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Conditional autoregressive value at risk and other essays in financial econometrics.

机译：有条件的自回归风险值和金融计量经济学中的其他文章。

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摘要

This dissertation contains three econometric applications in financial econometrics. The first two chapters deal with the estimation of Value at Risk. The last chapter, instead, focuses on market microstructure.; The first chapter introduces a new methodology to estimate the Value at Risk (VaR) of a portfolio. The Conditional Autoregressive Value at Risk or CAViaR model moves the focus of attention from the distribution of returns directly to the behavior of the quantile. I specify the evolution of the quantile over time using an autoregressive process and use the regression quantile framework to estimate the unknown parameters. Utilizing the criterion that each period the probability of exceeding the VaR must be independent of all the past information, I introduce a new test of model adequacy, the Dynamic Quantile test, Applications to simulated and real data provide empirical support to this methodology and illustrate the ability of these algorithms to adapt to new risk environments.; The main objective of the second chapter is to survey and evaluate the performance of the most popular VaR methodologies, paying particular attention to their underlying assumptions and to their logical flaws. I also provide two original methodological contributions. The first one introduces the extreme value theory into the CAViaR framework. The second one concerns the estimation of the expected shortfall (the expected loss, given that the return exceeded the VaR) using a simple regression technique. The performance of the models surveyed in the paper is evaluated using a Monte Carlo simulation. The results show that CAViaR models are the best performers with heavy-tailed DGP. This is attributed to the fact that CAViaR models impose weaker assumptions than all the other methodologies and thus are more robust to misspecification.; In the third chapter, I present a new econometric framework to test market microstructure theories. Following the modeling idea of the ACD models, I propose to model also volumes as an autoregressive process that multiplies an i.i.d. error term. Next, I model duration, volume and returns simultaneously, using a special type of vector autoregression. I allow expected duration, expected volume and variance of returns to depend on current and tagged values of the variables under study. I arrive to an econometric reduced form that incorporates causal and feedback effects among these variables. I also construct impulse-response functions that show how the system reacts to a perturbation of its long-run equilibrium.
机译：本文在金融计量经济学中包含三个计量经济学应用。前两章讨论风险价值的估计。相反，最后一章着重于市场微观结构。第一章介绍了一种新的方法来估算投资组合的风险价值（VaR）。条件自回归风险价值或CAViaR模型将注意力从收益的分布直接转移到分位数的行为。我使用自回归过程指定分位数随时间的演变，并使用回归分位数框架来估计未知参数。利用每个时期超过VaR的概率必须独立于所有过去信息的标准，我介绍了一种模型充分性的新检验，即动态分位数检验，对模拟和真实数据的应用为该方法提供了经验支持，并举例说明了这些算法适应新风险环境的能力。第二章的主要目的是调查和评估最流行的VaR方法的性能，并特别注意其基本假设和逻辑缺陷。我还提供了两种原始的方法论贡献。第一个将极值理论引入CAViaR框架。第二个问题涉及使用简单回归技术对预期缺口（预期收益，因为收益超过了风险价值）的估计。本文使用蒙特卡洛模拟评估了模型的性能。结果表明，CAViaR模型是重尾DGP的最佳性能。这归因于这样一个事实，即CAViaR模型比所有其他方法所施加的假设更弱，因此对错误指定更为健壮。在第三章中，我提出了一个新的计量经济学框架来测试市场微观结构理论。遵循ACD模型的建模思想，我建议也将体积建模为一个乘以i.i.d的自回归过程。错误项。接下来，我使用一种特殊类型的向量自回归来对持续时间，数量和收益同时建模。我允许预期的持续时间，预期的收益率和回报差异取决于所研究变量的当前值和标记值。我得出了一个计量经济学的简化形式，其中将因果和反馈效应纳入了这些变量中。我还构造了脉冲响应函数，以显示系统如何对长期平衡的扰动做出反应。

著录项

  • 作者

    Manganelli, Simone.;

  • 作者单位

    University of California, San Diego.;

  • 授予单位 University of California, San Diego.;
  • 学科 Economics Finance.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 120 p.
  • 总页数 120
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 财政、金融;
  • 关键词

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