Essays on Return Predictability and Volatility Estimation PDF Download
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Author: Yuzhao Zhang
Publisher:
ISBN:
Category : Investments
Languages : en
Pages : 316
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Book Description
Author: Yuzhao Zhang
Publisher:
ISBN:
Category : Investments
Languages : en
Pages : 316
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Book Description
Author: Bradley Steele Paye
Publisher:
ISBN:
Category : Asset allocation
Languages : en
Pages : 380
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Book Description
Author: Shu Yan
Publisher:
ISBN:
Category : Stock exchanges
Languages : en
Pages : 310
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Book Description
Author: Thomas B. Fomby
Publisher: Emerald Group Publishing
ISBN: 1784411825
Category : Political Science
Languages : en
Pages : 772
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Book Description
This volume honors Professor Peter C.B. Phillips' many contributions to the field of econometrics. The topics include non-stationary time series, panel models, financial econometrics, predictive tests, IV estimation and inference, difference-in-difference regressions, stochastic dominance techniques, and information matrix testing.
Author: Pascal Alphonse
Publisher: Springer Nature
ISBN: 303129050X
Category :
Languages : en
Pages : 344
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Book Description
Author: Amit Goyal
Publisher:
ISBN:
Category : Stocks
Languages : en
Pages : 374
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Book Description
Author: Rita Biswas
Publisher: Emerald Group Publishing
ISBN: 1789733898
Category : Business & Economics
Languages : en
Pages : 167
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Book Description
This volume, dedicated to John W. Kensinger, explores a variety of topics in financial economics, including firm growth, investment risks, and the profitability of the banking industry. With its global perspective, Essays in Financial Economics is a valuable addition to the bookshelf of any researcher in finance.
Author: Niels Haldrup
Publisher: OUP Oxford
ISBN: 0191669547
Category : Business & Economics
Languages : en
Pages : 393
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Book Description
This edited collection concerns nonlinear economic relations that involve time. It is divided into four broad themes that all reflect the work and methodology of Professor Timo Teräsvirta, one of the leading scholars in the field of nonlinear time series econometrics. The themes are: Testing for linearity and functional form, specification testing and estimation of nonlinear time series models in the form of smooth transition models, model selection and econometric methodology, and finally applications within the area of financial econometrics. All these research fields include contributions that represent state of the art in econometrics such as testing for neglected nonlinearity in neural network models, time-varying GARCH and smooth transition models, STAR models and common factors in volatility modeling, semi-automatic general to specific model selection for nonlinear dynamic models, high-dimensional data analysis for parametric and semi-parametric regression models with dependent data, commodity price modeling, financial analysts earnings forecasts based on asymmetric loss function, local Gaussian correlation and dependence for asymmetric return dependence, and the use of bootstrap aggregation to improve forecast accuracy. Each chapter represents original scholarly work, and reflects the intellectual impact that Timo Teräsvirta has had and will continue to have, on the profession.
Author: Xiaohong Chen
Publisher: Springer Science & Business Media
ISBN: 1461416531
Category : Business & Economics
Languages : en
Pages : 582
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Book Description
This book is a collection of articles that present the most recent cutting edge results on specification and estimation of economic models written by a number of the world’s foremost leaders in the fields of theoretical and methodological econometrics. Recent advances in asymptotic approximation theory, including the use of higher order asymptotics for things like estimator bias correction, and the use of various expansion and other theoretical tools for the development of bootstrap techniques designed for implementation when carrying out inference are at the forefront of theoretical development in the field of econometrics. One important feature of these advances in the theory of econometrics is that they are being seamlessly and almost immediately incorporated into the “empirical toolbox” that applied practitioners use when actually constructing models using data, for the purposes of both prediction and policy analysis and the more theoretically targeted chapters in the book will discuss these developments. Turning now to empirical methodology, chapters on prediction methodology will focus on macroeconomic and financial applications, such as the construction of diffusion index models for forecasting with very large numbers of variables, and the construction of data samples that result in optimal predictive accuracy tests when comparing alternative prediction models. Chapters carefully outline how applied practitioners can correctly implement the latest theoretical refinements in model specification in order to “build” the best models using large-scale and traditional datasets, making the book of interest to a broad readership of economists from theoretical econometricians to applied economic practitioners.
Author: Mr. Gaizka Ormazabal Sanchez
Publisher: Stanford University
ISBN:
Category :
Languages : en
Pages : 185
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Book Description
This dissertation comprises three papers on the governance of corporate risk: 1. The first paper investigates the role of organizational structures aimed at monitoring corporate risk. Proponents of risk-related governance structures, such as risk committees or Enterprise Risk Management (ERM) programs, assert that risk monitoring adds value by ensuring that corporate risks are managed. An alternative view is that such governance structures are nothing more than window-dressing created in response to regulatory or public pressure. Consistent with the former view, I find that, in the period between 2000 and 2006, firms with more observable risk oversight structures exhibit lower equity and credit risk than firms with fewer or no observable risk oversight structures. I also provide evidence that firms with more observable risk oversight structures experienced higher returns during the worst days of the 2007-2008 financial crisis and were less susceptible to market fluctuations than firms with fewer or no observable risk oversight structures. Finally, I find that firms without observable risk oversight structures experienced higher abnormal returns to recent legislative events relating to risk management than firms with observable risk oversight structures. 2. The most common empirical measure of managerial risk-taking incentives is equity portfolio vega (Vega), which is measured as the dollar change in a manager's equity portfolio for a 0.01 change in the standard deviation of stock returns. However, Vega exhibits at least three undesirable features. First, Vega is expressed as a dollar change. This implicitly assumes that managers with identical Vega have the same incentives regardless of differences in their total equity and other wealth. Second, the small change in the standard deviation of returns used to calculate Vega (i.e., 0.01) yields a very local approximation of managerial risk-taking incentives. If an executive's expected payoff is highly nonlinear over the range of potential stock price and volatility outcomes, a local measure of incentives is unlikely to provide a valid assessment of managerial incentives. Third, Vega is measured as the partial derivative of the manager's equity portfolio with respect to return volatility. This computation does not consider that this partial derivative also varies with changes in stock price. The second paper develops and tests a new measure of managerial risk-taking equity incentives that adjusts for differences in managerial wealth, considers more global changes in price and volatility, and explicitly considers the impact of stock price and volatility changes. We find that our new measure exhibits higher explanatory power and is more robust to model specification than Vegafor explaining a wide range of measures of risk-taking behavior. 3. The third paper examines the relation between shareholder monitoring and managerial risk-taking incentives. We develop a stylized model to show that shareholder monitoring mitigates the effect of contractual risk-taking incentives on the manager's actions. Consistent with the model, we find empirically that the positive association between the CEO's contractual risk-taking incentives and risk-taking behavior decreases with the level of shareholder monitoring. Furthermore, consistent with the board anticipating and optimally responding to shareholder monitoring, boards of firms exposed to more intense monitoring design compensation contracts that provide higher incentives to take risks. Overall, our results suggest that, when evaluating risk-taking incentives provided by a compensation contract, it is important to account for the firm's monitoring environment.
