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Is There a Risk-Return Tradeoff? Evidence from High-Frequency Data

Is There a Risk-Return Tradeoff? Evidence from High-Frequency Data
Author: Turan G. Bali
Publisher:
Total Pages: 39
Release: 2012
Genre:
ISBN:

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This paper examines the intertemporal relation between risk and return for the aggregate stock market using high-frequency data. We use daily realized, GARCH, implied, and range-based volatility estimators to determine the existence and significance of a risk-return tradeoff for several stock market indices. We find a positive and statistically significant relation between the conditional mean and conditional volatility of market returns at the daily level. This result is robust to alternative specifications of the volatility process, across different measures of market return and sample periods, and after controlling for macroeconomic variables associated with business cycle fluctuations. We also analyze the risk-return relationship over time using rolling regressions, and find that the strong positive relation persists throughout our sample period. The market risk measures adopted in the paper add power to the analysis by incorporating valuable information, either by taking advantage of high frequency intraday data (in the case of realized, GARCH, and range volatility) or by utilizing the market's expectation of future volatility (in the case of implied volatility index).


Risk-Return Relationship in High Frequency Data

Risk-Return Relationship in High Frequency Data
Author: Jihyun Lee
Publisher:
Total Pages: 57
Release: 2008
Genre:
ISBN:

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This study investigates the relationship between the return on a stock index and its volatility using high frequency data. Two well-known hypotheses are reexamined: the leverage effect and the volatility feedback effect hypotheses. In an analysis of the five-minute data from the Samp;P500 index, two distinct characteristics of high frequency data were found. First, it was noted that the sign of the relationship between the smallest wavelet scale components for return and volatility differs from those between other scale components. Second, it was found that long memory exists in the daily realized volatility. The study further demonstrates how these findings affect the risk and return relationship.In the regression of changes in volatility on returns, it was found that the leverage effect does not appear in intraday data, in contrast to the results for daily data. It is believed that the difference can be attributed to the different relationships between scale components. By applying wavelet multiresolution analysis, it becomes clear that the leverage effect holds true between return and volatility components with scales equal to or larger than twenty minutes. However, these relationships are obscured in a five-minute data analysis because the smallest scale component accounts for a dominant portion of the variation of return. In testing the volatility feedback hypothesis, a modified model was used to incorporate apparent long memory in the daily realized volatility. This makes both sides of the test model balanced in integration order. No evidence of a volatility feedback effect was found under these stipulations.The results of this study reinforce the horizon dependency of the relationships. Hence, investors should assume different risk-return relationships for each horizon of interest. Additionally, the results show that the introduction of the long memory property to the proposed model is critical in the testing of risk-return relationships.


The Risk Return Tradeoff in the Long-Run

The Risk Return Tradeoff in the Long-Run
Author: Christian T. Lundblad
Publisher:
Total Pages: 53
Release: 2012
Genre:
ISBN:

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The risk-return tradeoff is fundamental to finance. However, while many asset pricing models imply a positive relationship between the risk premium on the market portfolio and the variance of its return, previous studies find the empirical relationship is weak at best. In sharp contrast, this study, demonstrates that the weak empirical relationship is an artifact of the small sample nature of the available data, as an extremely large number of time-series observations is required to precisely estimate this relationship. To maximize the available time-series, I employ the nearly two century history of US equity market returns from Schwert (1990), exploring the empirical risk-return tradeoff for a variety of specifications that allow for asymmetric volatility, regime-switching, and additional factors associated with intertemporal (ICAPM) hedging demands. Similar to studies that use the more recent US equity price history, conditional market volatility in the historical data is persistent and displays strong asymmetric relationships to return innovations. Further, the conditional correlation between stock and bond markets is closely related to periods of documented financial crises. Finally, in contrast to evidence based upon the recent US experience, the estimated relationship between risk and return is positive and statistically significant across every specification considered.


The Risk Return Relationship

The Risk Return Relationship
Author: Minxian Yang
Publisher:
Total Pages: 31
Release: 2014
Genre:
ISBN:

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The risk return relationship is analysed in bivariate models for return and realised variance (RV) series. Based on daily time series from 21 international market indices for more than 13 years (January 2000 to February 2013), the empirical findings support the arguments of risk return tradeoff, volatility feedback and statistical balance. It is reasoned that the empirical risk return relationship is primarily shaped by two important data features: the negative contemporaneous correlation between the return and RV, and the difference in the autocorrelation structures of the return and RV.


High-Frequency Financial Econometrics

High-Frequency Financial Econometrics
Author: Yacine Aït-Sahalia
Publisher: Princeton University Press
Total Pages: 683
Release: 2014-07-21
Genre: Business & Economics
ISBN: 0691161437

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A comprehensive introduction to the statistical and econometric methods for analyzing high-frequency financial data High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. Yacine Aït-Sahalia and Jean Jacod cover the mathematical foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the asymptotic concepts that their analysis relies on. Aït-Sahalia and Jacod also deal with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As they demonstrate, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. Aït-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.


