Springer series in statistics
P Bickel, P Diggle, S Fienberg, U Gather, I Olkin… - Principles and Theory …, 2009 - Springer
The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …
Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics
OE Barndorff‐Nielsen… - Journal of the Royal …, 2001 - Wiley Online Library
Non‐Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing
important distributional deviations from Gaussianity and for flexible modelling of …
important distributional deviations from Gaussianity and for flexible modelling of …
[كتاب][B] Simulation and inference for stochastic differential equations: with R examples
SM Iacus - 2008 - Springer
Stochastic di? erential equations model stochastic evolution as time evolves. These models
have a variety of applications in many disciplines and emerge naturally in the study of many …
have a variety of applications in many disciplines and emerge naturally in the study of many …
[كتاب][B] Parameter estimation in stochastic differential equations
JPN Bishwal - 2007 - books.google.com
Parameter estimation in stochastic differential equations and stochastic partial differential
equations is the science, art and technology of modelling complex phenomena and making …
equations is the science, art and technology of modelling complex phenomena and making …
Operator methods for continuous-time Markov processes
Y Aït-Sahalia, LP Hansen, JA Scheinkman - Handbook of financial …, 2010 - Elsevier
Publisher Summary This chapter surveys a set of mathematical and statistical tools that are
valuable in understanding and characterizing nonlinear Markov processes. Such processes …
valuable in understanding and characterizing nonlinear Markov processes. Such processes …
Estimating functions for discretely observed diffusions: A review
M Sørensen - Lecture Notes-Monograph Series, 1997 - JSTOR
Several estimating functions for discretely observed diffusion processes are reviewed. First
we discuss simple explicit estimating functions based on Gaussian approximations to the …
we discuss simple explicit estimating functions based on Gaussian approximations to the …
Stochastic volatility models as hidden Markov models and statistical applications
V Genon-Catalot, T Jeantheau, C Larédo - 2000 - projecteuclid.org
This paper deals with the fixed sampling interval case for stochastic volatility models. We
consider a two-dimensional diffusion process (Y t, V t), where only (Y t) is observed at n …
consider a two-dimensional diffusion process (Y t, V t), where only (Y t) is observed at n …
Parametric inference for diffusion processes observed at discrete points in time: a survey
H Sørensen - International Statistical Review, 2004 - Wiley Online Library
This paper is a survey of estimation techniques for stationary and ergodic diffusion
processes observed at discrete points in time. The reader is introduced to the following …
processes observed at discrete points in time. The reader is introduced to the following …
Hyperbolic processes in finance
BM Bibby, M Sørensen - Handbook of heavy tailed distributions in finance, 2003 - Elsevier
Distributions that have tails heavier than the normal distribution are ubiquitous in finance.
For purposes such as risk management and derivative pricing it is important to use relatively …
For purposes such as risk management and derivative pricing it is important to use relatively …
[كتاب][B] Parameter estimation in stochastic volatility models
JPN Bishwal - 2022 - Springer
In this book, we study stochastic volatility models and methods of pricing, hedging, and
estimation. Among models, we will study models with heavy tails and long memory or long …
estimation. Among models, we will study models with heavy tails and long memory or long …