Marc Bojoly posts

In the first part of this series, I have introduced why Hadoop framework could be useful to compute the VAR and analyze intermediate values. In that second part I will give you a first concrete implementation of the VAR calculation with Hadoop.

After introducing the Value at Risk in my first article, I have implemented it using GridGain in my second article. I conclude in this latter that relatively good performances have been reached through some optimizations. One of them was based on the hypothesis that the intermediate results - the prices for each draw - can be discarded. However, it is not always the case. Keeping the generated parameters and the call price for each draw can be very useful for business in order to analyze…

After a first published article introducing the Value at Risk interest and calculation on a Grid, we will now explore the practical implementation by using a grid computing middleware. I have chosen GridGain, an open source grid middleware which implements the map/reduce pattern (see previous article). Firstly, I will give an overview of the Value at Risk implementation independently of the grid computing architecture. Then I will describe the GridGain middleware and the classes that have to be implemented to take advantage of the grid.…

Risk management is today a strategic objective for financial institutions such as, but not limited to, investment banking and insurance companies. Such risk evaluation uses advanced mathematical theory and requires a lot of data computing. In this article, we will introduce basic concepts of Value At Risk estimation in order to show which kind of calculation is required. We will introduce grid computing in order to show how such architecture can help financial companies producing quickly these very important VAR figures. In later articles, we…