基于蒙特卡罗模拟的商业银行信用风险度量方法

通过与Matlab程序相结合的方式介绍了基于蒙特卡罗模拟的商业银行信用风险度量方法。该方法使在给定的置信水平下科学地估算国内商业银行的信用风险成为可能。     

Pooling data for the analysis of dynamic marketing systems

Vector autoregressive (VAR) models have become popular in marketing literature for analyzing the behavior of competitive marketing systems. One drawback of these models is that the number of parameters can become very large, potentially leading to estimation problems. Pooling data for multiple cross-sectional units (stores) can partly alleviate these problems. An important issue in such models is how heterogeneity among cross-sectional units is accounted for. We investigate the performance of several pooling approaches that accommodate different levels of cross-sectional heterogeneity in a simulation study and in an empirical application. Our results show that the random coefficients modeling approach is an overall good choice when the estimated VAR model is used for out-of-sample forecasting only. When the estimated model is used to compute Impulse Response Functions, we conclude that one should select a modeling approach that matches the level of heterogeneity in the data.     

随机折现因子方法与CAPM关于风险溢价的实证比较

本文根据随机折现因子方法的基本理论，结合广义矩阵法和蒙特卡罗模拟，对随机折现因子方法和传统的CAPM对风险溢价的计算进行实证比较研究。实证结果表明，对于中小样本，随机折现因子方法比传统的CAPM方法优越。估计量较为精确，误差小；对于大容量样本，这两种方法性能接近。另外，随机折现因子方法得到Jensen＇sα均值比CAPM方法得到Jensen＇sα均值小，而且标准偏差明显较小，也从另一角度说明了随机折现因子方法的优越性。     

Estimation methods for stochastic volatility models: a survey

Abstract.  Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been introduced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.     

Testing equality of variances in the analysis of repeated measurements

The problem of comparing the precisions of two instruments using repeated measurements can be cast as an extension of the Pitman-Morgan problem of testing equality of variances of a bivariate normal distribution. Hawkins (1981) decomposes the hypothesis of equal variances in this model into two subhypotheses for which simple tests exist. For the overall hypothesis he proposes to combine the tests of the subhypotheses using Fisher's method and empirically compares the component tests and their combination with the likelihood ratio test. In this paper an attempt is made to resolve some discrepancies and puzzling conclusions in Hawkins's study and to propose simple modifications.
The new tests are compared to the tests discussed by Hawkins and to each other both in terms of the finite sample power (estimated by Monte Carlo simulation) and theoretically in terms of asymptotic relative efficiencies.     

SELECTION BIAS CORRECTIONS BASED ON THE MULTINOMIAL LOGIT MODEL: MONTE CARLO COMPARISONS

Abstract.  This survey presents the set of methods available in the literature on selection bias correction, when selection is specified as a multinomial logit model. It contrasts the underlying assumptions made by the different methods and shows results from a set of Monte Carlo experiments. We find that, in many cases, the approach initiated by Dubin and MacFadden (1984) as well as the semi-parametric alternative recently proposed by Dahl (2002) are to be preferred to the most commonly used Lee (1983) method. We also find that a restriction imposed in the original Dubin and MacFadden paper can be waived to achieve more robust estimators. Monte Carlo experiments also show that selection bias correction based on the multinomial logit model can provide fairly good correction for the outcome equation, even when the IIA hypothesis is violated.     

CEV模型的单位根检验研究

CEV模型（Constant Elasticity of Variance Model）作为常用的利率模型，在实证分析中得到了广泛运用，但是其单位根检验一直被忽略或者被默认可以使用迪基一富勒检验。本文首次运用Box—Cox变换的技巧，针对CEV模型的单位根检验问题，找到了合适的统计量并且证明其渐进分布存在，然后通过蒙特卡罗方法求出了该统计量的分布表。得到了在大样本的情形下可以沿用迪基一富勒检验，但在小样本的情形下与迪基一富勒检验有所偏差的结论。     

CGE assumptions under scrutiny: uncertainty in the ethanol promotion policy in Mexico

Based on a CGE exercise of a subsidy to initiate ethanol production in Mexico, we use Monte Carlo simulations for consumer demand elasticities and ethanol cost estimates. The analysis provides three conclusions: when markets vary smoothly and predictably, Monte Carlo methods can then help to gauge the actual probability that a given program will achieve a desired outcome. Second, secondary markets may display little or no sensitivity to these parameter variations. Finally, a ‘razor’s edge’ outcome with no positive benefits if a critical parameter falls below some critical value, reveals that an economic policy may not be conducive to ‘fine tuning’ by marginal adjustments.     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

Weather derivatives structuring and pricing: a sustainable agricultural approach in Africa

The objective of this article is to calculate the price of weather derivatives for different African countries with payout depending on temperature. A new approach for computing degree day contracts is shown and gives another scale to the numerical relevance and practical implementation of the findings. With historical data for each country, a stochastic process based on continuous time with mean reversion representing the evolution of the temperature is determined. Focusing on the Monte Carlo simulation method, the price of each contract and the potential implications to solve several aspects of the threatened African economy are presented.