What We Have Learnt from Financial Econometrics Modeling?
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Financial Econometrics Lecture Notes
Financial Econometrics Lecture Notes Professor Doron Avramov The Hebrew University of Jerusalem & Chinese University of Hong Kong Introduction: Why do we need a course in Financial Econometrics? 2 Professor Doron Avramov, Financial Econometrics Syllabus: Motivation The past few decades have been characterized by an extraordinary growth in the use of quantitative methods in the analysis of various asset classes; be it equities, fixed income securities, commodities, and derivatives. In addition, financial economists have routinely been using advanced mathematical, statistical, and econometric techniques in a host of applications including investment decisions, risk management, volatility modeling, interest rate modeling, and the list goes on. 3 Professor Doron Avramov, Financial Econometrics Syllabus: Objectives This course attempts to provide a fairly deep understanding of such techniques. The purpose is twofold, to provide research tools in financial economics and comprehend investment designs employed by practitioners. The course is intended for advanced master and PhD level students in finance and economics. 4 Professor Doron Avramov, Financial Econometrics Syllabus: Prerequisite I will assume prior exposure to matrix algebra, distribution theory, Ordinary Least Squares, Maximum Likelihood Estimation, Method of Moments, and the Delta Method. I will also assume you have some skills in computer programing beyond Excel. MATLAB and R are the most recommended for this course. OCTAVE could be used as well, as it is a free software, and is practically identical to MATLAB when considering the scope of the course. If you desire to use STATA, SAS, or other comparable tools, please consult with the TA. 5 Professor Doron Avramov, Financial Econometrics Syllabus: Grade Components Assignments (36%): there will be two problem sets during the term. -
Primena Statistike U Kliničkim Istraţivanjima Sa Osvrtom Na Korišćenje Računarskih Programa
UNIVERZITET U BEOGRADU MATEMATIČKI FAKULTET Dušica V. Gavrilović Primena statistike u kliničkim istraţivanjima sa osvrtom na korišćenje računarskih programa - Master rad - Mentor: prof. dr Vesna Jevremović Beograd, 2013. godine Zahvalnica Ovaj rad bi bilo veoma teško napisati da nisam imala stručnu podršku, kvalitetne sugestije i reviziju, pomoć prijatelja, razumevanje kolega i beskrajnu podršku porodice. To su razlozi zbog kojih želim da se zahvalim: . Mom mentoru, prof. dr Vesni Jevremović sa Matematičkog fakulteta Univerziteta u Beogradu, koja je bila ne samo idejni tvorac ovog rada već i dugogodišnja podrška u njegovoj realizaciji. Njena neverovatna upornost, razne sugestije, neiscrpni optimizam, profesionalizam i razumevanje, predstavljali su moj stalni izvor snage na ovom master-putu. Članu komisije, doc. dr Zorici Stanimirović sa Matematičkog fakulteta Univerziteta u Beogradu, na izuzetnoj ekspeditivnosti, stručnoj recenziji, razumevanju, strpljenju i brojnim korisnim savetima. Članu komisije, mr Marku Obradoviću sa Matematičkog fakulteta Univerziteta u Beogradu, na stručnoj i prijateljskoj podršci kao i spremnosti na saradnju. Dipl. mat. Radojki Pavlović, šefu studentske službe Matematičkog fakulteta Univerziteta u Beogradu, na upornosti, snalažljivosti i kreativnosti u pronalaženju raznih ideja, predloga i rešenja na putu realizacije ovog master rada. Dugogodišnje prijateljstvo sa njom oduvek beskrajno cenim i oduvek mi mnogo znači. Dipl. mat. Zorani Bizetić, načelniku Data Centra Instituta za onkologiju i radiologiju Srbije, na upornosti, idejama, detaljnoj reviziji, korisnim sugestijama i svakojakoj podršci. Čak i kada je neverovatno ili dosadno ili pametno uporna, mnogo je i dugo volim – skoro ceo moj život. Mast. biol. Jelici Novaković na strpljenju, reviziji, bezbrojnim korekcijama i tehničkoj podršci svake vrste. Hvala na osmehu, budnom oku u sitne sate, izvrsnoj hrani koja me je vraćala u život, nes-kafi sa penom i transfuziji energije kada sam bila na rezervi. -
Advanced Financial Econometrics
