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You have already agreed to pay $QK$ EUR at $T$ to receive $Q$ units of A. If you sell $Q$ lots of $F^A(t,T)$ then you will receive $Q F^A(t,T)$ EUR and deliver $Q$ units of A. The combined flow is now just in EUR: at $T$ you receive a net of $Q(F^A(t,T)-K)$ EUR. You can hedge that by selling $Q(F^A(t,T)-K)$ of $F^{FX}(t,T).$ Then with both hedges, the net ...


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Machine learning is a very wide field. Most often it is used for classification or regression tasks when you have labelled data to train the model. For example you show thousands of labeled pictures with an apple and computer "learns" what set of features gives high probability that picture contains an apple (for example, round, red etc). Now in your case ...


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QSTK is nice and open source , it is the QuantSciTookKit and it has some good functionality if you are interested in python programming. Here is the link: http://wiki.quantsoftware.org/index.php?title=QuantSoftware_ToolKit


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I really like Philip P's work, but frankly I do not believe this paper is his best one. It is understandable you do not catch how to use it: there is no dataset in the paper, and the orders of magnitude of $\sigma dW$ and $\delta_t x$ are so different. My suggestions: some components are missing, $x$ should be a point process, for instance an Hawkes ...


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I cannot suggest some reference particularly, since the field is going to develop day by day, but, generally, you could take a look to: Engelmann, Bernd, and Robert Rauhmeier, eds. The Basel II risk parameters: estimation, validation, and stress testing. Springer Science & Business Media, 2006. Particularly, look at the chapter 4 and 5; the ...


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If you take the a sample of historical asset returns as model for the risk then you can do two things: You calculate $r_j = \sum_{i=1}^n w_i r_i^j$ thus for each scenario $j$ you aggregate the individual asset returns to get a scenario for the portfolio. Then you can calculate $Var(r_j)$ the variance of the sample of portfolio returns. This is the same as ...


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if you take the variance of a single asset it scales as a quadratic, $$ var(\lambda X) = \lambda^2 var(X) $$ so it's not surprising that the general case gives a quadratic form.


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Try to give David Spiegelhalter a read/listen to David Spiegelhalter's work and research. He is a statistician and a Professor of the Public Understanding of Risk at Cambridge England. Rather than new ways of calculating risk, he looks at ways of communicating risk to a general public that doesn't have any knowledge of stats. I Linked an interesting ...


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The retail credit risk management is generally based on models that try to discriminate between good (people that probably will be able to pay back the debt) and bad customers (people that probably will not). Particularly, as the question explicitly asks for, you want to some references to allow to decide which customers, already acquired, to keep and which ...



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