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The best paper is probably Relative Volume as a Doubly Stochastic Binomial Point Process - James Mcculloch. In this paper the volume is modelled via a Point Process, and theoretical laws are derived (with confident intervals, etc). And if you can wait few days (it will be available very soon), we put elements about this in Market Microstructure in Practice, ...

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I have honestly not come across a good book (or good enough review to make me buy the book) on Fund Transfer Pricing. While it is not my career focus, I had to familiarize myself a bit with the topic because of certain requirements involving funding trading operations and the performance of funding specific operations. Personally I would recommend the ...

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You could read it like this: The typical change in equity value is equal to the typical change in asset value, adjusted for the probability of the assets surviving. Note that the formula is not specific to Merton models, it's also true for regular options and their underlyings. It's just that volatility of option prices isn't typically a concern in ...

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Well, the main intuition of the Merton model is that a company's equity can be treated as a call option on its assets, thus allowing for the application of Black-Scholes option pricing methods. Let's consider a company that has assets $A_{t}$ financed by equity $E_{t}$ and a zero-coupon debt $B_{t}$ with face value K, and maturity T. At time of maturity T, ...

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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 ...

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I deal recently with some analysis of the Volume time series, daily volume in € for European stocks. I found out that an ARIMA model works well. But, some EWMA could also provide good forecast if it's well parameterized. You can also face some seasonality effect due to macroeconomic events, some you may need to clean you data and treat these days in a ...

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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 ...

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Estimation of the initial states of R and particularly Q is indeed more of an art than science. The task at hand is to estimate the covariances. You have basically two main choices: Live with the fact that you will never be able to exactly pinpoint the covariance of noise in financial time series. The most often used approach is to pose the coveriance ...

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My answer will be very non-quantitative but the resulting models are actually quite mathematical but I like to stick to a general overview because of the proprietary nature of those models. Here couple thoughts though: You can't just try to explain market moves by a few indicators or a single Fed speech (by the way, the market hugely misread those ...

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The equation stated in the question is not at the core of Merton's credit model, (Not saying you claimed it is) but is a simple device in helping to solve the system of linear equations. The equation given simply establishes a relationship between the volatility of equity and the volatility of the assets and it follows from the application of Black Scholes ...

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From my point of view, dynamic models like the one developped in Relative Volume as a Doubly Stochastic Binomial Point Process - James Mcculloch to provide a dynamic forecast of the volume does not improve significantly the forecasting comparing to a static volume curve forecast using historical data (last month intraday data, and an EWMA algorithm). I've ...

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As @Rustam notes, "correlation" of deterministic functions in the sense you describe is a special case of allowing $\mu$ and $\sigma$ to have a term structure of arbitrary shape. Since the latter is easy to treat, no one bothers with restricted forms of it. Now, there quite a few people who deal with models that let $\sigma$ change with $S$. I am thinking ...

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In my experience with forecasting, you could try a model of the form $$X_ t = cycle_t + seasonality_t + residuum_t.$$ Sometimes it is hard to find the cycle but the seasonality could be doable if it has some natural structure (something happening in a certain month each year e.g.). Rob Hyndman explains all these things (and provides an R package) in his ...

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Nobody assumes the market is right. The issue with Libor was that nobody could do much about it. Well, not 100% correct, you had the following 3 choices: Participate in the scheme and benefit (monetarily) Be on the receiving end and despite you knowing the rate is not what it should be you have no choice, you gotta borrow/lend your funds at the end of the ...

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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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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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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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Try the following : perform the logarithmic transformation of the volume data. check if the transformed data fits the normal distribution nicely. if you are working with intraday volume, then adjust for the seasonality for time of the day effect, if using daily data, in some cases some special seasonalities like expiry day, etc might be applied but it may ...

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