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Cowles Foundation Discussion Papers

Asset pricing

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Full-Text Articles in Social and Behavioral Sciences

Robust Identification Of Investor Beliefs, Xiaohong Chen, Lars P. Hansen, Peter G. Hansen May 2020

Robust Identification Of Investor Beliefs, Xiaohong Chen, Lars P. Hansen, Peter G. Hansen

Cowles Foundation Discussion Papers

This paper develops a new method informed by data and models to recover information about investor beliefs. Our approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, our method allows for the direct use of sparse survey evidence to make these bounds more informative. Within our framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, we represent evidence …


Local Identification Of Nonparametric And Semiparametric Models, Xiaohong Chen, Victor Chernozhukov, Sokbae Lee, Whitney Newey Apr 2011

Local Identification Of Nonparametric And Semiparametric Models, Xiaohong Chen, Victor Chernozhukov, Sokbae Lee, Whitney Newey

Cowles Foundation Discussion Papers

In parametric models a sufficient condition for local identification is that the vector of moment conditions is differentiable at the true parameter with full rank derivative matrix. We show that additional conditions are often needed in nonlinear, nonparametric models to avoid nonlinearities overwhelming linear effects. We give restrictions on a neighborhood of the true value that are sufficient for local identification. We apply these results to obtain new, primitive identification conditions in several important models, including nonseparable quantile instrumental variable (IV) models, single-index IV models, and semiparametric consumption-based asset pricing models.


Local Identification Of Nonparametric And Semiparametric Models, Xiaohong Chen, Victor Chernozhukov, Sokbae Lee, Whitney Newey Apr 2011

Local Identification Of Nonparametric And Semiparametric Models, Xiaohong Chen, Victor Chernozhukov, Sokbae Lee, Whitney Newey

Cowles Foundation Discussion Papers

In parametric models a sufficient condition for local identification is that the vector of moment conditions is differentiable at the true parameter with full rank derivative matrix. We show that there are corresponding sufficient conditions for nonparametric models. A nonparametric rank condition and differentiability of the moment conditions with respect to a certain norm imply local identification. It turns out these conditions are slightly stronger than needed and are hard to check, so we provide weaker and more primitive conditions. We extend the results to semiparametric models. We illustrate the sufficient conditions with endogenous quantile and single index examples. We …


A Simple Test For Identification In Gmm Under Conditional Moment Restrictions, Francesco Bravo, Juan Carlos Escanciano, Taisuke Otsu Mar 2011

A Simple Test For Identification In Gmm Under Conditional Moment Restrictions, Francesco Bravo, Juan Carlos Escanciano, Taisuke Otsu

Cowles Foundation Discussion Papers

This paper proposes a simple, fairly general, test for global identification of unconditional moment restrictions implied from point-identified conditional moment restrictions. The test is based on the Hausdorff distance between an estimator that is consistent even under global identification failure of the unconditional moment restrictions, and an estimator of the identified set of the unconditional moment restrictions. The proposed test has a chi-squared limiting distribution and is also able to detect weak identification alternatives. Some Monte Carlo experiments show that the proposed test has competitive finite sample properties already for moderate sample sizes.


Liquidity Black Holes, Stephen Morris, Hyun Song Shin Sep 2003

Liquidity Black Holes, Stephen Morris, Hyun Song Shin

Cowles Foundation Discussion Papers

Traders with short horizons and privately known trading limits interact in a market for a risky asset. Risk-averse, long horizon traders supply a downward sloping residual demand curve that face the short-horizon traders. When the price falls close to the trading limits of the short horizon traders, selling of the risky asset by any trader increases the incentives for others to sell. Sales become mutually reinforcing among the short term traders, and payoffs analogous to a bank run are generated. A “liquidity black hole” is the analogue of the run outcome in a bank run model. Short horizon traders sell …


Empirical Implications Of Arbitrage-Free Asset Markets, S. Maheswaran, Christopher A. Sims Jan 1992

Empirical Implications Of Arbitrage-Free Asset Markets, S. Maheswaran, Christopher A. Sims

Cowles Foundation Discussion Papers

The martingale-equivalence condition delivered by a non-arbitrage assumption in complete asset markets has implications for fine-time-unit asset price behavior that can be rejected with finite spans of data. A class of stochastic processes that could model such deviations from martingale-equivalence is proposed.


Actual And Warranted Relations Between Asset Prices, Andrea E. Beltratti, Robert J. Shiller Feb 1991

Actual And Warranted Relations Between Asset Prices, Andrea E. Beltratti, Robert J. Shiller

Cowles Foundation Discussion Papers

Efficient markets models assert that the price of each asset is equal to the optimal forecast of its ex-post or fundamental value. These models do not imply, however, that the covariance between two asset prices is given by the covariance between the ex-post values they respectively forecast: these two covariances can even have opposite signs. However, it is possible to place bounds on the covariance between asset prices given the covariance matrix of ex-post values. We present such bounds for both covariances and correlations and show how such bounds can be tightened using information beyond the covariance matrix of ex-post …