MFE 2013-14 Trinity Term | Advanced Financial Econometrics Advanced Financial Econometrics Kevin Sheppard Department of Economics Oxford-Man Institute of Quantitative Finance Course Site: http://www.kevinsheppard.com/Advanced_Econometrics Weblearn Site: https://weblearn.ox.ac.uk/portal/site/socsci/sbs/mfe2012_2013/ttafe 1 Aims, objectives and intended learning outcomes 1.1 Basic aims This course aims to extend the core tools of Financial Econometrics I & II to some advanced forecasting problems. The first component covers techniques for working with many forecasting models – often referred to as data snoop- ing. The second component covers techniques for handling many predictors – including the case where the number of predictors is much larger than the number of data points available. 1.2 Specific objectives Part I (Week 1 – 4) Revisiting the bootstrap and extensions appropriate for time-series data • Technical trading rules and large number of forecasts • Formalized data-snooping robust inference • False-discovery rates • Detecting sets of models that are statistically indistinguishable • Part I (Week 5 – 8) Dynamic Factor Models and Principal Component Analysis • The Kalman Filter and Expectations-Maximization Algorithm • Partial Least Squares and the 3-pass Regression Filter • Regularized Reduced Rank Regression • 1.3 Intended learning outcomes Following class attendance, successful completion of assigned readings, assignments, both theoretical and empiri- cal, and recommended private study, students should be able to Detect superior performance using methods that are robust to data snooping • Produce and evaluate forecasts in for macro-financial time series in data-rich environments • 1 2 Teaching resources 2.1 Lecturing Lectures are provided by Kevin Sheppard. Kevin Sheppard is an Associate Professor and a fellow at Keble College. -
An Introduction to Financial Econometrics
An introduction to financial econometrics Jianqing Fan Department of Operation Research and Financial Engineering Princeton University Princeton, NJ 08544 November 14, 2004 What is the financial econometrics? This simple question does not have a simple answer. The boundary of such an interdisciplinary area is always moot and any attempt to give a formal definition is unlikely to be successful. Broadly speaking, financial econometrics is to study quantitative problems arising from finance. It uses sta- tistical techniques and economic theory to address a variety of problems from finance. These include building financial models, estimation and inferences of financial models, volatility estimation, risk management, testing financial economics theory, capital asset pricing, derivative pricing, portfolio allocation, risk-adjusted returns, simulating financial systems, hedging strategies, among others. Technological invention and trade globalization have brought us into a new era of financial markets. Over the last three decades, enormous number of new financial products have been created to meet customers’ demands. For example, to reduce the impact of the fluctuations of currency exchange rates on a firm’s finance, which makes its profit more predictable and competitive, a multinational corporation may decide to buy the options on the future of foreign exchanges; to reduce the risk of price fluctuations of a commodity (e.g. lumbers, corns, soybeans), a farmer may enter into the future contracts of the commodity; to reduce the risk of weather exposures, amuse parks (too hot or too cold reduces the number of visitors) and energy companies may decide to purchase the financial derivatives based on the temperature. An important milestone is that in the year 1973, the world’s first options exchange opened in Chicago. -
Dell VITA RFP- Revised COTS Pricing 12-17-08
COTS Software COTS Software is considered to be commercially available software read to run without customization, Gov't Pricing Academic Pricing Gov't Pricing Enter discount for publisher (This Enter discount for publisher (This will be the will be the lowest discount that you lowest discount that you will offer during the will offer during the term of the term of the contract) contract) COTS Discount % COTS Discount % Title Price Adobe 0.00% Adobe 0.00% Adobe Acrobat Professional Version 9 Windows 218.33 Autodesk 0.00% Autodesk 0.00% Adobe Photoshop CS4 Windows 635.24 Citrix 0.00% Citrix 0.00% Autodesk Sketchbook 2009 Pro 120 Corel 0.00% Corel 