A Nonparametric View of the Role of Jumps to Interest Rates

A Nonparametric View of the Role of Jumps to Interest Rates
Author: Michael S. Johannes
Publisher:
Total Pages: 55
Release: 2011
Genre:
ISBN:

This paper provides an empirical analysis of the role of jumps in continuous-time models of the short rate. A diagnostic is developed to relate the failure of single and certain multi-factor models to the presence of unaccounted for jump-type movements. I introduce a nonparametric jump-diffusion model and develop an estimation methodology, which is justified using Monte Carlo simulations. The results point toward a dominant role for jumps in determining the dynamics of the short rate relative to standard diffusion components. An approximate filtering algorithm estimates jump times and sizes, providing further insight into the role of jumps. Jumps appear to be a mechanism through which fundamental information regarding the state of the macroeconomy enters the term-structure. Last, I investigate the implications of jumps for the default free, zero coupon term structure of interest rates.

Recent Advances and Trends in Nonparametric Statistics

Recent Advances and Trends in Nonparametric Statistics
Author: M.G. Akritas
Publisher: Elsevier
Total Pages: 524
Release: 2003-10-31
Genre: Computers
ISBN: 0444513787

The advent of high-speed, affordable computers in the last two decades has given a new boost to the nonparametric way of thinking. Classical nonparametric procedures, such as function smoothing, suddenly lost their abstract flavour as they became practically implementable. In addition, many previously unthinkable possibilities became mainstream; prime examples include the bootstrap and resampling methods, wavelets and nonlinear smoothers, graphical methods, data mining, bioinformatics, as well as the more recent algorithmic approaches such as bagging and boosting. This volume is a collection of short articles - most of which having a review component - describing the state-of-the art of Nonparametric Statistics at the beginning of a new millennium. Key features: . algorithic approaches . wavelets and nonlinear smoothers . graphical methods and data mining . biostatistics and bioinformatics . bagging and boosting . support vector machines . resampling methods

Nonparametric Econometric Methods

Nonparametric Econometric Methods
Author: Qi Li
Publisher: Emerald Group Publishing
Total Pages: 570
Release: 2009-12-04
Genre: Business & Economics
ISBN: 1849506248

Contains a selection of papers presented initially at the 7th Annual Advances in Econometrics Conference held on the LSU campus in Baton Rouge, Louisiana during November 14-16, 2008. This work is suitable for those who wish to familiarize themselves with nonparametric methodology.

Parametric and Nonparametric Volatility Measurement

Parametric and Nonparametric Volatility Measurement
Author: Torben Gustav Andersen
Publisher:
Total Pages: 84
Release: 2002
Genre: Securities
ISBN:

Volatility has been one of the most active areas of research in empirical finance and time series econometrics during the past decade. This chapter provides a unified continuous-time, frictionless, no-arbitrage framework for systematically categorizing the various volatility concepts, measurement procedures, and modeling procedures. We define three different volatility concepts: (i) the notional volatility corresponding to the ex-post sample-path return variability over a fixed time interval, (ii) the ex-ante expected volatility over a fixed time interval, and (iii) the instantaneous volatility corresponding to the strength of the volatility process at a point in time. The parametric procedures rely on explicit functional form assumptions regarding the expected and/or instantaneous volatility. In the discrete-time ARCH class of models, the expectations are formulated in terms of directly observable variables, while the discrete- and continuous-time stochastic volatility models involve latent state variable(s). The nonparametric procedures are generally free from such functional form assumptions and hence afford estimates of notional volatility that are flexible yet consistent (as the sampling frequency of the underlying returns increases). The nonparametric procedures include ARCH filters and smoothers designed to measure the volatility over infinitesimally short horizons, as well as the recently-popularized realized volatility measures for (non-trivial) fixed-length time intervals.

Encyclopedia of Finance

Encyclopedia of Finance
Author: Cheng-Few Lee
Publisher: Springer Nature
Total Pages: 2746
Release: 2022-09-12
Genre: Business & Economics
ISBN: 3030912310

The Encyclopedia of Finance comprehensively covers the broad spectrum of terms and topics relating finance from asset pricing models to option pricing models to risk management and beyond. This third edition is comprised of over 1,300 individual definitions, chapters, appendices and is the most comprehensive and up-to-date resource in the field, integrating the most current terminology, research, theory, and practical applications. It includes 200 new terms and essays; 25 new chapters and four new appendices. Showcasing contributions from an international array of experts, the revised edition of this major reference work is unparalleled in the breadth and depth of its coverage.

Interest Rate Models: an Infinite Dimensional Stochastic Analysis Perspective

Interest Rate Models: an Infinite Dimensional Stochastic Analysis Perspective
Author: René Carmona
Publisher: Springer Science & Business Media
Total Pages: 236
Release: 2007-05-22
Genre: Mathematics
ISBN: 3540270671

This book presents the mathematical issues that arise in modeling the interest rate term structure by casting the interest-rate models as stochastic evolution equations in infinite dimensions. The text includes a crash course on interest rates, a self-contained introduction to infinite dimensional stochastic analysis, and recent results in interest rate theory. From the reviews: "A wonderful book. The authors present some cutting-edge math." --WWW.RISKBOOK.COM

Handbook of Quantitative Finance and Risk Management

Handbook of Quantitative Finance and Risk Management
Author: Cheng-Few Lee
Publisher: Springer Science & Business Media
Total Pages: 1700
Release: 2010-06-14
Genre: Business & Economics
ISBN: 0387771174

Quantitative finance is a combination of economics, accounting, statistics, econometrics, mathematics, stochastic process, and computer science and technology. Increasingly, the tools of financial analysis are being applied to assess, monitor, and mitigate risk, especially in the context of globalization, market volatility, and economic crisis. This two-volume handbook, comprised of over 100 chapters, is the most comprehensive resource in the field to date, integrating the most current theory, methodology, policy, and practical applications. Showcasing contributions from an international array of experts, the Handbook of Quantitative Finance and Risk Management is unparalleled in the breadth and depth of its coverage. Volume 1 presents an overview of quantitative finance and risk management research, covering the essential theories, policies, and empirical methodologies used in the field. Chapters provide in-depth discussion of portfolio theory and investment analysis. Volume 2 covers options and option pricing theory and risk management. Volume 3 presents a wide variety of models and analytical tools. Throughout, the handbook offers illustrative case examples, worked equations, and extensive references; additional features include chapter abstracts, keywords, and author and subject indices. From "arbitrage" to "yield spreads," the Handbook of Quantitative Finance and Risk Management will serve as an essential resource for academics, educators, students, policymakers, and practitioners.

Financial Modelling with Jump Processes

Financial Modelling with Jump Processes
Author: Peter Tankov
Publisher: CRC Press
Total Pages: 552
Release: 2003-12-30
Genre: Business & Economics
ISBN: 1135437947

WINNER of a Riskbook.com Best of 2004 Book Award! During the last decade, financial models based on jump processes have acquired increasing popularity in risk management and option pricing. Much has been published on the subject, but the technical nature of most papers makes them difficult for nonspecialists to understand, and the mathematic