股票市场经济活动的行为方式.doc

毕业论文（设计）外文翻译外文题目：Approaches to Stock Market and Economic Activity 出 处：Asset Price，Booms and Recessions Financial Economics Form a Dynamic Persepective 作 者：Mohamed EI Hedi Arouri, Fredj Jawadi Approaches to Stock Market and Economic Activity1.IntroductionThe interaction of the stock market and economic activity has recently become an important topic in empirical finance as well as in macroeconomic research. The research has pursued two directions. A large number of papers have studied the impact of the stock market on real activity. Here particular emphasis is given to the relationship of the volatility of the stock market and output. The research studies the impact of wealth, as evaluated on the stock market, on borrowing, lending and spending behavior of banks, firms and households. The argument may go like this. An increase in wealth through the appreciation of stocks increases spending directly since people feel wealthier. At the same time the appreciation of stocks increases the collateral for borrowing by firms and households. Credit may expand and thus spending is likely to increase. A depreciation of stocks lets spending decrease and devaluates the collateral and credit contractions followed by large output loss may occur. Thus, large stock price swings can easily be seen to impact economic activity. Often Tobins Q is employed to study this impact of stock market appreciation or depreciation on firm investment. Of course, other financial variables such as interest rates, interest rate spreads, the term structure of the interest rates and credit constraints, as discussed in Chaps. They are also important for household and firm spending. Thus, beside the real variables, asset prices and financial variables are also important for economic activity and, moreover, have often been good predictors for turning points in economic activity and business cycles.On the other hand, another important line of research is to show how real activity affects asset prices and returns. Often, proxies for economic fundamentals are employed to show that fundamentals drive stock prices and returns. The two main important variables for stock prices are the expected cash flows (and dividend payments) of firms and discount rates. Both are supposed to determine asset prices in a fundamental way. Empirical researchers have used numerous macroeconomic variables as proxies for news on expected returns, future cash flows and discount rates. In addition variables with leads and lags are studied for their impact on asset pricing and returns. In general, econometric literature has shown that good predictors of stock prices and returns have proved to be dividends, earnings and growth rate of real output Moreover, financial variables such as interest rate spread and the term structure of interest rates have also been significant in predicting stock prices and stock returns .Other balance sheet variables, such as firms leverage ratio, net worth and liquidity have also successfully been employed Presently discussed approaches in the empirical literature have primarily stressed either of the above mentioned two strands of research. Subsequently we will present some approaches, the relevant stylized facts for those approaches and some empirical results of the studies. Thereafter,we will presentmodels that deal with the interaction of macroeconomic factors and the stock market.We will also discuss some empirical results on such models as well. 2.The Intertemporal ApproachIn the above table we present summary statistics of time series for U.S. and Europe on GNP, consumption, investment, employment, the treasury bill rate, equity return and the Sharpe-ratio. The latter measure of financial market performance has recently become a quite convenient measure to