0.00% McAfee Active Virus Defense 14.87 DoubleTake 0.00% DoubleTake 0.00% Symantec Norton Antivirus 2009 25 Intuit 0.00% Intuit 0.00% Symantec Backup Exec 12.5 for Windows Servers 454.25 McAfee 0.00% McAfee 0.00% PRICE FOR ABOVE TITLES SHOULD BE Novell 0.00% Novell 0.00% QUOTED FOR PURCHASE OF ONE (1) COPY Nuance 0.00% Nuance 0.00% Quark Software 0.00% Quark Software 0.00% Quest Software 0.00% Quest Software 0.00% Riverdeep 0.00% Riverdeep 0.00% Academic Pricing Symantec 0.00% Symantec 0.00% Title Price Trend Micro 0.00% Trend Micro 0.00% Adobe Acrobat Professional Version 9 Windows 131.03 VMWare 0.00% VMWare 0.00% Adobe Photoshop CS4 Windows 276.43 WebSense 0.00% WebSense 0.00% Autodesk Sketchbook 2009 Pro 120 McAfee Active Virus Defense 14.87 Symantec Norton Antivirus 2009 25 Symantec Backup Exec 12.5 for Windows Servers 454.25 PRICE FOR ABOVE TITLES SHOULD BE QUOTED FOR PURCHASE OF ONE (1) COPY LICENSE AND MEDIA For purposes of evaluation VITA will create a market basket. -
Discoversim™ Version 2 Workbook
DiscoverSim™ Version 2 Workbook Contact Information: Technical Support: 1-866-475-2124 (Toll Free in North America) or 1-416-236-5877 Sales: 1-888-SigmaXL (888-744-6295) E-mail: [email protected] Web: www.SigmaXL.com Published: December 2015 Copyright © 2011 - 2015, SigmaXL, Inc. Table of Contents DiscoverSim™ Feature List Summary, Installation Notes, System Requirements and Getting Help ..................................................................................................................................1 DiscoverSim™ Version 2 Feature List Summary ...................................................................... 3 Key Features (bold denotes new in Version 2): ................................................................. 3 Common Continuous Distributions: .................................................................................. 4 Advanced Continuous Distributions: ................................................................................. 4 Discrete Distributions: ....................................................................................................... 5 Stochastic Information Packet (SIP): .................................................................................. 5 What’s New in Version 2 .................................................................................................... 6 Installation Notes ............................................................................................................... 8 DiscoverSim™ System Requirements .............................................................................. -
Essential Statistics for Applied Linguistics : Using R Or Jasp
ESSENTIAL STATISTICS FOR APPLIED LINGUISTICS : USING R OR JASP Author: Hanneke Loerts Number of Pages: 250 pages Published Date: 06 Feb 2020 Publisher: MacMillan Education UK Publication Country: London, United Kingdom Language: English ISBN: 9781352007817 DOWNLOAD: ESSENTIAL STATISTICS FOR APPLIED LINGUISTICS : USING R OR JASP Essential Statistics for Applied Linguistics : Using R or JASP PDF Book The newspaper headlines tell us that Britain's wildlife is in trouble. Every chapter opens up with a vignette called a "Decision Dilemma" about real companies, data, and business issues.Ch. This book will be an invaluable source of information for a variety of professionals working with patients with advanced disease, including palliative care doctors and specialist nurses, as there is a scarcity of consultants in pain management in the field of palliative care. Although the units are diverse and have a range of poetry and prose for teachers to use, the book presents cohesive methods for engaging children with a variety of different literary texts and improving standards of literacy. StateResponsibility. With these questions providing the building blocks for your essay, Sawyer guides you through the rest of the process, from choosing a structure to revising your essay, and answers the big questions that have probably been keeping you up at night: How do I brag in a way that doesn't sound like bragging. Important topics such as vascular lesions, warts, acne, scars, and pigmented lesions are presented and discussed in all aspects. His discovery, which he described to his king in the presence of Christopher Columbus, opened up the sea route around Africa to India and the rest of Asia. -