International Finance

International Finance
Author: H. Kent Baker
Publisher: Oxford University Press
Total Pages: 701
Release: 2013-01-17
Genre: Business & Economics
ISBN: 0199754659

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Understanding the current state of affairs and tools available in the study of international finance is increasingly important as few areas in finance can be divorced completely from international issues. International Finance reflects the new diversity of interest in international finance by bringing together a set of chapters that summarizes and synthesizes developments to date in the many and varied areas that are now viewed as having international content. The book attempts to differentiate between what is known, what is believed, and what is still being debated about international finance. The survey nature of this book involves tradeoffs that inevitably had to be made in the process given the vast footprint that constitutes international finance. No single book can cover everything. This book, however, tries to maintain a balance between the micro and macro aspects of international finance. Although each chapter is self-contained, the chapters form a logical whole that follows a logical sequence. The book is organized into five broad categories of interest: (1) exchange rates and risk management, (2) international financial markets and institutions, (3) international investing, (4) international financial management, and (5) special topics. The chapters cover market integration, financial crisis, and the links between financial markets and development in some detail as they relate to these areas. In each instance, the contributors to this book discuss developments in the field to date and explain the importance of each area to finance as a field of study. Consequently, the strategic focus of the book is both broad and narrow, depending on the reader's needs. The entire book provides a broad picture of the current state of international finance, but a reader with more focused interests will find individual chapters illuminating on specific topics.


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.


Volatility

Volatility
Author: Robert A. Jarrow
Publisher:
Total Pages: 472
Release: 1998
Genre: Derivative securities
ISBN:

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Written by a number of authors, this text is aimed at market practitioners and applies the latest stochastic volatility research findings to the analysis of stock prices. It includes commentary and analysis based on real-life situations.


Handbook of High Frequency Trading

Handbook of High Frequency Trading
Author: Greg N. Gregoriou
Publisher: Academic Press
Total Pages: 495
Release: 2015-02-05
Genre: Business & Economics
ISBN: 0128023627

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This comprehensive examination of high frequency trading looks beyond mathematical models, which are the subject of most HFT books, to the mechanics of the marketplace. In 25 chapters, researchers probe the intricate nature of high frequency market dynamics, market structure, back-office processes, and regulation. They look deeply into computing infrastructure, describing data sources, formats, and required processing rates as well as software architecture and current technologies. They also create contexts, explaining the historical rise of automated trading systems, corresponding technological advances in hardware and software, and the evolution of the trading landscape. Developed for students and professionals who want more than discussions on the econometrics of the modelling process, The Handbook of High Frequency Trading explains the entirety of this controversial trading strategy. Answers all questions about high frequency trading without being limited to mathematical modelling Illuminates market dynamics, processes, and regulations Explains how high frequency trading evolved and predicts its future developments


Empirical Asset Pricing

Empirical Asset Pricing
Author: Turan G. Bali
Publisher: John Wiley & Sons
Total Pages: 512
Release: 2016-02-26
Genre: Business & Economics
ISBN: 1118589475

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“Bali, Engle, and Murray have produced a highly accessible introduction to the techniques and evidence of modern empirical asset pricing. This book should be read and absorbed by every serious student of the field, academic and professional.” Eugene Fama, Robert R. McCormick Distinguished Service Professor of Finance, University of Chicago and 2013 Nobel Laureate in Economic Sciences “The empirical analysis of the cross-section of stock returns is a monumental achievement of half a century of finance research. Both the established facts and the methods used to discover them have subtle complexities that can mislead casual observers and novice researchers. Bali, Engle, and Murray’s clear and careful guide to these issues provides a firm foundation for future discoveries.” John Campbell, Morton L. and Carole S. Olshan Professor of Economics, Harvard University “Bali, Engle, and Murray provide clear and accessible descriptions of many of the most important empirical techniques and results in asset pricing.” Kenneth R. French, Roth Family Distinguished Professor of Finance, Tuck School of Business, Dartmouth College “This exciting new book presents a thorough review of what we know about the cross-section of stock returns. Given its comprehensive nature, systematic approach, and easy-to-understand language, the book is a valuable resource for any introductory PhD class in empirical asset pricing.” Lubos Pastor, Charles P. McQuaid Professor of Finance, University of Chicago Empirical Asset Pricing: The Cross Section of Stock Returns is a comprehensive overview of the most important findings of empirical asset pricing research. The book begins with thorough expositions of the most prevalent econometric techniques with in-depth discussions of the implementation and interpretation of results illustrated through detailed examples. The second half of the book applies these techniques to demonstrate the most salient patterns observed in stock returns. The phenomena documented form the basis for a range of investment strategies as well as the foundations of contemporary empirical asset pricing research. Empirical Asset Pricing: The Cross Section of Stock Returns also includes: Discussions on the driving forces behind the patterns observed in the stock market An extensive set of results that serve as a reference for practitioners and academics alike Numerous references to both contemporary and foundational research articles Empirical Asset Pricing: The Cross Section of Stock Returns is an ideal textbook for graduate-level courses in asset pricing and portfolio management. The book is also an indispensable reference for researchers and practitioners in finance and economics. Turan G. Bali, PhD, is the Robert Parker Chair Professor of Finance in the McDonough School of Business at Georgetown University. The recipient of the 2014 Jack Treynor prize, he is the coauthor of Mathematical Methods for Finance: Tools for Asset and Risk Management, also published by Wiley. Robert F. Engle, PhD, is the Michael Armellino Professor of Finance in the Stern School of Business at New York University. He is the 2003 Nobel Laureate in Economic Sciences, Director of the New York University Stern Volatility Institute, and co-founding President of the Society for Financial Econometrics. Scott Murray, PhD, is an Assistant Professor in the Department of Finance in the J. Mack Robinson College of Business at Georgia State University. He is the recipient of the 2014 Jack Treynor prize.