By Matt Sekerke
A possibility dimension and administration framework that takes version possibility seriously
Most monetary chance types imagine the long run will appear like the prior, yet potent threat administration will depend on determining basic alterations on the market as they take place. Bayesian hazard Management info a extra versatile method of possibility administration, and gives instruments to degree monetary possibility in a dynamic marketplace surroundings. This booklet opens dialogue approximately uncertainty in version parameters, version necessities, and model-driven forecasts in a fashion that ordinary statistical chance size doesn't. and in contrast to present computer learning-based equipment, the framework offered the following helps you to degree chance in a fully-Bayesian environment with no wasting the constitution afforded by way of parametric probability and asset-pricing types.
- Recognize the assumptions embodied in classical statistics
- Quantify version hazard alongside a number of dimensions with out backtesting
- Model time sequence with out assuming stationarity
- Estimate state-space time sequence versions on-line with simulation methods
- Uncover uncertainty in workhorse possibility and asset-pricing models
- Embed Bayesian pondering threat inside of a posh organization
Ignoring uncertainty in possibility modeling creates an phantasm of mastery and fosters faulty decision-making. organizations who forget about the numerous dimensions of version chance degree too little danger, and prove taking over an excessive amount of. Bayesian chance Management presents a roadmap to raised hazard administration via extra circumspect dimension, with finished remedy of version uncertainty
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Extra info for Bayesian risk management : a guide to model risk and sequential learning in financial markets
Without a doubt, one can manipulate results by changing a prior. Had we used B(3 × 106 , 7 × 106 ) as our prior distribution, even 10,000 observations with x = 9000 would have very little influence on the outcome. But a prior may be exposed to criticism every bit as much as the posterior result. One who routinely uses farfetched or tendentious priors will draw faulty conclusions and lose credibility every bit as much as one who produces incorrect results or dogmatically refuses to consider the data.
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.
The technique of discounting, introduced in Chapter 4, ensures that current observations have a greater role in reevaluating parameter distributions and model probabilities than the accumulated weight of observations in the distant past. Discounting past data is already common practice and is implemented in standard risk management software. When new data enter the observation window, models are recalibrated on the reweighted data set. However, reweighting the data introduces new problems and does not nullify the problems associated with recalibration.