Certification Training Materials PRIMERS
QUALITY PROGRESS QUALITY Putting Best Practices to Work www.qualityprogress.com | June 2014 | JUNE 2014 Supply Chains Speed Up p. 22 QUALITYP PROGRESS Chain of SUPPLY CHAIN SUPPLY Command Quality tools linked to supply chain efficiency, reduced waste p. 14 Plus: VOLUME 47/NUMBER 6 Process factors: A cornerstone of supplier audits p. 28 A fail-safe FMEA p. 42 The Global Voice of QualityTM Certification Training Materials PRIMERS Our Primers are the most widely used texts for certification training. They can be taken into the exam. QCI offers 16 different Primers. SOLUTION TEXTS Detailed solutions to all questions in the corresponding Primer. CD-ROMS Interactive software to assist students preparing for ASQ exams. QUALITY COUNCIL OF INDIANA Online Orders: www.qualitycouncil.com Phone Orders: 800-660-4215 Fax Orders: 812-533-4216 Mail Orders: QCI Order Department, 602 W. Paris Ave., W. Terre Haute, IN 47885-1124 Check out a few of the NEW books from ASQ Quality Press! The FDA and Worldwide Current Good Manufacturing Practices and Quality System Requirements for Finished Pharmaceuticals This guidance book is meant as a resource to Continuous Permanent Improvement manufacturers of pharmaceuticals, providing The purpose of this book is not to expound any up-to-date information concerning required and new theory or tools, but to share experiences in recommended quality system practices. implementing existing methods with a bias toward Item: H1458 business results. In fact, one of the important lessons we have learned is that most existing models or methods, if adhered to in the right spirit, will give results. The Certified Pharmaceutical GMP Item: H1466 Professional Handbook The purpose of this handbook is to highlight and partially annotate what the founders of the Certified Pharmaceutical Good Manufacturing Practices Professional (CPGP) examination believed to be the main topics comprising worldwide pharmaceutical good manufacturing practices (GMPs). -
Two Pillars of Asset Pricing †
American Economic Review 2014, 104(6): 1467–1485 http://dx.doi.org/10.1257/aer.104.6.1467 Two Pillars of Asset Pricing † By Eugene F. Fama * The Nobel Foundation asks that the Nobel lecture cover the work for which the Prize is awarded. The announcement of this year’s Prize cites empirical work in asset pricing. I interpret this to include work on efficient capital markets and work on developing and testing asset pricing models—the two pillars, or perhaps more descriptive, the Siamese twins of asset pricing. I start with efficient markets and then move on to asset pricing models. I. Efficient Capital Markets A. Early Work The year 1962 was a propitious time for PhD research at the University of Chicago. Computers were coming into their own, liberating econometricians from their mechanical calculators. It became possible to process large amounts of data quickly, at least by previous standards. Stock prices are among the most accessible data, and there was burgeoning interest in studying the behavior of stock returns, cen- tered at the University of Chicago Merton Miller, Harry Roberts, Lester Telser, and ( Benoit Mandelbrot as a frequent visitor and MIT Sidney Alexander, Paul Cootner, ) ( Franco Modigliani, and Paul Samuelson . Modigliani often visited Chicago to work ) with his longtime coauthor Merton Miller, so there was frequent exchange of ideas between the two schools. It was clear from the beginning that the central question is whether asset prices reflect all available information—what I labelled the efficient markets hypothesis Fama 1965b . The difficulty is making the hypothesis testable. We can’t test whether ( ) the market does what it is supposed to do unless we specify what it is supposed to do. -
Lean Six Sigma Using Sigmaxl and Minitab ABOUT the AUTHORS
