Rare Disasters and Asset Markets in the 20th Century

罕见的经济灾难的前景对股票、债券和其他资产市场产生重大影响。 该项目将这些灾害纳入一个模型。 该分析假设发生产量大幅度福尔斯的灾难的可能性很低。如果发生经济灾难，还有另一种可能性，即部分债务违约，通常是通过战时的高通胀。灾难风险可以解释许多金融难题，包括安全资产的低回报率、高股权溢价和股票回报的高波动性。该模型还可以解释为什么预期的真实的利率在美国内战期间很低，并持续到9月11日之后。这些结果来自一个易于处理的框架，允许分析解决方案。 研究人员使用20世纪世纪全球历史来衡量与经济灾害有关的模型参数。这段历史主要是由第一次世界大战，大萧条，第二次世界大战和二战后经济萧条以外的经合组织。在35个国家和100年的数据中，有60个国家的人均GDP短期收缩幅度在15%到64%之间。根据这一模式，研究人员假设每年发生灾害的概率为1.7%，灾害规模的频率分布与观察到的分布相匹配。条件违约概率设定为40%，以适应战时经济灾难期间观察到的低票据回报率频率。违约的条件规模被设定为等于收缩的规模，以复制在这些情况下股票和票据平均回报率的相似性。 研究人员使用这些灾害参数和其他与GDP增长和家庭偏好相关的参数来校准模型。模拟模型的预测在许多方面与股票和票据的历史回报率雅阁，最重要的下一步是扩展模型，使其包含灾难概率pt的随机、持续变化。这一扩展应有助于解释股票价格的波动性和真实的利率和市盈率的时间序列实现。一个主要的实证项目是测量pt，并将这些值与资产回报和消费联系起来。衡量感知灾难概率的想法包括股票市场的期权价格、保险费、博彩市场的合同价格以及原子钟中的“午夜前几分钟”。更多的信息将来自pt与各种资产价格和回报率的理论关系，包括黄金价格、真实的利率、市盈率、期权价格和真实的房地产价格。另一个重要项目是收集七国集团国家和其他几个有数据的国家长期消费支出和资产收益的年度数据。这些信息被用来评估灾害的概率和规模，并衡量灾害对人均国内生产总值水平的持续影响程度。进一步的分析致力于解释投资和经济增长率，并区分全球和局部干扰。该分析也适用于开放经济体，以理解与利率平价条件相关的难题。更广泛的影响：中心思想是，罕见灾难的可能性在一个易于处理的模型中解释了许多金融难题。这些灾难不仅包括经济萧条和战争，还包括最近讨论的自然灾害卡特里娜飓风、印度洋海啸和禽流感的更大版本。金融难题包括高股权溢价、相对安全资产的低回报率、股票价格的波动以及美国大部分战争期间的低真实的利率。关键在于，灾难风险的增加会提高对政府票据等安全资产的需求，从而降低这些资产的真实的利率。如果该项目的观点是正确的，罕见灾害框架可能成为宏观经济和金融研究人员使用的标准分析的基本部分。

Prospects for rare economic disasters have major effects on markets for stocks, bonds, and other assets. This project incorporates these disasters into a model. The analysis assumes a low probability of a disaster in which output falls by a substantial proportion. Contingent on economic disaster, there is another probability of partial default on bills, usually through high wartime inflation. Disaster risks can explain a number of financial puzzles, including the low rate of return on safe assets, the high equity premium, and the high volatility of stock returns. The model can also explain why expected real interest rates were low during U.S. wars back to the Civil War and continuing to the post-September 11th period. These results come from a tractable framework that allows for analytical solutions. The investigator uses the 20th century global history to gauge model parameters related to economic disasters. This history is dominated by WWI, the Great Depression, WWII, and post-WWII depressions outside the OECD. For 35 countries and 100 years of data, there were 60 short-term contractions of per capita GDP in the range between 15% and 64%. Based on this pattern, the investigator assumes a disaster probability of 1.7% per year and a frequency distribution of disaster sizes that matches the observed distribution. The conditional default probability is set at 40% to fit the observed frequency of low bill returns during wartime economic disasters. The conditional size of default is set to equal the size of contraction to replicate the similarity in average returns on stocks and bills in these circumstances. The investigator calibrates the model using these disaster parameters and other parameters related to GDP growth and household preferences. The simulated model's predictions accord in many respects with the history of returns on stocks and bills.The most important next step is to extend the model to incorporate random, persisting variations in the disaster probability, pt. This extension should help to explain the volatility of stock prices and the time series realizations of real interest rates and price-earnings ratios. A major empirical project is to measure pt and to relate these values to asset returns and consumption. Ideas for measuring perceived disaster probabilities include options prices on stock markets, insurance premia, contract prices in betting markets, and the "minutes to midnight" in the atomic clock. Additional information will come from the theoretical relation of pt to various asset prices and rates of return, including gold prices, real interest rates, price earnings ratios, options prices, and real estate prices. Another important project is the assembly of annual data on consumer expenditure and asset returns over long periods for the G7 countries and for a few other countries with available data. This information is used to assess disaster probabilities and sizes and to gauge the extent to which disasters have persisting influences on levels of per capita GDP. Further analysisis devoted to explaining rates of investment and economic growth and to distinguishing global from local disturbances. The analysis is also applied to open economies to understand puzzles related to interest-rate parity conditions.Broader Impacts: The central idea is that the potential for rare disasters explains a lot of financial puzzles within a tractable model. The disasters include not only depressions and wars but larger versions of recently discussed natural disasters hurricane Katrina, the Indian Ocean tsunami, and avian flu. The financial puzzles include a high equity premium, low rate of return on comparatively safe assets, volatility of stock prices, and low real interest rates during most U.S. wars. The key point is that a heightened disaster risk raises the demand for safe assets, such as government bills, and thereby lowers real interest rates on these assets. If the project's perspective is correct, the rare-disasters framework could become a basic part of standard analyses used by researchers in macroeconomics and finance.