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Semi-Parametric Estimation of Risk-Return Relationships

Semi-Parametric Estimation of Risk-Return Relationships
Author: Juan Carlos Escanciano
Publisher:
Total Pages: 30
Release: 2013
Genre:
ISBN:
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This article proposes semi-parametric least squares estimation of parametric risk-return relationships, i.e. parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of unobservable parametric factors. A distinctive feature of our estimator is that it does not require a parametric model for the conditional mean and variance. We establish consistency and asymptotic normality of the estimates. The theory is non-standard due to the presence of estimated factors. We provide simple sufficient conditions for the estimated factors not to have an impact in the asymptotic standard error of estimators. A simulation study investigates the nite sample performance of the estimates. Finally, an application to the CRSP value-weighted excess returns highlights the merits of our approach. In contrast to most previous studies using non-parametric estimates, we find a positive and significant price of risk in our semi-parametric setting.

A Risk Return Relation in Stock Markets

A Risk Return Relation in Stock Markets
Author: Napon Hongsakulvasu
Publisher:
Total Pages:
Release: 2015
Genre:
ISBN:
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In this paper, I propose a new semi-parametric GARCH-in-Mean model. Since many empirical papers have the mix results on the risk-return relation, the cause of problem may come from the misspecification of conditional mean equation or conditional variance equation or both of them. My model uses non-parametric estimation in conditional mean equation and semi-parametric estimation in conditional variance equation which allows the non-linear risk return relation in conditional mean equation and allows the non-linear relation between the volatility and the cumulative sum of exponentially weighted past returns. Three parameters on my model are GARCH parameter, the leverage effect parameter and leptokurtic parameter. I also extend my model to include four exogenous variables, dividend yield, term spread, default spread and momentum into conditional mean equation by using additive model which allows each variable to have non-linear relation with the return. An empirical study on S&P 500 suggests that risk has a small affect on market return. However, when four exogenous variables are added to the model, my model shows that the risk-return relation has a positive hump shape. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/155545

Risk-Return Relationship and Portfolio Management

Risk-Return Relationship and Portfolio Management
Author: Raj S. Dhankar
Publisher: Springer Nature
Total Pages: 323
Release: 2019-10-24
Genre: Business & Economics
ISBN: 8132239504
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This book covers all aspects of modern finance relating to portfolio theory and risk–return relationship, offering a comprehensive guide to the importance, measurement and application of the risk–return hypothesis in portfolio management. It is divided into five parts: Part I discusses the valuation of capital assets and presents various techniques and models used in this context. Part II then addresses market efficiency and capital market models, particularly focusing on measuring market efficiency, which is a crucial factor in making correct investment decisions. It also analyzes the major capital market models like CAPM and APT to determine to what extent they are suitable for use in developing economies. Part III highlights the significance of risk–return analysis as a prerequisite for investment decisions, while Part IV examines the selection and performance appraisals of portfolios against the backdrop of the risk–return relationship. It also examines new tools such as the value-at-risk application for mutual funds and the applications of the price-to-earnings ratio in portfolio performance measurement. Lastly, Part V explores contemporary issues in finance, including the relevance of Islamic finance in the increasingly volatile global financial system.

Semi-Parametric Estimation of Factor Risk-Premia

Semi-Parametric Estimation of Factor Risk-Premia
Author: Maziar Kazemi
Publisher:
Total Pages: 43
Release: 2019
Genre:
ISBN:
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This paper shows that factor risk premia can be consistently estimated using a semi-parametric estimate of the stochastic discount factor without requiring a correctly specified linear factor model. We use a minimum discrepancy objective function to construct a stochastic discount factor from asset returns using only the economic assumption of no arbitrage. The stochastic discount factor and factor risk-premia are estimated using only data on portfolio returns and factor realizations: The same data used when evaluating linear models. The econometrics are applications of standard extremum estimator arguments and the Delta Method, making inference simple. In simulations, the estimated risk-premia have low root mean squared errors and are comparable to classic two-pass estimates even when the model is correctly specified. Empirical estimates of popular traded factors are close to their mean excess returns. For non-traded factors, we find that intermediary leverage and consumption growth carry risk-premia, while employment growth does not. A final application shows that the estimated risk-premia can be used as an extra moment condition to discipline the creation of factor mimicking portfolios.

Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes)

Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes)
Author: Cheng Few Lee
Publisher: World Scientific
Total Pages: 5053
Release: 2020-07-30
Genre: Business & Economics
ISBN: 9811202400
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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.