match theory and facts, since, as a measure of the risk-return trade-off, the Sharpe-ratio captures both excess returns and excess volatility.85 Yet, we want to mention that the Sharpe-ratio might also be time varying. This will be discussed in Chaps. As shown in table 5.1, the hierarchy of volatility measured by the standard deviation is common for U.S. as well as European data. As shown, stock returns exhibit the strongest volatility. The second strongest volatility is exhibited by investment followed by consumption. Employment has the lowest volatility. In addition, as can be seen for U.S. as well as European data, the equity return carries an equity premium as compared to the risk free interest rate. This excess return was first stated by Mehra and Prescott (1985) as the equity premium puzzle. As can be observed the market return by far exceeds the return from the risk-free rate. As shown in a variety of recent papers,86 the intertemporalmodels, in particular the RBC model insufficiently explains the equity premium and the excess volatility of equity return and thus the Sharpe-ratio. Standard RBC asset market models employ the Solow-residual as technology shocks or impulse dynamics. For a given variance In sum, for the actual time series compared, for example, with the standard RBC model, we observe a larger equity return and stronger volatility of equity prices in contrast to the risk-free rate. These two facts are measured by the Sharpe-ratio which cannot be matched by the standard RBC model.87 Moreover, it is worth noting that in the stochastic growth model there is only a one-sided relationship. Real shocks affect stock prices and returns but shocks to asset prices or overreaction of asset prices relative to changes in fundamentals have no effects on real activity. The asset market is always cleared and there are no feedback mechanisms to propagate financial shocks to the real side.3. The Excess Volatility TheoryOther theories and macro econometric studies depart from the market efficiency hypothesis and pursue the overreaction hypothesiswhen employing macro variables as predictors for stock prices and stock returns (Shiller 1991, Summers 1986, Poterba and Summers 1988). Moreover, in this tradition the role of monetary, fiscal and external shocks are seen to be relevant. Although in the long run stock prices may revert to their mean as determined by macroeconomic proxies of fundamentals in the short-run, speculative forces and the interaction of trading strategies of heterogeneous.4.Heterogeneous Agents ModelsIn recent times numerous researchers have developed models of heterogeneous agents and heterogeneous expectations to explain waves of optimismand pessimism, excess volatility of the above mentioned type and the statistical properties that characterizes asset price dynamics such as volatility clustering and time varying volatility. In principle models of heterogeneous expectations are well suited to explain those phenomena. Yet there are also some short comings of those models, As indicated above, although the heterogeneous trading strategies of the different groups of investors may generate overshooting and quite complex asset price dynamics, we want to point out that it is presumably the interaction of the trading strategies and the varying perception of what the fundamentals are and what their trend is which explains the actual asset price dynamics.5. The VAR MethodologyMoreover, a more complete VAR study of the stock market and its interaction with other variables may also take into account inflation rates and exchange rates. Regime Change Models Overall, one might argue that the VAR methodology is strong in capturing lead and lag patterns in the interaction of the variables but it does not reveal important structural