Financial Econometrics

Financial Econometrics

Author: Christian Gourieroux

Publisher: Princeton University Press

Published: 2022-12-13

Total Pages: 528

ISBN-13: 0691242364

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Financial econometrics is a great success story in economics. Econometrics uses data and statistical inference methods, together with structural and descriptive modeling, to address rigorous economic problems. Its development within the world of finance is quite recent and has been paralleled by a fast expansion of financial markets and an increasing variety and complexity of financial products. This has fueled the demand for people with advanced econometrics skills. For professionals and advanced graduate students pursuing greater expertise in econometric modeling, this is a superb guide to the field's frontier. With the goal of providing information that is absolutely up-to-date—essential in today's rapidly evolving financial environment—Gourieroux and Jasiak focus on methods related to foregoing research and those modeling techniques that seem relevant to future advances. They present a balanced synthesis of financial theory and statistical methodology. Recognizing that any model is necessarily a simplified image of reality and that econometric methods must be adapted and applied on a case-by-case basis, the authors employ a wide variety of data sampled at frequencies ranging from intraday to monthly. These data comprise time series representing both the European and North American markets for stocks, bonds, and foreign currencies. Practitioners are encouraged to keep a critical eye and are armed with graphical diagnostics to eradicate misspecification errors. This authoritative, state-of-the-art reference text is ideal for upper-level graduate students, researchers, and professionals seeking to update their skills and gain greater facility in using econometric models. All will benefit from the emphasis on practical aspects of financial modeling and statistical inference. Doctoral candidates will appreciate the inclusion of detailed mathematical derivations of the deeper results as well as the more advanced problems concerning high-frequency data and risk control. By establishing a link between practical questions and the answers provided by financial and statistical theory, the book also addresses the needs of applied researchers employed by financial institutions.


Return Distributions in Finance

Return Distributions in Finance

Author: Stephen Satchell

Publisher: Elsevier

Published: 2000-12-08

Total Pages: 329

ISBN-13: 0080516246

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Quantitative methods have revolutionised the area of trading, regulation, risk management, portfolio construction, asset pricing and treasury activities, and governmental activity such as central banking. One of the original contributions in this area is the classic by Cootner entitled 'The Random Nature of Stock Market Prices'. This work investigated the statistical properties of asset prices and was one of the first works to investigate this area in a rigorous manner. Much has happened in this field in the last 35 years and 'Return Distributions in Finance' contains much new information that reflects this huge growth. The authors combined experience reflects not only the new theory but also the new practice in this fascinating area. The rise of financial engineering now allows us to change the nature of asset returns to whatever pattern we desire, albeit at a cost. Benefits and costs can only be understood if we understand the underlying processes. 'Return Distributions in Finance' allows us to gain that understanding. - Assists in understanding asset return distributions - Provides a full overview of financial risk management techniques in asset allocation - Demonstrates how to use asset return forecast applications


Nonlinear Modelling of High Frequency Financial Time Series

Nonlinear Modelling of High Frequency Financial Time Series

Author: Christian L. Dunis

Publisher: John Wiley & Sons

Published: 1998-07-09

Total Pages: 344

ISBN-13:

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Nonlinear Modelling of High Frequency Financial Time Series Edited by Christian Dunis and Bin Zhou In the competitive and risky environment of today's financial markets, daily prices and models based upon low frequency price series data do not provide the level of accuracy required by traders and a growing number of risk managers. To improve results, more and more researchers and practitioners are turning to high frequency data. Nonlinear Modelling of High Frequency Financial Time Series presents the latest developments and views of leading international researchers and market practitioners, in modelling high frequency data in finance. Combining both nonlinear modelling and intraday data for financial markets, the editors provide a fascinating foray into this extremely popular discipline. This book evolves around four major themes. The first introductory section focuses on high frequency financial data. The second part examines the exact nature of the time series considered: several linearity tests are presented and applied and their modelling implications assessed. The third and fourth parts are dedicated to modelling and forecasting these financial time series.


Decision Technologies For Financial Engineering - Proceedings Of The Fourth International Conference On Neural Networks In The Capital Markets (Nncm '96)

Decision Technologies For Financial Engineering - Proceedings Of The Fourth International Conference On Neural Networks In The Capital Markets (Nncm '96)

Author: Yaser Abu-mostafa

Publisher: World Scientific

Published: 1998-01-02

Total Pages: 442

ISBN-13: 9814546216

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This volume selects the best contributions from the Fourth International Conference on Neural Networks in the Capital Markets (NNCM). The conference brought together academics from several disciplines with strategists and decision makers from the financial industries.The various chapters present and compare new techniques from many areas including data mining, information systems, machine learning, and statistical artificial intelligence. The volume focuses on evaluating their usefulness for problems in computational finance and financial engineering.Applications — risk management; asset allocation; dynamic trading and hedging; forecasting; trading cost control. Markets — equity; foreign exchange; bond; commodity; derivatives; Approaches — data mining; statistical AI; machine learning; Monte Carlo simulation; bootstrapping; genetic algorithms; nonparametric methods; fuzzy logic.The chapters emphasizes in-depth and comparative evaluation with established approaches.


Statistical Methods in Finance

Statistical Methods in Finance

Author: G. S. Maddala

Publisher:

Published: 1996-12-11

Total Pages: 760

ISBN-13:

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A comprehensive reference work for teaching at graduate level and research in empirical finance. The chapters cover a wide range of statistical and probabilistic methods applied to a variety of financial methods and are written by internationally renowned experts.


Understanding Volatility and Liquidity in the Financial Markets

Understanding Volatility and Liquidity in the Financial Markets

Author: Dimitris N. Chorafas

Publisher: Euromoney Publications

Published: 1998

Total Pages: 252

ISBN-13:

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This title is useful reading for anyone responsible for minimizing exposures and failures within their organization, as well as financial professionals working to produce models of risk and reward. It goes beyond the issues of volatility and liquidity, leading towards a system of risk management.