Download
1 / 51
510 likes | 636 Views
Chapter 8 Conditioning Information. Structure. Introduction 8.0 An Important Theorem 8.1 Scaled Payoffs 8.2 Sufficiency of Adding Scaled Returns 8.3 Conditional and Unconditional Models 8.4 Scaled Factors:A Partial Solution 8.5 Summary. Introduction.
E N D
Structure • Introduction • 8.0 An Important Theorem • 8.1 Scaled Payoffs • 8.2 Sufficiency of Adding Scaled Returns • 8.3 Conditional and Unconditional Models • 8.4 Scaled Factors:A Partial Solution • 8.5 Summary
Introduction • If payoffs and discount factors were independent and identically distributed (i.i.d.) over time, then conditional expectations would be the same as unconditional expectations. • But stock price/dividend ratios, bond and option prices all change over time, which must reflect changing conditional moments. • Then how can we do?
Approach • One approach is to specify and estimate explicit statistical models of conditional distributions of asset payoffs and discount factor variables (e.g. consumption growth). This approach is sometimes used, and is useful in some applications, but it is usually cumbersome. Because the number of required parameters is so large that may quickly exceed the number of observations. • More importantly, we(economists or would-be social planners) obviously don’t even observe all the conditioning information used by economic agents, and we can’t include even a fraction of observed conditioning information in our models.
Method to settle • If we don’t want to model conditional distributions explicitly, and if we want to avoid assuming that investors only see the variables that we include in an empirical investigation, we eventually have to think about unconditional moments, or at least moments conditioned on less information than agents see.
Advantage of the unconditional moment • We may be interested in finding out why the unconditional mean returns on some stock portfolios are higher than others, even if every agent fundamentally seeks high conditional mean returns. • Most statistical estimation essentially amounts to characterizing unconditional means, as we will see in the chapter on GMM.
8.0 An Important Theoremthe Law of Iterated Expectation • Specification: If you take an expected value using less information of an expected value that is formed on more information, you get back the expected value using less information. • Your best forecast today of your best forecast tomorrow is the same as your best forecast today. • Some useful guises: （1）
8.1 Scaled Payoffs • From or We can take unconditional expectations: （2）
Remark 1 • Actually, every test practically uses managed portfolios. For example, the size, beta, industry, and so forth portfolios of stocks are all managed portfolios. Investors changes their composition every year in response to conditioning information-the size, beta, etc. of the individual stocks.
Conclusion • If we want to incorporate the extra information in conditioning information, we just add managed portfolio payoffs, and proceed with unconditional moments as if conditioning information did not exist. • We can thus incorporate conditioning information while still looking at unconditional moments instead of conditional moments, without any of the statistical machinery of explicit models with time-varying moments.
Subtleties • The set of asset payoffs expands dramatically, since we can consider all managed portfolios as well as basic assets, potentially multiplying every asset return by every information variable.
Remark 1 • We really don’t have to write the explicitly, recognizing that is a price and is a payoff. The space of all payoffs already includes the time-(t+1) payoffs we can generate with a basis set of assets and all dynamic strategies that use information in the set . So:
Remark 2 • [Theoretical]“All linear and nonlinear transformations of all variables observed at time t” sounds like a lot of instruments, and it is. But there is a practical limit to the number of instruments one needs to scale by, since only variables that forecast returns or m add any information.
Remark 3 • Since adding instruments is the same thing as including potential managed portfolios, the thoughtful choice of a few instruments is the same thing as the thoughtful choice of a few assets or portfolios that one makes in any test of an asset pricing models. Even when evaluating completely unconditional asset pricing models, one always forms portfolios and omits many possible assets from analysis.
Remark 4 • Of course, the other way to incorporate conditioning information is by constructing explicit parametric models of conditional distrubutions.But the parametric model may be incorrect,or may not reflect some variable used by investors.
