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Stochastic volatility and stocks returns: Evidence from microstructure data.

机译:随机波动率和股票收益率:来自微观结构数据的证据。

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摘要

Chapter 1 shows that investors' concerns about systematic volatility risk (a distress proxy) can explain some of the small firm premia in the long run. Using a sample of returns spanning the period January 1927-December 2005, we document a statistically significant negative price for volatility risk. This finding indicates a "flight to quality": during recessions, investors shift their preferences away from small firms, which are considered as being relatively distressed. Instead, they use large, "quality" stocks which co-vary positively with innovations in volatility, and therefore pay off during bad economic times, when volatility is more volatile and market returns are lower. This leads to higher prices and lower average returns for large stocks. We find evidence that growth stocks are of hedging value to investors, too. We document a volatility premium ranging from 3% to 7% per year, across value sorted portfolios. We provide assurance that the volatility effect is not subsumed by classical risk factors that is robust with respect to the type of volatility measure used, model specification and that is not sample specific.; Chapter 2 focuses on high-frequency data. The use of high-frequency data in this paper allows us to isolate two potential sources of risk. The first one has to do with the efficient price process. The second one, which is novel, relates to genuine market frictions. We define market friction volatility as the volatility of the difference between observed asset prices and fundamental values.; We propose an asset pricing model having these two intuitively appealing volatility components, market volatility and market friction volatility, and investigate the extent to which it explains documented cross-sectional asset price anomalies.; At monthly frequencies, we find that both volatility measures are negatively priced in the cross-section of stock returns, and they have a strong link to the size anomaly. The small-minus-big premium due to efficient price volatility is as high as 3% per year, while the premium due to market friction volatility is as high as 8% per year, across value-sorted portfolios.; Market volatility and market friction volatility are also negatively priced in the cross-section of daily stock returns. Interestingly, in a 3-factor model comprising both volatility measures and daily market returns, market friction volatility appears to largely subsume the information contained in market volatility. Its premium is as high as 10% per year, across value-sorted portfolios.
机译:第1章表明,投资者对系统波动风险的担忧(一个求救信号)可以从长期上解释一些小公司的溢价。使用1927年1月至2005年12月这段时间的收益样本,我们记录了波动风险的统计上显着的负价格。这一发现表明“向质量飞行”:在经济衰退期间,投资者将偏好从小企业转移到了小企业,而小企业被认为是相对苦恼的。取而代之的是,他们使用大型的“优质”股票,这些股票与波动性的创新呈正相关关系,因此,在波动性更大,市场回报率较低的经济不景气时期会得到回报。这导致大型股票的价格上涨和平均回报降低。我们发现有证据表明,成长型股票对投资者也具有对冲价值。我们记录了价值分类投资组合中的波动率溢价每年3%至7%。我们提供保证,波动率效应不会被传统的风险因素所包含,该因素对于所用波动率度量的类型,模型规格而言是稳健的,并且并非针对样本。第2章重点介绍高频数据。本文使用高频数据可以使我们隔离出两个潜在的风险来源。第一个与有效的价格过程有关。第二个是新颖的,涉及真正的市场摩擦。我们将市场摩擦波动率定义为观察到的资产价格与基本价值之间的差额的波动率。我们提出了一种资产定价模型,该模型具有两个具有直观吸引力的波动性成分,即市场波动性和市场摩擦波动性,并研究了其解释已记录横截面资产价格异常的程度。在月度频率上,我们发现两种波动率度量在股票收益的横截面中均被负定价,并且它们与规模异常密切相关。在价值分类的投资组合中,由于有效的价格波动而产生的小负大溢价每年高达3%,而由于市场摩擦波动性而产生的溢价每年高达8%。在每日股票收益的横截面中,市场波动率和市场摩擦波动率也被负面定价。有趣的是,在包含波动率测度和每日市场收益的三因素模型中,市场摩擦波动性似乎很大程度上包含了市场波动性中包含的信息。在各种按价值分类的投资组合中,其溢价每年高达10%。

著录项

  • 作者

    Moise, Elena-Claudia.;

  • 作者单位

    The University of Chicago.;

  • 授予单位 The University of Chicago.;
  • 学科 Business Administration General.; Economics Finance.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 113 p.
  • 总页数 113
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 贸易经济;财政、金融;
  • 关键词

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