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Three Essays of Applied Bayesian Modeling: Financial Return Contagion, Benchmarking Small Area Estimates, and Time-Varying Dependence.

机译：应用贝叶斯建模的三篇论文：财务收益传染，基准化小面积估计和时变依赖。

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摘要

This dissertation is composed of three chapters, each an application of Bayesian statistical models to particular research questions.;In Chapter 1, we evaluate systemic risk exposure of financial institutions. Building upon traditional regime switching approaches, we propose a network model for volatility contagion to assess linkages between institutions in the financial system. Focusing empirical analysis on the financial sector, we find that network connectivity has dynamic properties, with linkages between institutions increasing immediately before the recent crisis. Out-of-sample forecasts demonstrate the ability of the model to predict losses during distress periods. We find that institutional exposure to crisis events depends upon the structure of linkages, not strictly the number of linkages.;In Chapter 2, we develop procedures for benchmarking small area estimates. In sample surveys, precision can be increased by introducing small area models which "borrow strength" by incorporating auxiliary covariate information. One consequence of using small area models is that small area estimates at lower geographical levels typically will not aggregate to the estimate at the corresponding higher geographical levels. Benchmarking is the statistical procedure for reconciling these differences. Two new approaches to Bayesian benchmarking are introduced, one procedure based on Minimum Discrimination Information, and another for Bayesian self-consistent conditional benchmarking. Notably the proposed procedures construct adjusted posterior distributions whose moments all satisfy benchmarking constraints. In the context of the Fay-Herriot model, simulations are conducted to assess benchmarking performance. In Chapter 3, we exploit the Pair Copula Construction (PCC) to develop a flexible multivariate model for time-varying dependence. The PCC is an extremely flexible model for capturing complex, but static, multivariate dependency. We use a Bayesian framework to extend the PCC to account for time dynamic dependence structures. In particular, we model the time series of a transformation of parameters of the PCC as an autoregressive model, conducting inference using a Markov Chain Monte Carlo algorithm. We use financial data to illustrate empirical evidence for the existence of time dynamic dependence structures, show improved out-of-sample forecasts for our time dynamic PCC, and assess performance of dynamic PCC models for forecasting Value-at-Risk.
机译：本文由三章组成，每一章都将贝叶斯统计模型应用于特定的研究问题。在第一章中，我们评估了金融机构的系统性风险敞口。在传统的制度转换方法的基础上，我们提出了一种用于波动性传染的网络模型，以评估金融体系中机构之间的联系。通过对金融部门的实证分析，我们发现网络连接具有动态特性，在最近的危机爆发之前，机构之间的联系不断增加。样本外预测证明了模型预测遇险期间损失的能力。我们发现机构在危机事件中的风险敞口取决于联系的结构，而不是严格地取决于联系的数量。在第二章中，我们开发了对小面积估算进行基准测试的程序。在样本调查中，可以通过引入小面积模型来提高精度，该模型通过合并辅助协变量信息来“借用强度”。使用小面积模型的结果是，较低地理级别的小面积估计通常不会汇总到相应较高地理级别的估计。标杆管理是调和这些差异的统计程序。引入了两种新的贝叶斯基准测试方法，一种基于最小歧视信息的过程，另一种用于贝叶斯自洽条件条件基准测试。值得注意的是，所提出的程序构造了调整后验分布，其矩均满足基准约束。在Fay-Herriot模型的背景下，进行了仿真以评估基准性能。在第3章中，我们利用结对Copula构造（PCC）为时变依赖关系开发了灵活的多元模型。 PCC是一种非常灵活的模型，用于捕获复杂但静态的多变量依赖关系。我们使用贝叶斯框架来扩展PCC，以解决时间动态依赖结构。特别是，我们使用马尔可夫链蒙特卡罗算法进行推理，将PCC参数转换的时间序列建模为自回归模型。我们使用财务数据来说明存在时间动态依赖结构的经验证据，显示对我们的时间动态PCC的改进的样本外预测，并评估用于预测风险价值的动态PCC模型的性能。

著录项

  • 作者

    Vesper, Andrew Jay.;

  • 作者单位

    Harvard University.;

  • 授予单位 Harvard University.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 141 p.
  • 总页数 141
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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