Conditional vs. Unconditional Factor Models in Discount Factor Language
Here a>0 and b>0,and as , ,and vary over time, a and b must vary over time. • If it is to price assets conditionally, the CAPM must be a linear factor model with time-varying weights:
This fact means that we can no longer transparently condition down. That is: does not imply that we can find constants a and b so that
Proof • taking unconditional expectations, • While the unconditional model is:
Conditional vs. Unconditional in an Expected Return-Beta Model • We begin with beta pricing language, • But it does not imply that • Taking unconditional expectations of (14):
Remark 1 • Actually, an unconditional factor pricing model might be more appropriately called a fixed-weight factor pricing model. • So, the unconditional model is just a special case of the conditional model, one that happens to have fixed weights. • Thus, a conditional factor model does not imply an unconditional factor model (because the weights may vary) but an unconditional factor model does imply a conditional factor model.
Remark 2 • Consider the relation between a conditional factor pricing model using a fine information set (like investors’ information sets) and conditional factor pricing models using coarser information sets (like ours). If a set of factors prices assets with respect to investors’ information, that does not mean the same set of factors prices assets with respect to our, coarser, information sets.
Relation between two Frontiers • These two frontiers are related by: • If a return is on the unconditional mean-variance frontier, it is on the conditional mean-variance frontier. • If a return is on the conditional mean-variance frontier, it need not be on the unconditional mean-variance frontier. • Several ways to see this relation:
Proof 2(续) • Constants are in the time-t information set; but time-t random variables are not necessarily constant. • Thus again, unconditional efficiency (including managed portfolios) implies conditional efficiency but not vice versa.
Brute Force and Examples • If a return is on the unconditional MVF it must be on the conditional MVF at each date. • The unconditional MVF solves: • Writing the unconditional moment in terms of conditional moments:
Example Presentation • Return B is conditionally mean-variance efficient. It also has zero unconditional variance, so it is the unconditionally mean-variance efficient return at the expected return shown. Return A is on the conditional mean-variance frontiers, and has the same unconditional expected return as B. But return A has some unconditional variance, and so is inside the unconditional mean-variance frontier.
Implications: Hansen-Richard Critique • Models such as the CAPM imply a conditional linear factor model with respect to investors’ information sets. However, the best we can hope to do is to test implications conditioned down on variables that we can observe and include in a test. Thus, a conditional linear factor model is not testable!
Here call this observation the “Hansen-Richard critique” by analogy to the “Roll Critique.” Roll pointed out, among other things, that the wealth portfolio might not be observable, making tests of the CAPM impossible. Hansen and Richard point out that the conditioning information of agents might not be observable, and that one cannot omit it in testing a conditional model. Thus, even if the wealth portfolio were observable, the fact that we cannot observe agents’ information sets dooms tests of the CAPM.
For example, consider the conditional CAPM: • We can add some scaled factors, say, the dividend/price ratio and term premium, thus the conditional CAPM implies an unconditional, five-factor (plus constant) model. The factors are a constant, the market return, the dividend/price ratio, the term premium, and the market return times the dividend-price ratio and the term premium.
The unconditional pricing implications of such a five-factor model could, of course, be summarized by a single-beta representation. The reference portfolio would not be the market portfolio, of course, but a mimickingportfolio of the five factors. However, the single mimicking portfolio would not be easily interpretable in terms of a single factor conditional model and two instruments. In this case, it might be more interesting to look at a multiple-beta or multiple-factor representation.
8.5 Summary • It is rather troublesome when facing conditioning information. But now we get a simple method. To express the conditional implications of a given model, all we have to do is include some scaled or managed portfolio returns, and then pretend we never heard about conditioning information. • Some factor models are conditional models, and have coefficients that are functions of investors’ information sets. In general, there is no way to test such models, but if we are willing to assume that the relevant conditioning information is well summarized by a few variables, then we can just add new factors, equal to the old factors scaled by the conditioning variables, and again forget that we ever heard about conditioning information.
