New PDF release: Bayesian risk management : a guide to model risk and

By Matt Sekerke

ISBN-10: 1118708601

ISBN-13: 9781118708606

ISBN-10: 1118747453

ISBN-13: 9781118747452

ISBN-10: 111874750X

ISBN-13: 9781118747506

ISBN-10: 1118864786

ISBN-13: 9781118864784

A danger size and administration framework that takes version threat seriously

Most monetary danger types imagine the longer term will appear like the prior, yet potent hazard administration will depend on selecting basic adjustments on the market as they happen. Bayesian chance Management info a extra versatile method of chance administration, and offers instruments to degree monetary probability in a dynamic marketplace atmosphere. This e-book opens dialogue approximately uncertainty in version parameters, version requirements, and model-driven forecasts in a fashion that ordinary statistical danger dimension doesn't. and in contrast to present laptop learning-based equipment, the framework offered right here enables you to degree danger in a fully-Bayesian atmosphere with out wasting the constitution afforded by way of parametric threat and asset-pricing versions.

  • Recognize the assumptions embodied in classical statistics
  • Quantify version danger alongside a number of dimensions with no backtesting
  • Model time sequence with no assuming stationarity
  • Estimate state-space time sequence versions on-line with simulation methods
  • Uncover uncertainty in workhorse probability and asset-pricing models
  • Embed Bayesian puzzling over possibility inside a posh organization

Ignoring uncertainty in possibility modeling creates an phantasm of mastery and fosters faulty decision-making. organizations who forget about the various dimensions of version hazard degree too little threat, and turn out taking over an excessive amount of. Bayesian possibility Management offers a roadmap to higher danger administration via extra circumspect dimension, with finished therapy of version uncertainty

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Download e-book for iPad: Bayesian risk management : a guide to model risk and by Matt Sekerke

A hazard dimension and administration framework that takes version probability heavily most money danger versions imagine the long run will seem like the previous, yet powerful chance administration relies on selecting basic alterations available to buy as they take place. Bayesian chance administration information a extra versatile method of probability administration, and offers instruments to degree monetary threat in a dynamic marketplace surroundings.

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Extra resources for Bayesian risk management : a guide to model risk and sequential learning in financial markets

Example text

So-called objective Bayesians prefer uninformative prior distributions because they allow the apparatus of Bayesian probability to be used consistently without introducing information from sources other than the data. For our purposes, we note that the mean of the posterior distribution of a parameter will coincide with its maximum-likelihood estimate only when we begin from a state of complete ignorance about the parameters of interest. However, it is important to emphasize that even when we begin from an improper, uninformative prior we still arrive at a posterior distribution for the parameter of interest, rather than a point estimate.

As a result, the order of the xi would not change the conclusions that we draw from the data about s, whether we use the maximum-likelihood estimator ŝ ML = xn or the Bayesian a+x . In fact we can throw out the complete set of information analogue a+b+n {x1 ,…, xn } without loss so long as we retain the sufficient statistics n and x. Not all likelihoods have low-dimension sufficient statistics, so it is often the case that the sufficient statistics comprise the entire data set. In these situations ongoing storage of the data set is necessary.

This reflects both the weight given to prior information as well as the information gained by restricting the parameter space to [0, 1]. 7. 7 29 30 CAPTURING UNCERTAINTY IN STATISTICAL MODELS Posterior estimates clearly gravitate away from maximum-likelihood estimates when prior and likelihood information are at odds with each other, and more so when greater weight is given to the prior. Whether this is a problem is a question we will explore at the end of the chapter. Hyperparameters and Sufficient Statistics A further point can be made from the Bernoulli trial example.

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Bayesian risk management : a guide to model risk and sequential learning in financial markets by Matt Sekerke


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