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3.
ASSESSING VOLATILITY FORECASTING
MODELS: WHY GARCH MODELS TAKE
THE LEAD
1
Marius MATEI
Abstract
The paper provides a critical assessment of the main forecasting techniques and an
evaluation of the superiority of the more advanced and complex models. Ul
Forecasting Stock Return Volatility in the
Presence of Structural Breaks
David E. Rapach
Saint Louis University
Jack K. Strauss
Saint Louis University
Mark E. Wohar
University of Nebraska at Omaha
September 29, 2007
Abstract
We examine the role of structu
Stochastic volatility: option pricing using a multinomial
recombining tree
Ionut Florescu1,3 and Frederi G. Viens2,4
1
Department of Mathematical Sciences, Stevens Institute of Technology,
Castle Point on the Hudson, Hoboken, NJ 07030
2
Department of Stat
Metodoloki zvezki, Vol. 2, No. 2, 2005, 243-257
s
Properties and Estimation of GARCH(1,1) Model
Petra Posedel1
Abstract
We study in depth the properties of the GARCH(1,1) model and the assumptions on the parameter space under which the process is stationa
Pricing and Portfolio Optimization Analysis
in Defaultable Regime-Switching Markets
Agostino Capponi
Jos E. Figueroa-Lpez
e
o
Jerey Nisen
Abstract
We analyze pricing and portfolio optimization problems in defaultable regime switching markets driven by a
u
Modeling Asymmetric Volatility Clusters Using Copulas
and High Frequency Data
Cathy Ning a , Dinghai Xub , and Tony S. Wirjantoc
a
b
Department of Economics, Ryerson University,Toronto, Ontario, Canada, M5B 2K3.
Department of Economics, University of Wate
International Statistical Institute (ISI) and Bernoulli Society for Mathematical Statistics
and Probability
Maximum Likelihood Estimation of Pure GARCH and ARMA-GARCH Processes
Author(s): Christian Francq and Jean-Michel Zakoan
Reviewed work(s):
Source: B
PORTFOLIO OPTIMIZATION WITH CONSUMPTION IN A
FRACTIONAL BLACK-SCHOLES MARKET
YALCIN SAROL, FREDERI G. VIENS, AND TAO ZHANG
Abstract. We consider the classical Merton problem of nding the optimal
consumption rate and the optimal portfolio in a Black-Schole
Estimation and Pricing under Long-Memory
Stochastic Volatility
Alexandra Chronopoulou
Frederi G. Viens
Department of Statistics, Purdue University
150 N. University St. West Lafayette
IN 47907-2067, USA.
[email protected][email protected] (correspo
MODELLING ANTICIPATIONS ON FINANCIAL MARKETS
FABRICE BAUDOIN
Abstract. The aim of the present course is to give a review of the modern mathematical tools
which can be used on a nancial market by a small investor who possesses some informations
on the pric
Multivariate Volatility Models
Author(s): Ruey S. Tsay
Reviewed work(s):
Source: Lecture Notes-Monograph Series, Vol. 52, Time Series and Related Topics: In Memory
of Ching-Zong Wei (2006), pp. 210-222
Published by: Institute of Mathematical Statistics
St
Portfolio Optimization with Discrete Proportional
Transaction Costs under Stochastic Volatility
HA-YOUNG KIM
Department of Mathematics, Purdue University
150 N. University St.,
West Lafayette, IN 47907-2067, USA
FREDERI G. VIENS
Department of Statistics a
Journal of Data Science 6(2008), 273-301
A Copula-based Approach to Option Pricing and
Risk Assessment
Shang C. Chiou1 and Ruey S. Tsay2
Sachs Group Inc. and 2 University of Chicago
1 Goldman
Abstract: Copulas are useful tools to study the relationship be
Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts
Author(s): Vedat Akgiray
Reviewed work(s):
Source: The Journal of Business, Vol. 62, No. 1 (Jan., 1989), pp. 55-80
Published by: The University of Chicago Press
Stable
Forthcoming: Journal of Futures Markets
Forecasting Volatility
Louis H. Ederington
University of Oklahoma
Wei Guan
University of South Florida St. Petersburg
September 2004
Contact Info: Louis Ederington: Finance Division, Michael F. Price College of Busi
TI 2011-125/4
Tinbergen Institute Discussion Paper
Forecasting Volatility with CopulaBased Time Series Models
Oleg Sokolinskiy
Dick van Dijk*
Erasmus University Rotterdam, Tinbergen Institute.
