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服从 Gamma 分布的随机前沿模型AI 解读

We modify the stochastic frontier model of Aigner, Lovell, and Schmidt to allow the one-sided part of the disturbance to have a two-parameter Gamma distribution rather than the less flexible half-normal distribution. Maximum-likelihood estimation and the estimation of firm-specific inefficiency estimates require the evaluation of integrals which have no closed form and for which there are no polynomial approximations available. We consider two methods of computing these…

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数据包络分析中的最大规模有效尺度与决策单元大小效率AI 解读

This paper extends the usual DEA analysis, which evaluates input savings that could have been effected by a decision-making unit, given its observed task, to inquire into whether even greater savings would be possible if the task were to be optimally apportioned to a number of smaller units. The notion of size efficiency is introduced to measure this potential for further input reductions, and then compared and contrasted to the extant notion of scale efficiency. The…

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利用资产支付的条件矩推断跨期边际替代率波动AI 解读

Previously Hansen and Jagannathan (1990a) derived and computed mean-standard deviation frontiers for intertemporal marginal rates of substitution (IMRS) implied by asset market data. These frontiers give the lower bounds on the standard deviations as a function of the mean. In this paper we develop a strategy for utilizing conditioning information efficiently, and hence improve on the standard deviation bounds computed by Hansen and Jagannathan. We implement this strategy…

重新审视残差风险AI 解读

The Capital Asset Pricing Model in conjunction with the usual market model assumptions implies that well-diversified portfolios should be mean variance efficient and, hence, betas computed with respect to such indices should completely explain expected returns on individual assets. In fact, there is now a large body of evidence indicating that the market proxies usually employed in empirical tests are not mean variance efficient. Moreover, there is considerable evidence…

股票条件波动率的替代模型AI 解读

This paper compares several statistical models for monthly stock return volatility. The focus is on U.S. data from 1834-19:5 because the post-1926 data have been analyzed in more detail by others. Also, the Great Depression had levels of stock volatility that are inconsistent with stationary models for conditional heteroskedasticity, We show the importance of nonlinearities in stock return behavior that are not captured by conventional ARCH or GARCH models. We also show the…

因子 ARCH 协方差结构下的资产定价AI 解读

In this paper we suggest using the FACTOR-ARCH model as a parsimonious structure for the conditional covariance matrix of asset excess returns. This structure allows us to study the dynamic relationship between asset risk premia and volatilities in a multivariate system. One and two FACTOR-ARCH models are succussfully applied to pricing of Treasury bills. The results show stability over time, pass a variety of diagnostic tests, and compare favorably with previous empirical…

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