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In general there are two basic ways to make money out of your option pricing models: Sell side (market maker, risk neutral): You use these models to calculate your greeks to hedge your portfolio, so that you live on the spread. Buy side (market/risk taker): You use your model to find mispriced options in the market and buy/sell accordingly. (A third ...

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There is a family of models that is so commonly used among practitioners that it can be almost regarded as standard. For a survey, check out Rob Almgren's entry in the Encyclopedia of Quantitative Finance. Check out also Barra, Axioma and Northfield's handbooks. In general, the impact term per unit traded currency is of the form $$MI \propto \sigma_n \cdot ... 10 You can forecast stock prices thru time-series models, cross-sectional, or panel models. There is considerable variation within these categories. In time-series models you would use an auto-regressive model such as an AR(1) where the independent variable is the dependent variable lagged by one period. Naturally, an AR(2) would consist of 2 lags and so on. ... 7 I don't believe that there is a "standard" model (per say); in fact, there are many considerations around market impact models, so you would need to be more specific. At the most basic level, you might define market as P_{first fill} - P_{last fill} once your order in actually in the order book (e.g. not including other costs like "opportunity cost"). ... 6 Market makers covers a broad range of shops, from large investment banks to small proprietary trading firms. So working capital can be in the millions or the billions, and leverage can be anywhere from 2x to 30x. This is no different from buy-side firms, which includes a variety of both asset managers and retail investors. There is tons of diversity among ... 6 Agree with all of vonjd's points though I like to add the following: First of all, market practitioners do not read options prices or set options prices in the market, they price the option through models primarily on the basis of implied volatility. Im plied volatility is actually traded, options prices is what comes out on the other side. I know there ... 5 Since both ER and S are gaussian random, why not just assume their dependence is captured by their covariance, and make your draws from the bivariate normal distribution? It is hard to construct any other way of making two marginal gaussians cointegrated. Even if the variables were not gaussian, you would probably find yourself relating them using a ... 5 I don't think that there is a precise point in time when we can say that model is valid (well, it's a model not a law). For example, E. Derman in his article on Model risk describes the verification of model as a iterative process: It is impossible to avoid errors during model development, especially when they are created under trading floor duress .... ... 5 Keynes introduced this idea in the notion of a Keynesian Beauty contest: http://en.wikipedia.org/wiki/Keynesian_beauty_contest Anyone who uses a rolling window regression where the parameters and/or parameter estimates are re-fitted periodically are implicitly accounting for this reflexivity (i.e. the market's changing behavior as agents respond and adapt ... 5 Multi-fractal models can be applied to the modeling and forecasting of volatility. I read the following book with much interest and actually setup couple models in order to compare performance vs Garch family models and the application of multi-fractals much better captures discontinuous regime-changes than traditional volatility models. ... 5 You don't mention if the puts in question are exotic or vanilla, but assuming they are vanilla, you should read this paper by Chen and Joshi. In it, they find optimal performance by using smoothed, truncated Tian-parameter binomial lattices with Richardson extrapolation -- where the idea is to run one extra low-cost (long \Delta T) tree in order to ... 4 The answers above are good, but I suspect they will be unsatisfactory if you are looking for implementations that are successful in practice. The sort of bottom-up analysis championed by Soros is very difficult to carry out in a rigorous, quantitative manner. This is true very generally, not just in finance. There are certainly models of financial markets ... 4 The paper "High Frequency Trading and The New-Market Makers" by Menkveld will likely have information that will be interesting to you. The paper breaks down the activity of one HFT in a European market. It provides statistics such as the # of trades, capital required, average profit, loss, etc. You can judge for yourself whether you trust the numbers based ... 4 You can ask for a quote from a bank as I am sure they will create it for you. If you want to create this kind of payoff yourself, you can use the following paper from Peter Carr where he introduces the spanning formula for replicating any twice differentiable payoff. http://www.math.nyu.edu/research/carrp/papers/pdf/twrdsfig.pdf 4 The primary alternative to Bayesian subjective probabilities is the frequentist approach. This would involve measuring the % of times where international bonds outperformed US bonds by 25 bps over the relevant period in market history and using that as your confidence level. A quantitative view in-between the Bayesian and frequentist approaches would be a ... 