* Econometric Institute
Tinbergen Institute is the graduate sc
Economics Department
Economics Working Papers
The University of Auckland
Year
Forecasting Volatility in the New Zealand
Stock Market
Jun Yu
University of Auckland, [email protected]
This paper is posted at [email protected]
http:/researchspace.auckl
Economics Department
Economics Working Papers
The University of Auckland
Year
Forecasting Volatility:Evidence from the
German Stock Market
Hagen Bluhm
University of Auckland, [email protected]
This paper is posted at [email protected]
http:/rese
1
Derivatives Pricing, Hedging and Risk
Management:
The State of the Art
1.1
INTRODUCTION
The purpose of this chapter is to give a brief review of the basic concepts used in nance
for the purpose of pricing contingent claims. As our book is focusing on th
Copula Processes
Andrew Gordon Wilson
Department of Engineering
University of Cambridge
[email protected]
Zoubin Ghahramani
Department of Engineering
University of Cambridge
[email protected]
Abstract
We dene a copula process which describes the dependen
Continuous-Time GARCH Processes
Author(s): Peter Brockwell, Erdenebaatar Chadraa and Alexander Lindner
Reviewed work(s):
Source: The Annals of Applied Probability, Vol. 16, No. 2 (May, 2006), pp. 790-826
Published by: Institute of Mathematical Statistics
A Monte-Carlo method for portfolio optimization
under partially observed stochastic volatility
Rahul Desai, Tanmay Lele , and Frederi Viens
Department of Mathematics and School of Elec. and Comp. Engr., Purdue University, West Lafayette, IN 47907, U.S.A
S
Bayesian Portfolio Selection: An Empirical Analysis of the S&P 500 Index 1970-1996
Author(s): Nicholas G. Polson and Bernard V. Tew
Reviewed work(s):
Source: Journal of Business & Economic Statistics, Vol. 18, No. 2 (Apr., 2000), pp. 164-173
Published by:
Bayesian Analysis of Stochastic Volatility Models
Author(s): Eric Jacquier, Nicholas G. Polson, Peter E. Rossi
Reviewed work(s):
Source: Journal of Business & Economic Statistics, Vol. 20, No. 1, Twentieth Anniversary
Commemorative Issue (Jan., 2002), pp.
AppendixBWinbugs.fm Page 305 Friday, August 27, 2004 11:57 AM
B
Introduction to WinBUGS
B.1
INTRODUCTION
WinBUGS (the MS Windows operating system version of BUGS: Bayesian
Analysis Using Gibbs Sampling) is a versatile package that has been designed to
car
An introduction to Copulas
An introduction to Copulas
Carlo Sempi
Dipartimento di Matematica Ennio De Giorgi
Universit del Salento
Lecce, Italy
[email protected]
The 33rd Finnish Summer School on Probability Theory and
Statistics, June 6th10th, 20
A Practical Guide to Volatility Forecasting
through Calm and Storm
Christian Brownlees
Robert Engle
Bryan Kelly
Abstract
We present a volatility forecasting comparative study within the ARCH class of models.
Our goal is to identify successful predictive m
Applied Financia l Econo m ics, 2000, 10, 435 448
Forecasting UK stock m arket volatility
D A V I D M C M I L L A N , A L A N S P E I G H T * cfw_ and OW A I N A PG W I L Y M
Department of Economics, University of St Andrews, Fife, KY 16 9AL cfw_ Departm