Intertemporal Asset Pricing

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In the mid-eighties Mehra and Prescott showed that the risk premium earned by American stocks cannot reasonably be explained by conventional capital market models. Using time additive utility, the observed risk pre mium can only be explained by unrealistically high risk aversion parameters. This phenomenon is well known as the equity premium puzzle. Shortly aft erwards it was also observed that the risk-free rate is too low relative to the observed risk premium. This essay is the first one to analyze these puzzles in the German capital market. It starts with a thorough discussion of the available theoretical mod els and then goes on to perform various empirical studies on the German capital market. After discussing natural properties of the pricing kernel by which future cash flows are translated into securities prices, various multi period equilibrium models are investigated for their implied pricing kernels. The starting point is a representative investor who optimizes his invest ment and consumption policy over time. One important implication of time additive utility is the identity of relative risk aversion and the inverse in tertemporal elasticity of substitution. Since this identity is at odds with reality, the essay goes on to discuss recursive preferences which violate the expected utility principle but allow to separate relative risk aversion and intertemporal elasticity of substitution.

This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.

Seminar paper from the year 2018 in the subject Economics - Finance, grade: 1.7, University of Duisburg-Essen (Faculty of Business and Economics), language: English, abstract: The Capital Asset Pricing Model (CAPM), which is developed by Harry Markowitz, lacks on empirical validation and is not economically fully plausible. By only considering a single period within the CAPM, Merton tried to improve the model by implementing different intertemporal assumptions. This paper focuses on the analysis, if the lack of the CAPM can be improved by using the assumptions of the ICAPM and if the eight investigated models are in the sense of Merton's assumptions. The first chapter reviews a short explanation of the classical CAPM and his critics, followed by Merton's intertemporal CAPM and his assumptions in the next chapter. Additionally, there were models developed, trying to be economically plausible by considering the ICAPM main assumptions, which are presented in the second chapter. A different way to develop an empirical better fitting CAPM is by using empirical motivated state variables. Fama & French started to take this approach by developing the three-factor-model (FF3). A lot of researchers were influenced by the FF3 and made their own version of a multifactor model by implementing variables. Even Fama & French enhanced their three-factor-model by adding further variables. In the third section there is the forecasting power of the four ICAPM models and the four empirical motivated multifactor models on the US market data and on the European market data compared. Then follows an examination if these models can be determined in the sense of the ICAPM restrictions. The last chapter concludes the results.