4 These returns are almost always modeled by finding some fundamental two-sided variable and modeling that. For options, we would model their prices as derivatives -- we would take the log-returns of underlying prices as the fundamental variable, possibly with other models for what would happen to volatilities and the like, and compute the consequences for ... 4 SMM stands for single-month mortality and CPR stands for constant (or conditional) prepayment rate. They're both units of voluntary prepayment rates (CPR = 1-(1-SMM)^{12}). They could be based on either estimated or actual prepayments. Where to get actual MBS prepayment data will depend on what type(s) of MBS pools you're modeling (e.g. agency, ... 4 Couple points I like to make: There exists no reliable model that can even predict future price returns (risk premiums, excess returns, whatever you want to call it) beyond a year, run as fast as you can if you hear from someone who claims he can predict risk premiums 10 years out, whether reliably or not. It makes zero sense and clearly comes from either ... 4 Generally we use models that go so far out in a comparative sense, not as an absolute decision. You are definitely do some good reading but I believe you are thinking about these models in the wrong way. I think (and correct me if I'm wrong) you are looking at creating or finding the perfect "crystal ball" model that will predict returns/risk premiums etc. ... 3 The original Nelson Siegel paper describes a parsimonious model of the term structure using only four or three (if \lambda_t is fixed). Filipovic (1999) proves that this model can never be used in a arbitrage free context, paraphrasing the abstract: We introduce the class of consistent state space processes, which have the property to provide an ... 3 This link is to a book that covers this exact question: http://onlinelibrary.wiley.com/doi/10.1002/9783527610006.ch9/summary Summary: the models that map to both stock markets and currency markets are those that have an autoregressive feature (curreny markets commonly exhibit this feature, limiting the choice of models that apply to both currencies and ... 3 It shows up in Bayes Analysis where a binomial distribution is involved (integer values apply):$$ \Gamma(k + 1) = k! $$That allows the following integral to be evaluated in closed form:$$ \int_{0}^{1}p^{j-1}(1-p)^{k-1}dp = \frac{\Gamma(k)\Gamma(j)}{\Gamma(j+k)} $$That integral can easily show up in the numerator and/or denominator of Bayes Equation. 3 If \Sigma is the variance/covariance matrix of random variables U_1, U_2, \ldots U_n, and V = c + w_1 U_1 + \ldots + w_n U_n, where c is a constant, and we let \mathbf{w} be the vector with the 'weights' w_1, w_2, \ldots, w_n, then the variance of V is equal to \mathbf{w}^{\top}\Sigma\mathbf{w}. Moreover, if T is another random variable ... 2 This paper by Filimonov and Sornette might be interesting or useful to you. I've only read about the first third, but I thought the model was pretty cool. The model for price changes is a self-exciting Poisson process: there is an exogenous factor modeling the "real" price changes, and then there is a feedback mechanism where the overall arrival rate is an ... 2 Yes you can. Begin by choosing your favorite stochastic differential equation with a fat-tailed terminal distribution, for example a local volatility model. Use the usual techniques to convert to a partial differential equation (PDE). Construct an explicit finite difference scheme for solving the PDE, and make your number M of grid points in time ... 2 I don't believe there are any models because it would be fruitless to develop one. Whenever central bank intervention looms large in currency markets, all the traditional models become much less relevant than trying to predict how the central bank will react to various scenarios. In this case, foreseeing the SNB's move to sell a significant quantity of ... 2 I would say Start with Black Scholes to look at accuracy. In particular, you have a closed formula and you know what the characteristic function for lognormal is. Running FFT and comparing FFT pricing with the closed formula will give you an idea of what are the convergence issues, what is the behaviour at the boundaries (extreme strikes) etcetera. Then ... 2 Jim Gatherals Book deals with the models you mention and gives an intuitive understanding about calibration and issues that arise. Mostly basic stuff, but very useful if you're just starting out. Also very understandable without an extensive math background. 2 Amirsani, Here couple points how I would proceed: I would first look to divide your time series into different clusters, enough so that different market dynamics fall into different clusters. I guess you will not be trading a single asset and thus you will not just optimize over a single stock or options contract. I would strongly try to discourage from ... 2 Let C be the price of the option, S_t=S_0e^{X_t} be the stock price, r be the risk-free rate, K be the strike price, T be the maturity time, m=S_0/K, f be the density of X_T and \phi be the characteristic function E(e^{i\xi X_T}) which we assume is known.$$ C = e^{-rT}E((S_T-K)^+) = e^{-rT}S_0\int_{-\infty}^\infty ...

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