Statistical analysis of financial econometric models

金融计量模型的统计分析

• 批准号: 36358-2007
• 负责人: Knight, John
• 金额: $ 1.38万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=366229
• 关键词: Statistical; analysis; financial; econometric; models

This research will examine the statistical properties of various financial econometric models along with their associated estimators. In particular there will be two broad areas examined:a)models dealing with optimal portfolio allocation.Here the aim is to examine the distribution of the optimal portfolio weights or some scalar function of these weights. Since the weights are the result of a parametric quadratic programming problem, their properties are usually examined via Monte Carlo simulation. Past simulation studies have shown that the weights are usually severly biased. In order to ascertain the source of the bias and hence ways to reduce it one needs to consider the exact moments and the exact distribution of the weights.The research will consider various multivariate distributional assumptions on the vector of asset returns along with realistic and practically relevant constraints on the weights. While some progress has been made in simple cases under multivariate normality other distributional assumptions and non-negativity constraints have not been considered; these will be the subject of this research.b)models involving stochastic volatility.The research here will examine stochastic volatility (SV) models formulated in both discrete and continuous time. These models have, for many years, posed a major challenge to econometricians since their formulation involves an unobserved (latent) variable, the volatility. From a practical point there have been many alternative specifications each claiming to better capture various stylized facts of the data. Detailed analytical statistical analysis of many of these models has not been undertaken. Consequently, this section of the proposed research will aim to systematically examine these models, deriving exact statistical properties such as moments and characteristic functions. The results will enable a clearer understanding of the dynamics of these models and lead to improved estimation.

本研究将探讨各种金融计量经济学模型的统计特性沿着与其相关的估计。特别是，我们将研究两个广泛的领域：a）处理最优投资组合配置的模型，目的是研究最优投资组合权重或这些权重的标量函数的分布。由于权重是参数二次规划问题的结果，因此通常通过Monte Carlo模拟来检查它们的属性。过去的模拟研究表明，权重通常是严重的偏见。为了确定偏差的来源和减少偏差的方法，需要考虑权重的精确矩和精确分布，研究将考虑资产收益向量的各种多元分布假设，沿着对权重进行现实和实际相关的约束。虽然在简单的情况下，多元正态分布的其他假设和非负约束下，已取得了一些进展，没有被考虑，这些将是本研究的主题。B）涉及随机波动率的模型。本研究将探讨随机波动率（SV）模型制定在离散和连续时间。多年来，这些模型对计量经济学家提出了重大挑战，因为它们的公式涉及一个不可观察的（潜在的）变量，即波动性。从实践的角度来看，已经有许多替代规范，每一个都声称可以更好地捕获数据的各种程式化事实。对其中许多模式尚未进行详细的分析统计分析。因此，本节的研究将系统地研究这些模型，推导出精确的统计特性，如矩和特征函数。结果将使这些模型的动态更清晰的理解，并导致改进的估计。