Review of Economic Studies (1998) 65, 361-393 0 1998 The Review of Economic Studies Limited
0034-6527/98/00170361$02.00
Stochastic Volatility : Likelihood Inference and Comparison with ARCH Models
SANGJOON KIM Salomon Brothers Asia Limited NEIL SHEPHARD N
So, you want to go to grad school in Economics? A practical guide of the first years (for outsiders) from insiders
Ceyhun Elgin Mario Solis-Garcia University of Minnesota April 2007
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Felix Chana, Dora Marinovab, Michael McAleera a Department of Economics, University of Western Australia ([email protected]) b Institute for Sustainabi
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Financial market volatility is known to cluster. A volatile period is known to persist for some time before the market returns to normality. The ARCH (AutoRegressive Conditional Heteroskedasticity) model proposed by Eng
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Pei-Shih Weng a,
a
Department of Finance, National Central University, Jhongli, TY 32001, Taiwan May 2008
Abstract Empirically, the conditionally heteroskedastic volatility eect in short r
Q U A N T IT A T IV E F I N A N C E V O L U M E 3 (2003) 110 INSTITUTE O F PHYSICS PUBLISHING
RE S E A R C H PA P E R
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Estimating GARCH models using support vector machines*
Fernando P rez-Cruz1, Julio A Afonso-Rodrguez2 and e 3 Javier Giner
Financial Analysts Journal Volume 61 Number 1 2005, CFA Institute
Practical Issues in Forecasting Volatility
Ser-Huang Poon and Clive Granger
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Estimating and Forecasting Volatility of Financial Time Series in Pakistan with GARCH-type Models G.R. Pasha*, Tahira Qasim* and Muhammad Aslam*
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Bank of Canada
Banque du Canada
Working Paper 2006-14 / Document de travail 2006-14
Forecasting Commodity Prices: GARCH, Jumps, and Mean Reversion
by
Jean-Thomas Bernard, Lynda Khalaf, Maral Kichian, and Sebastien McMahon
ISSN 1192-5434 Printed in Canada
A FORECAST COMPARISON OF VOLATILITY MODELS: DOES ANYTHING BEAT A GARCH(1,1)?
PETER R. HANSENa AND ASGER LUNDEb
a Brown b Aarhus
University, Department of Economics, Box B, Providence, RI 02912, USA School of Business, Department of Information Science, De
FORECASTING FINANCIAL VOLATILITY: EVIDENCE FROM CHINESE STOCK MARKET by HONGYU PAN and ZHICHAO ZHANG
WORKING PAPER IN ECONOMICS AND FINANCE No. 06/02 FEBRUARY 2006
School of Economics, Finance and Business University of Durham 23-26 Old Elvet Durham DH1 3
TI 2000-104/4 Tinbergen Institute Discussion Paper
Forecasting the Variability of Stock F orecasting Index Returns with Stochastic Volatility Models and Implied Volatility
Eugenie Hol Siem Jan Koopman
Tinbergen Institute
The Tinbergen Institute is the ins
Forecasting Volatility: Evidence from the German Stock Market*
Hagen H.W. Bluhma, Jun Yub
February 2001
Abstract In this paper we compare two basic approaches to forecast volatility in the German stock market. The first approach uses various univariate ti
Published in Journal of Business, 51 (4), 1978, 549-564 Forecasting with Econometric Methods: Folklore versus Fact J. Scott Armstrong The Wharton School, University of Pennsylvania
Abstract Evidence from social psychology suggests that econometricians wil
Working Paper 01-08 Statistics and Econometrics Series 05 March 2001
Departamento de Estadstica y Econometra Universidad Carlos III de Madrid Calle Madrid, 126 28903 Getafe (Spain) Fax (34) 91 624-98-49
IS STOCHASTIC VOLATILITY MORE FLEXIBLE THAN GARCH? M
Is Unlevered Firm Volatility Asymmetric?
By Hazem Daouk Cornell University David Ng1 Cornell University This version: January 11, 2007 Abstract We develop a new, unlevering approach to document how well financial and operating leverage explain volatility
Quiz 6 Probability and Statistics
Bernardo Guimaraes September 2009
(For questions 1 to 10) A random sample of size n is drawn from X N ( ; 2 ). Denote an element of the sample by Pn X Xi ; 1 i n. I want to test whether the expected value of X is bigger t
Quiz 4 Probability and Statistics
Bernardo Guimaraes September 2009
1. Which of the following are probability density functions of 2 independent variables? (a) fX;Y (x; y ) = 2; 0 x y 1, otherwise fX;Y (x; y ) = 0. (b) fX;Y (x; y ) = 2; 0 x 1; 0 y 1, othe
Q U A N T I T A T I V E F I N A N C E V O L U M E 1 (2001) 237245 INSTITUTE O F PHYSICS PUBLISHING
RE S E A R C H PA P E R
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What good is a volatility model?
Robert F Engle and Andrew J Patton
Department of Finance, NYU Stern School of Busines
ECON 371 2003 SUMMARY #4 COMPARATIVE STATICS: PROPERTIES OF CONSUMER DEMAND We are now ready to investigate what sort of implications the model we have discussed has on demand behaviour. We have already seen that the (Marshallian) demand functions are hom
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Nov 26, 2008 (Afterclass version 1pm) Lecture Notes for Graduate Level Advanced Microeconomics, Peking University Guanghua School of Management
1/26
We have covered some tools and optimization methods i
Course: EC400 - Introductory Courses in Maths and Statistics
EC400 - Introductory Courses in Maths and Statistics
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Library
Chapter 10: Firms and Technology
10.1: Introduction
So far we have worked in a world without production in a pure-exchange world where people have initial endowments and might want to exchange them. We must now add production to the story. Production is i
Lecture 4
1 2
2.1 2.2 2.3 2.4
Summary of Lecture 1: Technology Summary of Lecture 2: Prot maximization
The prot maximization problem Implications of prot maximization The prot function Cost minimization
3
3.1
Summary of Lecture 3: The cost function and du
Lecture 5
1 Summary of Lectures 1, 2, and 3: Technology, Prot maximization, The cost function and duality Summary of Lecture 4: Consumption theory
Preference orders The utility function The utility maximization problem
Solving the UMP Walrasian demand Ind
Solow Model
1. No Technological Progress
Setup CRS production function: Y = K L1 y = f (k ) = k
13
Constant savings rate:
I = sY
Constant labour force growth rate:
L L=n
Capital accumulation:
K = sY K
Maths:
K K = sY K = s y k k k = K K L L = s y k n k