Lean Six Sigma Using SigmaXL and Minitab ABOUT THE AUTHORS ISSA BASS is a Master Black Belt and senior consultant with Manor House and Associates. He is the founding editor of SixSigmaFirst.com. Bass has extensive experience in qual- ity and operations management, and is the author of Six Sigma Statistics with Minitab and Excel. BARBARA LAWTON, Ph.D., is a Six Sigma Black Belt and has been improving manufacturing processes using Lean tech- niques and Six Sigma for more than 15 years, in various industries on three continents. Dr. Lawton specializes in data analysis, and is a keen experimentalist and problem solver, using a wide range of statistical tools. She currently works for one of the world’s leading aerospace organizations in the UK. Lean Six Sigma Using SigmaXL and Minitab Issa Bass Barbara Lawton, Ph.D. New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. ISBN: 978-0-07-162621-7 MHID: 0-07-162621-2 The material in this eBook also appears in the print version of this title: ISBN: 978-0-07-162130-4, MHID: 0-07-162130-X. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. -
Financial Econometrics Value at Risk
Financial Econometrics Value at Risk Gerald P. Dwyer Trinity College, Dublin January 2016 Outline 1 Value at Risk Introduction VaR RiskMetricsTM Summary Risk What do we mean by risk? I Dictionary: possibility of loss or injury I Volatility a common measure for assets I Two points of view on volatility measure F Risk is both good and bad changes F Volatility is useful because there is symmetry in gains and losses What sorts of risk? I Market risk I Credit risk I Liquidity risk I Operational risk I Other risks sometimes mentioned F Legal risk F Model risk Different ways of dealing with risk Maximize expected utility with preferences about risk implicit in the utility function I What are problems with this? The worst that can happen to you I What are problems with this? Safety first I One definition (Roy): Investor chooses a portfolio that minimizes the probability of a loss greater in magnitude than some disaster level I What are problems with this? I Another definition (Telser): Investor specifies a maximum probability of a return less than some level and then chooses the portfolio that maximizes the expected return subject to this restriction Value at risk Value at risk summarizes the maximum loss over some horizon with a given confidence level I Lower tail of distribution function of returns for a long position I Upper tail of distribution function of returns for a short position F Can use lower tail if symmetric I Suppose standard normal distribution, which implies an expected return of zero I 99 percent of the time, loss is at most -2.32634 -
On the Autoregressive Conditional Heteroskedasticity Models
On the Autoregressive Conditional Heteroskedasticity Models By Erik Stenberg Department of Statistics Uppsala University Bachelor’s Thesis Supervisor: Yukai Yang 2016 Abstract This thesis aims to provide a thorough discussion on the early ARCH- type models, including a brief background with volatility clustering, kur- tosis, skewness of financial time series. In-sample fit of the widely used GARCH(1,1) is discussed using both standard model evaluation criteria and simulated values from estimated models. A rolling scheme out-of sam- ple forecast is evaluated using several different loss-functions and R2 from the Mincer-Zarnowitz regression, with daily realized volatility constructed using five minute intra-daily squared returns to proxy the true volatility. Keywords: ARCH, GARCH, Volatility clustering, fat tail, forecasting, intra-daily returns Contents 1 Introduction 3 2 Volatility 3 3 Autoregressive Conditional Heteroskedasticity 7 3.1 ARCH(1) . 8 3.2 Implied Kurtosis of ARCH(1) . 8 4 Generalized Autoregressive Conditional Heteroskedasticity 9 4.1 GARCH(1,1) . 9 5 Other Distributional Assumptions 11 5.1 Student-t . 11 5.2 Skewed Student-t . 12 5.3 Skewed Generalized Error Distribution . 12 6 Diagnostics 13 6.1 In-Sample Fit Evaluation . 13 6.2 Forecast Evaluation . 13 6.2.1 Value-at-Risk . 17 7 Illustrations 17 7.1 In-Sample Fit . 17 7.2 Forecast . 19 8 Summary, Conclusions and Further Research 21 9 Appendix 24 1 Introduction Ever since the first of many Autoregressive Conditional Heteroskedastic (ARCH) models was presented, see Engle (1982), fitting models to describe conditional heteroskedasticity has been a widely discussed topic. The main reason for this is the fact that up to that point, many of the conventional time series and models used in finance assumed a constant standard deviation in asset returns, see Black and Scholes (1973).