Rolling regression technique and cross-sectional regression: A tool to analyze Capital Asset Pricing Model

2021 ◽  
Author(s):  
Sowmya Shetty ◽  
Janet Jyothi Dsouza ◽  
Iqbal Thonse Hawaldar
Keyword(s):  
Asset Pricing ◽  
Regression Technique ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Cross Sectional ◽  
Asset Pricing Model ◽  
Rolling Regression

scholarly journals Rolling regression technique and cross-sectional regression: A tool to analyze Capital Asset Pricing Model

2021 ◽  
Vol 18 (4) ◽  
pp. 241-251
Author(s):  
Soumya Shetty ◽  
Janet Jyothi Dsouza ◽  
Iqbal Thonse Hawaldar
Keyword(s):  
Asset Pricing ◽  
Stock Returns ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Cross Sectional ◽  
Asset Pricing Model ◽  
Business Environments ◽  
Rolling Regression

The Capital Asset Pricing Model (henceforth, CAPM) is considered an extensively used technique to approximate asset pricing in the field of finance. The CAPM holds the power to explicate stock movements by means of its sole factor that is beta co-efficient. This study focuses on the application of rolling regression and cross-sectional regression techniques on Indian BSE 30 stocks. The study examines the risk-return analysis by using this modern technique. The applicability of these techniques is being viewed in changing business environments. These techniques help to find the effect of selected variables on average stock returns. A rolling regression study rolls the data for changing the windows for every 3-month period for three years. The study modifies the model with and without intercept values. This has been applied to the monthly prices of 30 BSE stocks. The study period is from January 2009 to December 2018. The study revealed that beta is a good predictor for analyzing stock returns, but not the intercept values in the developed model. On the other hand, applying cross-section regression accepts the null hypothesis. α, β, β2 ≠ 0. Therefore, a researcher is faced with the task of finding limitations of each methodology and bringing the best output in the model.

scholarly journals The Conditional Capital Asset Pricing Model Revisited: Evidence from High-Frequency Betas

2020 ◽  
Vol 66 (6) ◽  
pp. 2474-2494 ◽  
Author(s):  
Fabian Hollstein ◽  
Marcel Prokopczuk ◽  
Chardin Wese Simen
Keyword(s):  
Asset Pricing ◽  
High Frequency ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Cross Sectional ◽  
Conditional Capm ◽  
Asset Pricing Model ◽  
Daily Data

When using high-frequency data, the conditional capital asset pricing model (CAPM) can explain asset-pricing anomalies. Using conditional betas based on daily data, the model works reasonably well for a recent sample period. However, it fails to explain the size anomaly as well as three out of six of the anomaly component excess returns. Using high-frequency betas, the conditional CAPM is able to explain the size, value, and momentum anomalies. We further show that high-frequency betas provide more accurate predictions of future betas than those based on daily data. This result holds for both the time-series and the cross-sectional dimensions. This paper was accepted by Karl Diether, finance.

The Capital Asset Pricing Model and Market Microstructure

CFA Digest ◽  
1999 ◽  
Vol 29 (4) ◽  
pp. 96-96
Author(s):  
Frank T. Magiera
Keyword(s):  
Asset Pricing ◽  
Market Microstructure ◽  
Capital Asset Pricing Model ◽  
Capital Asset Pricing ◽  
Pricing Model ◽  
Capital Asset ◽  
Asset Pricing Model
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