This paper presents the classic-static beta values and beta values estimated by an asymmetric beta model. In asymmetric model we have the possibility to estimate the upside and downside betas, while in the static model we are not able to work it out. We will estimate the static and asymmetric betas of two stocks in France Exchange stock market, Michelin and Tf1. So the data consists of daily returns of France Exchange stock market index CAC-40 and the above two stocks , during the period June 2nd of 2000 to May 17th of 2004. Actually this paper examines the estimation of betas under bull and bear market conditions. Asymmetries are of substantial economic importance for an investor who has symmetric beliefs, so he must switch his beliefs in an asymmetry one, where this is necessary.
Table of Contents
1. Abstract
2. Introduction
3. Summary statistics
4. Data
5. Methodology
6. Results
7. Conclusions
8. References
Research Objectives and Key Topics
The primary objective of this study is to evaluate the reliability of the classic static beta model compared to an asymmetric beta model. By analyzing the daily returns of two stocks, Michelin and TF1, from the French CAC-40 index, the paper investigates whether risk measures differ significantly under bull and bear market conditions and whether asymmetric beta models provide better explanatory power for stock volatility.
- Comparison between static and asymmetric beta models
- Impact of market conditions (bull vs. bear) on stock risk
- Stationarity testing of financial time series data
- Application of four distinct market definitions for asymmetric beta estimation
- Statistical hypothesis testing of beta equality
Excerpt from the Book
2. Introduction
The classic-static beta model is probably not reliable, because it assumes that beta and so the stock or asset’s risk follows the same behavior as in the past, which is an unrealistic assumption (Bilbao et al., 2007). Bai and Perron (2003) found that there are strong evidences that in most, but not in all cases, there are asymmetries in betas, indicating that the risk measures can be different vary depending on market conditions. So the risk measure estimation and forecasting can be usually best feasible and reliable, using the asymmetric beta model and not the static. Also Levy (1974) proposed that beta may differ with market condition. This hypothesis was tested by Fabozzi and Francis (1977) using three different definitions for bull and bear markets, or for upside and downside betas. In the section of methodology we will refer the definitions that we will obtain to estimate the asymmetric beta model. Fabozzi and Francis in their study found, in all the three definitions, that the number of stocks with significant differences in bear and bull market betas is not higher than probability would predict.
Summary of Chapters
1. Abstract: Provides an overview of the paper, detailing the comparison between static and asymmetric beta models using French stock market data.
2. Introduction: Discusses the limitations of the classic beta model and reviews literature supporting the hypothesis that market conditions influence beta values.
3. Summary statistics: Presents statistical analysis and graphical evidence to confirm the stationarity and distribution characteristics of the selected stock returns.
4. Data: Describes the dataset, which consists of daily returns from the CAC-40 index and two specific stocks, Michelin and TF1, covering the period 2000–2004.
5. Methodology: Outlines the mathematical models used for estimation, including Sharpe's single-index model and the four specific definitions for classifying market states.
6. Results: Details the empirical findings of the regressions and the comparative performance of the models across the defined market states.
7. Conclusions: Summarizes the findings and notes that the null hypothesis of equality between static and asymmetric betas was generally not rejected.
Keywords
Asymmetric beta, Static beta, Market volatility, Bull market, Bear market, CAC-40, Financial time series, Risk measure, OLS estimation, Michelin, TF1, Sharpe model, Stationarity, Hypotheses testing
Frequently Asked Questions
What is the core focus of this research?
The paper examines whether the traditional static beta model is sufficient for risk estimation or if an asymmetric beta model, which distinguishes between bull and bear market conditions, is more accurate.
What are the primary subjects of the analysis?
The study analyzes the French Exchange stock market index CAC-40 and two specific companies: Michelin and TF1.
What is the main research question?
The research asks if downside and upside betas significantly differ from static beta and from each other, thereby providing better explanatory power for asset returns.
Which methodology is utilized?
The study uses Sharpe's single-index model and applies OLS regression to four different market definitions to estimate both static and asymmetric beta coefficients.
What does the main body of the work cover?
It covers data preparation, stationarity tests, the mathematical framework for asymmetric models, and empirical regression results for both stocks under varying market definitions.
Which keywords best describe this study?
Key terms include Asymmetric beta, Market volatility, Bull/Bear market conditions, CAC-40, and Financial time series analysis.
How is the stationarity of the data verified?
The authors use line graphs for visual inspection and perform formal Dickey-Fuller tests to confirm that stock levels are non-stationary while returns are stationary.
What are the four definitions used to classify bull and bear markets?
The definitions are: the simple method, up/down markets, substantially up/down days, and the peak and trough method.
What is the final conclusion regarding beta equality?
The study generally fails to reject the null hypothesis of equality, indicating that there are no significant differences between static, upside, and downside betas in most tested scenarios.
- Quote paper
- Eleftherios Giovanis (Author), 2008, A Study of Sharpe’s asymmetric beta model, Munich, GRIN Verlag, https://www.grin.com/document/146638