relations, in particular if nonlinearities prevail in the interaction of the variables. Moreover, dynamic macro models may be needed to provide some rationale for the use of structural relationships and to highlight relevant restrictions on empirical tests.6.Regime Change ModelsThere is some econometric work on the nonlinear interaction of stock market and output. The major type of models are built on Hamiltons regime change models. The Hamilton idea (Hamilton, 1989) that output follows two different autoregressive processes depending on whether the economy is in an expanding or contracting regime, is extended to a study of the stock market in Hamilton and Lin (1996). The above mentioned studies of threshold (or business cycle) dependent volatility points to the possibility that returns and volatility may not be constant but time varying, i.e. vary with the business cycle. This gives rise to the conjecture that the above stated assumption in Chap.of a constant risk-free rate, equity premium and Sharpe-ratio often referred to in RBC models might not be quite correct. One should rather attempt to match models with time varying financial characteristics such as equity premium and Sharpe-ratio. 7. ConclusionsOur review of empirical approaches to study the interaction of stock prices and output or in some cases stock prices, other financial variables and output should be viewed as an introduction to modelling asset markets and economic activity. In Chap. 6 macro factors impacting stock prices are studied and a macro model that takes account of the interaction of macro variables and asset prices is introduced and empirical results reported. In Chap we explore the effects of new technology on asset prices and returns. Thereafter standard asset pricing models, in particular the capital asset pricing and intertemporal capital asset pricing models, are considered in detail and some estimation results are reported as well.译 文： 股票市场经济活动的行为方式1.简介在实证金融以及宏观经济研究中，股票的市场和经济活动的互动已成为一个重要课题。这项研究一直通过两个方向发展。在实际活动中大量的论文研究了股票市场的影响。这里特别强调的是对股票的市场和波动的关系。该研究研究了财富的影响，通过借款，贷款和银行的消费，公司和家庭的行为对股市的评价。参数可以是这样的。财富是通过增加股票升值来增加开支，因为人们直接觉得越来越富裕。在同一时间内，股票升值增加了企业和家庭借贷的抵押品。信用消费可能扩大，从而支出可能还会上升。股票贬值让支出的减少和抵押品贬值和信贷收缩，紧随其后的是大规模生产可能会出现亏损。因此，大型股票的价格波动很容易被视为影响经济活动。通常Tobins Q是用来研究这个市场的股票升值或贬值对企业投资的影响。当然还有其他金融变量，如文章讨论的利率，利率差，对利率和期限结构的信贷约束。下面的部分也说明家庭和企业支出的重要。因此，真正的变量，资产价格波动和金融变量对经济活动也很重要，此外，通常是预测经济活动的转折点和商业周期。另一方面，另一个重要的方向研究是指如何真正展示经济活动对资产价格和回报的影响。通常情况下，是用经济基本面代理来表明，基本面驱动股价和回报。两个主要的股票价格的重要变量是预期现金流量和贴现率的公司（和股息支付）。两者都应该确定资产价格的根本途径。实证的研究人员使用众多宏观经济变量。作为代表预期回报，未来现金流量和贴现率的信息。此外变量和滞后变量是其对资产价格和收益的影响研究。一般来说，计量经济学文献表明，通过良好的预测股票价格和收益后得到的股息，盈利和实际产出增长速率，此外，诸如利率差和利率期限结构的财务变数也已显着的预测股票价格和股票收益率。其他资产负债表的变量，如企业的杠杆比率，净资产和流动性成功地运作。目前讨论的实证文献方法，主要强调上述两股的研究。随后，我们将提出一些方法，有关的典型事实对那些方法和一些实证结果研究。此后，我们将目前的模式，随着宏观经济因素的互动和交易的股票市场。我们还将讨论这些模型的一些结果研究。2跨期的方法目前，最知名的做法是市场效率假设。从理论上讲它是基于资本资产定价模型（CAPM）及其扩展到多期消费为基础的资本资产定价模型（CCAPM）。在经济模式方面的研究人员，如今，常常为研究采用资产市场和真实之间的关系进行生产经济的活动，一个随机最优增长模型的RBC型是资本资产的定价模型。跨期决策是在RBC的方法的核心，这是自然的研究，因此在这种模型中的资产市场产出的相互作用，因为它也包括生产。这里的典型事实简短的总结中经常提到的这种方法以及实证调查结果，连接就足够了。跨期均衡模型通常衡量，如表1报告的典型事实。最近，它已成为惯例时间序列和历史时间序列的对比模型，并说明在某种程度上模型的时间序列可以匹配的历史数据。在第一和第二的时刻模型是需要匹配的和相关条款与产出的实际时间序列的统计规律。 在上面的表中，我们目前报告摘要的是美国和欧洲系列国民生产总值，消费，投资，就业，国库券利率，股东权益报酬率和夏普比率统计。近来后者测量的金融市场的表现已成为衡量一个相当方便的措施，匹配理论和事实，因为作为风险收益权衡的措施，夏普比率抓住超额收益和超额波动。然而，我们要提及的是夏普比率也可能随时间变化的。如表5.1所示，由标准差衡量波动性的层次结构是很常见的美国以及欧洲经济数据。如图所示，股票收益表现出强烈的波动。第二强的是表现最强的波动投资是消费。就业有最低的波动性。此外，我们可以看到美国以及欧洲经济数据、净资产收益率、股权溢价进行无风险利率。作为股权溢价之谜，这是种提供的金融贷款的超额回报。目前观察到的市场回报率远远超过了无风险回报率。总之，比较实际的时间序列，例如，与标准RBC模型，相对于无风险利率，我们看到一个更大的股本回报率和较强的股票价格波动。这两个事实是夏普比率不能用标准RBC匹配测量模型。此外，值得注意的是，在随机增长模型只有一个片面的关系。真正的冲击影响股票价格和回报，但对资产价格的冲击 或过度反应的资产价格基本面变化，对实际经济活动没有影响。资产市场始终为零，也没有反馈机制来传播冲击金融真实的一面。3超额波动理论其他理论和宏观计量经济学研究偏离市场效率假说和追求过度反应假说用宏观变量作为预测股票价格和股票收益。此外，在这个传统的角色中，财政和货币政策外部冲击的作用，被认为是相关的。对短期宏观经济而言，虽然从长远来看股票价格可能恢复到他们的平均代理的