lec5 - Statistical Inference for FE Professor S. Kou,...

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Statistical Inference for FE Professor S. Kou, Department of IEOR, Columbia University Lecture 5. Bayesian Inference 1I n t r o d u c t i o n So far we have focused on point estimation and hypothesis testing via fre- quentist methods. The frequentist method is based on the following assump- tions. F1. Probability is equivalent to limiting relative frequencies, and hence are objective properties. F2. Unknown parameters are f xed, deterministic constants. F3. Inference procedures should always be interpreted via long run aver- ages. For example, a 95% c.i. should has a 95% limiting coverage frequency if we repeat the same procedure many times. However, there is a di f erent school of statistical inference, called Bayesian inference, which is based on the following assumptions. B1. Probability relates to degree of belief, not frequencies. With this interpretation, we can have wider applications of probability. For example, we can say that with probability 0.55 an apple from a tree did drop to the head of Isaac Newton. This statement re F ects a subjective belief, not a limiting frequency. B2. Unknown parameters are uncertain and therefore can be modeled as random variables. B3. Inference means giving a updated prediction about the distribution of the unknown parameters. Clearly the Bayesian approach is subjective; this attributes to the popu- larity of the frequentist approach, as people in general likes objective meth- ods. However, in f nance, there is a growing support of Bayesian approach, mainly because estimation in many f nancial problems are very hard with- out Bayesian approaches. For example, since it is very di cult to estimate the true unknown returns of stocks, it makes sense to model the returns as random variables, and use Bayesian approaches to give estimators of returns by combining subjective views of the traders and the empirical data. This is the view adapted in Black-Litterman’s asset allocation method, which we will cover later. 2T h e B a y e s i a n M e t h o d The Bayesian inference requires two inputs. 1. A prior distribution f ( θ ) about the unknown parameter θ before we see the data. 1
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2. A likelihood function, or a model, that links θ and the data X = ( X 1 ,...,X n ) . More precisely, we need to specify the conditional joint density f ( X | θ ) . After this, we can calculate the posterior distribution of f ( θ | X ) by using Bayes’ formula from elementary probability. More precisely, f ( θ | X )= f ( θ ,X ) f ( X ) = f ( X | θ ) f ( θ ) R f ( θ ,X ) d θ = f ( X | θ ) f ( θ ) R f ( X | θ ) f ( θ ) d θ . We can write this as f ( θ | X ) f ( X | θ ) f ( θ ) , because the normalizing constant C ( X )= Z f ( X | θ ) f ( θ ) d θ does not depend on θ . Typical C ( X ) can be either got analytically (by using the fact that total probability must be one), or by numerical integration. A recent revolution in Bayesian analysis is that very often we do not need
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This note was uploaded on 10/18/2010 for the course IEOR 4702 taught by Professor Kou during the Spring '10 term at Columbia.

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lec5 - Statistical Inference for FE Professor S. Kou,...

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