Author: Stefano GrassiPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Asset Pricing Using Block-Cholesky GARCH and Time-varying Betas
Author: Stefano GrassiAsset Pricing with Time Varying Volatility
Author: Victor NgAsset Pricing Models with Conditional Betas and Alphas
Author: Wayne E. FersonPublisher:
ISBN:
Category :
Languages : en
Pages : 38
Book Description
This paper studies the estimation of asset pricing model regressions with conditional alphas and betas, focusing on the joint effects of data snooping and spurious regression. We find that the regressions are reasonably well specified for conditional betas, even in settings where simple predictive regressions are severely biased. However, there are biases in estimates of the conditional alphas. When time-varying alphas are suppressed and only time-varying betas are considered, the betas become baised. Previous studies overstate the significance of time-varying alphas.
Time-Varying Betas Help in Asset Pricing
Author: Aslihan Altay SalihPublisher:
ISBN:
Category :
Languages : en
Pages : 24
Book Description
Although there is a consensus about time variation in market betas, it is not clear how this variation should be captured. Several researchers continue to analyze different versions of the conditional CAPM. However, Ghysels (1998) shows that these conditional CAPM models fail to capture the dynamics of beta risk. In this study, we introduce a new model, threshold CAPM, which outperforms both the conditional and unconditional CAPMs by generating smaller pricing errors. We also show that the beta risk changes through time with the changes in the economic environment and the dynamics of time variation of beta differ across industries. These findings have important implications for asset allocation, portfolio selection, and hedging decisions.
Conditional Asset Pricing - Predicting Time Varying Beta-Factors with Group Method of Data Handling Methods
Author: Sebastian SchneiderPublisher:
ISBN:
Category :
Languages : en
Pages : 27
Book Description
Allowing for time-varying risk premia yields sophisticated asset pricing models, but the search for adequate model specifications is more challenging. We introduce, to our knowledge, previously in conditional asset pricing not used Group Method of Data Handling (GMDH) that rests on sorting out requiring statsitical models for complex problems of unknown structure but does not require a model to predict conditional variation in betas. We find that lagged instruments used to proxy for expected returns in conditional asset pricing provide a challenge not only for the unconditional CAPM but also the Fama-French-model. Thereby non-linear GMDH-algorithms challenge traditional models of conditional asset pricing as we find a highly non-linear influence of lagged instruments on both conditional alphas and betas. Therefore, predetermining a structure for functional relationships between conditional alphas as well as betas and lagged instruments may lead to a significant misspecification of asset pricing models.
Asset Pricing Models with Conditional Betas and Alphas
Author: Wayne E. FersonPublisher:
ISBN:
Category : Assets (Accounting)
Languages : en
Pages : 31
Book Description
This paper studies the estimation of asset pricing model regressions with conditional alphas and betas, focusing on the joint effects of data snooping and spurious regression. We find that the regressions are reasonably well specified for conditional betas, even in settings where simple predictive regressions are severely biased. However, there are biases in estimates of the conditional alphas. When time-varying alphas are suppressed and only time-varying betas are considered, the betas become baised. Previous studies overstate the significance of time-varying alphas.
Asset Pricing with a Factor Arch Covariance Structure
Author: Robert F. EnglePublisher:
ISBN:
Category :
Languages : en
Pages : 36
Book Description
Asset pricing relations are developed for a vector of assets with a time varying covariance structure. Assuming that the eigenvectors are constant but the eigenvalues changing, both the Capital Asset Pricing Model and the Arbitrage Pricing Theory suggest the same testable implication: the time varying part of risk premia are proportional to the time varying eigenvalues. Specifying the eigenvalues as general ARCH processes. the model is a multivariate Factor ARCH model. Univariate portfolios corresponding to the eigenvectors will have (time varying) risk premia proportional to their own (time varying) variance and can be estimated using the GARCH-M model. This structure is applied to monthly treasury bills from two to twelve months maturity and the value weighted NYSE returns index. The bills appear to have a single factor in the variance process and this factor is influenced or quot;caused in variancequot; by the stock returns.
Tests of Asset Pricing with Time-varying Expected Risk Premiums and Market Betas
Author: Wayne FersonPublisher:
ISBN:
Category : Investments
Languages : en
Pages : 20
Book Description
Cross-Sectional Asset Pricing with Individual Stocks
Author: Tarun ChordiaPublisher:
ISBN:
Category :
Languages : en
Pages : 61
Book Description
We develop a methodology for bias-corrected return-premium estimation from cross-sectional regressions of individual stock returns on betas and firm characteristics. Over the period 1963-2014, there is some evidence of a negative premium on the size factor and positive beta premiums for the profitability and investment factors as well as the market factor (though not for the CAPM). There is no pricing evidence for the book-to-market and momentum factors with all characteristics included. Characteristics consistently explain a much larger proportion of variation in estimated expected returns than factor loadings, even with time-varying return premia.
Recent Econometric Techniques for Macroeconomic and Financial Data
Author: Gilles DufrénotPublisher: Springer Nature
ISBN: 3030542521
Category : Business & Economics
Languages : en
Pages : 387
Book Description
The book provides a comprehensive overview of the latest econometric methods for studying the dynamics of macroeconomic and financial time series. It examines alternative methodological approaches and concepts, including quantile spectra and co-spectra, and explores topics such as non-linear and non-stationary behavior, stochastic volatility models, and the econometrics of commodity markets and globalization. Furthermore, it demonstrates the application of recent techniques in various fields: in the frequency domain, in the analysis of persistent dynamics, in the estimation of state space models and new classes of volatility models. The book is divided into two parts: The first part applies econometrics to the field of macroeconomics, discussing trend/cycle decomposition, growth analysis, monetary policy and international trade. The second part applies econometrics to a wide range of topics in financial economics, including price dynamics in equity, commodity and foreign exchange markets and portfolio analysis. The book is essential reading for scholars, students, and practitioners in government and financial institutions interested in applying recent econometric time series methods to financial and economic data.