Blog Archives

Pricing of a financial product : A pricer of a call option.

March 4, 2013
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Pricing of a financial product : A pricer of a call option.

The financial market is not only made of stock options. Other financial products enable market actors to target specific aims. For example, an oil buyer like a flight company may want to cover the risk of increase in the price of oil. In this case it i...

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Temporal network model – Barabási-Albert model with the library igraph

February 17, 2013
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I found a golden website. The blog of Esteban Moro. He uses R to work on networks. In particular he has done a really nice code to make some great videos of networks. This post is purely a copy of his code. I just changed a few arguments to change colors and to do my own network.To...

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How to plot a network (package network) – Tip 2

December 12, 2012
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How to plot a network (package network) – Tip 2

As you have certainly seen now, I like working on artificial neural networks. I have written a few posts about models with neural networks (Models to generate networks, Want to win to Guess Who and Study of spatial segregation).Unfortunately, I missed ...

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Have you tried to understand your network? – Random generation of network models

December 4, 2012
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Have you tried to understand your network? – Random generation of network models

I already talked about networks a few times in this blog. In particular, I had this approach to explain spatial segregation in a city or to solve the Guess Who? problem. However, one of the question is how to generate a good network. Indeed, I aim to study strategy to split a network, but I...

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Function apply() – Tip 1

November 19, 2012
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Function apply() – Tip 1

The function apply() is certainly one of the most useful function. I was scared of it during a while and refused to use it. But it makes the code so much faster to write and so efficient that we can't afford not using it. If you are like me, that yo...

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Want to win "Guess who?" – Have an institutional neural network approach

November 15, 2012
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Want to win "Guess who?" – Have an institutional neural network approach

Have you ever played the board game "Guess who?". For those who have not experienced childhood (because it might be the only reason to ignore this board game), this is a game consisting in trying to guess who the opponent player is thinking o...

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How to choose your next holidays destination – Uniform distribution on a sphere

November 14, 2012
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How to choose your next holidays destination – Uniform distribution on a sphere

If you want to choose randomly your next holidays destination, you are likely to process in a way which is certainly biased. Especially if you choose randomly the latitude and the longitude. A bit like they do in this lovely advertising (For those of y...

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Generation of a normal distribution from "scratch" – The box-muller method

November 3, 2012
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Generation of a normal distribution from "scratch" – The box-muller method

My previous post is about a method to simulate a Brownian motion. A friend of mine emailed me yesterday to tell me that this is useless if we do not know how to simulate a normally distributed variable. My first remark is: use the rnorm() function if t...

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Generate stock option prices – How to simulate a Brownian motion

October 29, 2012
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Generate stock option prices  – How to simulate a Brownian motion

The Brownian motion is certainly the most famous stochastic process (a random variable evolving in the time). It has been the first way to model a stock option price (Louis Bachelier's thesis in 1900).The reason why is easy to understand, a Brownian mo...

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The consequence of merging insurance companies – Risk simulation and probability of ruin

October 17, 2012
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The consequence of merging insurance companies – Risk simulation and probability of ruin

The merge of two insurance companies enables to curb the probability of ruin by sharing the risk and the capital of the two companies. For example, we can consider two insurance companies, A and B. A is a well known insurance company with a big capital and is dealing with a risk with a low variance. We will assume...

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