# Tag Info

5

It looks as if you are actually asking the following: given a short rate model, how does the HJM volatility function look like. If your short rate model has an analytic bond price formula (many do have this, because this makes them "pratical") then you get the instanteous forward rate from the bonds and via Ito the HJM process and the HJM volatility. ...

5

Let's first restate the formula of the beta of a portfolio $P$ relative to a benchmark $B$: $$\beta_P=\frac{Cov(r_P,r_B)}{Var(r_B)}$$ As chrisaycock said in his comment, the key thing to understand is that the beta is a statistical measure computed relative to a benchmark. Hence, I believe that the real question you should be asking is: Which benchmark ...

5

Yes, there is a software application that you can purchase for \$39.99 which stores all your tick data in a highly compressed format while still allowing maximum throughput and lowest latency data queries that I have ever seen. The package provides APIs to all languages under the sun but because they have a special sale going on it comes with the complete ...

4

You question is quite strange: so you do not want to use methods inspired by bioinfo and genetics (neural networks, GA, geometry of folding, etc) but methods that are used in these fields? In terms of modeling, the problematics in bioinfo and genetics are mainly: tree or graph matching (to build metrics in the space of molecules), like in SIGMA: a ...

3

From an academic viewpoint you do not have a lot of choices: The Rosenbaum-Robert approach, the price model with uncertainty zones is a model of trades and duration between trades (implicitly). It is worthwhile to try it. You can also use an Hawkes process, it will have the nice effect of capturing clustering effects on trades. if you want to use ...

3

You could try using the Gaussian Affine Term Structure Models (GATSM), with the right boundary conditions to stop rates being negative (in the style of their Black implementation). See, for example, Monika Piazzesi, the "Affine Term Structure Models" if you want to enter/modify the basis or the work of Krippner, for example "Measuring the stance of monetary ...

3

1) The probability of a H or T of any next coin toss (fair coin) is always 0.5 because coin tosses are independent of each other. 2) Stock markets, or for that matter any asset, are an entirely different game. First of all the expectancy is not 0.5 of, for example, experiencing an up or down day tomorrow in a stock simply because the distribution is ...

2

The OIS rate is simply the price of an interest rate swap where you're swapping the fed funds rate (paid daily) for a fixed rate. For example if a 3 month OIS pays 2%, and the fed funds rate is currently 0.5% then what we're saying is you can enter into a swap (at zero price) where you will receive 2% and pay fed funds. If the fed funds rate goes up you pay ...

2

You must use the same scaling parameters out of sample as you did during training. Do otherwise will give you incorrect results. If the distribution of your out of sample observations different significantly from your training data then you need to address that by gathering more training data.

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To quickly answer and address your first question. ARMA - Fractionally integrated GARCH or FIGARCH is one of the more common methods used at higher frequencies, it handles some properties required for higher frequency that standard ARMA-GARCH does not There are also a few other so called long memory volatility models, and there are other models which i ...

2

Well, typically in the process of coming up with a model you are supposed to understand the assumptions that you're making and the circumstances(preferably quantifiable) under which your assumptions will hold/break. No model is infallible and it is how well the assumptions are stated and understood that will determine if your model is acceptable. I can't ...

1

I think most models failed in the 2008 crisis. Historical simulation and e.g. a value-at-risk calculated from it is designed for normal to "medium" market behavior. To account for crisis scenarios stress tests should be in place. This what is done e.g. in the USCITS framework. However, after the crisis your model should keep this history "in mind".

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it was meant to be a comment, so please don't treat it like an answer, just suggestion. I think every department has its own standards. and if you want to constitute your model somehow, then you can just compute R^2 between real and fitted values, RMSD, information capacity criteria (AIC/BIC) or you can use any from tens other measures. you can also state ...

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This is an incredibly broad question, there are tons of different schools of thought, and each housing market reacts differently to various different unions of fundamentals. Also, the type of housing market makes a huge difference, single detached housing vs. multi story apartment complexes,...Every investment bank's research dept. applies different set of ...

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There is a market accepted standard to translate vanilla option prices to implied vols and backward which is the Black Scholes (BS) options pricing formula. There is no ambiguity here, everyone knows of the deficiencies of BS yet its what people use to translate between iVols <-> prices. The numerical difficulty I see is to make more realistic ...

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That depends on your application, obviously. If you intend to run Matlab or Python on a single machine, and you're looking into which graphics card to buy, multiplication vs addition should not matter much. I that situation I would recommend an Nvidia card which features CUDA. For CUDA, there are lot of libraries available which make it easy to adapt ...

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The Systematic Investor has a series of articles on using PCA and clustering to improve on traditional Risk Parity approaches. The series of posts start here: http://systematicinvestor.wordpress.com/2012/12/22/visualizing-principal-components/

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As best I can tell, the primary difference between traditional approaches (be they classical or Bayesian) and the newer "predictive analytics" is that the traditional approaches make a few explicit (and testable) assumptions, and then give you quantified estimates of your potential errors. The newer methods exchange a chance at more sophisticated (and ...

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Increased volatility towards the event start is definitely from increased order flow. There are some papers specifically on "prediction markets", the ones with practical applications are on market making which I suspect is generally a loss-making operation conducted by the exchanges themselves when a market is opened. Given the short-time periods and small ...

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