经济增长模型中的储蓄率内生化问题

本文讨论经济增长模型中储蓄率内生化的各种问题,包括有限寿命与无限寿命、确定寿命与不确定寿命、不确定寿命下的消费决策方法、不确定寿命下非预料死亡者的遗产如何处理、个人生命如何分期、个人如何预期未来收入等。强调了寿命假定与人口年龄结构的内在联系,提出了寿命不确定下个人消费决策的新方法,探讨了有关未来收入的趋势预期和水平预期,并分析了哪一种更接近实际。     

货币政策地区效应——基于地区总供给曲线的理论与实证分析

本文着重检验了我国区域经济中的基于产出缺口的地区短期总供给曲线的存在性,进而分析地区短期总供给曲线对货币政策地区效应的影响。文章首先对地区短期总供给曲线存在的可能性进行了理论解释,应用卡尔曼滤波方法估算了我国各地区的产出缺口,然后运用ARMA模型说明预期通货膨胀率形成机制,最后以通货膨胀率与其预期之差、外部需求冲击等作为解释变量对产出缺口进行回归,结果发现,我国东部地区的短期总供给曲线较中西部地区的短期总供给曲线更平坦,因而东部地区货币政策效应要强于中西部地区。     

论完善合同法中的预期违约制度

《中华人民共和国合同法》的颁布是中国法制建设进程中一件大事,它结束了“三足鼎立”的合同立法状况,使具有中国特色的合同法律制度得以完善。新合同法借鉴英关法系国家的合同制度,规定了预期违约制度。我国《合同法》设置的预期违约制度,为预期的受害方提供了救济基础。在实际中,明确界定、准确把握预期违约的适用条件是充分发挥这一制度功能的前提。     

Inter-temporal choices with temporal reference dependence

I develop an intertemporal choice model for rational deviators whose preferences depend not only on their actual consumption but also on comparison to their beliefs about the optimal consumption. The standard decision maker is loss averse with respect to this belief-dependent reference point. When psychologically weighted loss aversion is low, a decision maker deviates from the standard intertemporal choice behavior and over-consumption, as well as the alternative possibility of under-consumption can be rationalized. When the decision maker has time-varying degrees of loss aversion, he re-optimizes the consumption plan through adjusted beliefs as subsequent selves realize that past decision for the present period is no longer optimal. In the dynamic model, I solve for consistent intertemporal optimization rules by which a dynamic deviator should meet rational intertemporal consistency at each point in time. Finally, I demonstrate that the dynamic reference dependent model can solve a puzzling feature in lifecycle consumption data.     

金融稳定是否应纳入中国货币政策目标——基于混频IS-Phillips模型的实证分析

文章首先使用混频动态因子模型（MF-DFM），构建中国首个混频金融稳定指数（MF-FSI），接着把MF-FSI作为金融稳定的代理变量，使用文章新构建的混频IS-Phillips模型，比较分析纳入与不纳入金融稳定的中国货币政策损失函数差异，最后对货币政策目标选择和时效选择进行了敏感性分析，实证分析表明：（1）中国混频金融稳定指数是金融稳定的一个实时性有效测度指标；（2）中国货币政策目标应纳入金融稳定，以减少货币政策福利损失；（3）无论中国货币政策目标偏好和预期偏好怎样变化，央行都应给予金融稳定固定且不可忽略的关注度，但物价稳定和经济增长仍为主要货币政策目标。     

Expected utility theory on mixture spaces without the completeness axiom

A mixture preorder is a preorder on a mixture space (such as a convex set) that is compatible with the mixing operation. In decision theoretic terms, it satisfies the central expected utility axiom of strong independence. We consider when a mixture preorder has a multi-representation that consists of real-valued, mixture-preserving functions. If it does, it must satisfy the mixture continuity axiom of Herstein and Milnor (1953). Mixture continuity is sufficient for a mixture-preserving multi-representation when the dimension of the mixture space is countable, but not when it is uncountable. Our strongest positive result is that mixture continuity is sufficient in conjunction with a novel axiom we call countable domination, which constrains the order complexity of the mixture preorder in terms of its Archimedean structure. We also consider what happens when the mixture space is given its natural weak topology. Continuity (having closed upper and lower sets) and closedness (having a closed graph) are stronger than mixture continuity. We show that continuity is necessary but not sufficient for a mixture preorder to have a mixture-preserving multi-representation. Closedness is also necessary; we leave it as an open question whether it is sufficient. We end with results concerning the existence of mixture-preserving multi-representations that consist entirely of strictly increasing functions, and a uniqueness result.     

Loss aversion and market crashes

This study proposes a rational expectation equilibrium model of stock market crashes with information asymmetry and loss averse speculators. We obtain a state-dependent linear optimal trading strategy, which makes the equilibrium price tractable. The model predicts nonlinear market depth and the result that small shocks to fundamentals (e.g., supply or informational shocks) can cause abrupt price movements. We demonstrate that short-sale constraints intensify asset price collapses relative to upward movements. The model also generates contagion between uncorrelated assets. These results are consistent with the main puzzling features observed during market crashes, namely abrupt and asymmetric price movements that are not driven by major news events but coupled with a spillover effect between unrelated markets.     

Mixed data sampling expectile regression with applications to measuring financial risk

To avoid information loss or measurement error in traditional methods dealing with mixed frequency data, we develop a novel mixed data sampling expectile regression (MIDAS-ER) model to measure financial risk. We construct the MIDAS-ER model by introducing a MIDAS structure into expectile regressions. This enables us to perform an expectile regression on raw mixed frequency data directly. We apply the proposed MIDAS-ER model to estimate two popular financial risk measures, namely, Value at Risk and Expected Shortfall, with both simulated data and four stock indices, and compare the model's performance with those of several popular models. The outstanding performance of our model demonstrates that high-frequency information helps to improve the accuracy of risk measurement. In addition, the numerical results also imply that our model can be a significant tool for risk-averse investors to control risk losses and for financial institutions to implement robust risk management.     

Predicting LGD distributions with mixed continuous and discrete ordinal outcomes

The loss given default (LGD) distribution is known to have a complex structure. Consequently, the parametric approach for its prediction by fitting a density function may suffer a loss of predictive power. To overcome this potential drawback, we use the cumulative probability model (CPM) to predict the LGD distribution. The CPM applies a transformed variable to model the LGD distribution. This transformed variable has a semiparametric structure. It models the predictor effects parametrically. The functional form of the transformation is unspecified. Thus, CPM provides more flexibility and simplicity in modeling the LGD distribution. To implement CPM, we collect a sample of defaulted debts from Moody’s Default and Recovery Database. Given this sample, we use an expanding rolling window approach to investigate the out-of-time performance of CPM and its alternatives. Our results confirm that CPM is better than its alternatives, in the sense of yielding more accurate LGD distribution predictions.