Browsing by Author "Bansal, R"

Now showing 1 - 4 of 4
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    Confidence risk and asset prices
    (American Economic Review, 2010-05-01) Bansal, R; Shaliastovich, I
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    Long run risks, the macroeconomy, and asset prices
    (American Economic Review, 2010-05-01) Bansal, R; Kiku, D; Yaron, A
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    Nonparametric estimation of structural models for high-frequency currency market data
    (Journal of Econometrics, 1995-01-01) Bansal, R; Gallant, AR; Hussey, R; Tauchen, G
    Empirical modeling of high-frequency currency market data reveals substantial evidence for nonnormality, stochastic volatility, and other nonlinearities. This paper investigates whether an equilibrium monetary model can account for nonlinearities in weekly data. The model incorporates time-nonseparable preferences and a transaction cost technology. Simulated sample paths are generated using Marcet's parameterized expectations procedure. The paper also develops a new method for estimation of structural economic models. The method forces the model to match (under a GMM criterion) the score function of a nonparametric estimate of the conditional density of observed data. The estimation uses weekly U.S.-German currency market data, 1975-90. © 1995.
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    Rational Pessimism, Rational Exuberance, and Asset Pricing Models
    (1999) Bansal, R; Gallant, AR; Tauchen, G
    estimates and examines the empirical plausibility of asset pricing models that attempt to explain features of financial markets such as the size of the equity premium and the volatility of the stock market. In one model, the long-run risks (LRR) model of Bansal and Yaron, low-frequency movements, and time-varying uncertainty in aggregate consumption growth are the key channels for understanding asset prices. In another, as typified by Campbell and Cochrane, habit formation, which generates time-varying risk aversion and consequently time variation in risk premia, is the key channel. These models are fitted to data using simulation estimators. Both models are found to fit the data equally well at conventional significance levels, and they can track quite closely a new measure of realized annual volatility. Further, scrutiny using a rich array of diagnostics suggests that the LRR model is preferred.