2003 Sitmo started consultancy services in Energy trading and quantitative modeling.

Tell us a little background info about yourself. Where are you from? What’s your education background?

I’m from The Netherlands. As long as I can remember I’ve been curious: math, physics. I got my first computer when I was 10 and things became magical: I had my personal desktop lab to experiment with! About that time my family decided to move to a sunny island. I had a great time windsurfing, surfing and skating, but education was a bit 2nd place. I went a year to a local Spanish school but didn’t speak much Spanish and so the only thing I could follow was the math classes. The second year I went to a British International school and that was very intense and good. Every morning sausages and beans etc. After that we moved back to The Netherlands, I skipped a school year, and eventually went to the Delft Technical University when I was 17 to study Computer Science. The first year was perfect -I was in the top 5-, but then I started to doubt my choices... I ended up working in a popular bar and was really enjoying that, ..until a professor knocked on my door and said he wanted to talk to me. He’s now a very good friend. After that I quickly finished university, did a thesis at a bank on forecasting with Wavelets.

Do you have any experience with quantitative finance? If yes, how long have you been in the quantitative finance industry and to what extent?

I ran into QF when I started trading (equity) derivates on the floor in the 90s. I’d build our own option pricing models and risk management tools, those were great times, we always had different prices than other traders, but we got it right... After that I got a job running a quant department at an energy Company. Energy trading was in its infancy: there was extremely much to do from a modeling perspective. The commodities have very complex dynamics, exotic assets, optimization, load forecasting, credit, data warehouses. We managed to get a couple of good PhD on board who delivered good models on fundamental activities. It was a true startup: when I joined the company the trade floor was just 6 people, when I left 250 with full blows specialized departments.
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