Stan Hurn

A general test for time dependence in parameters

Publisher: Wiley

Date: 17-05-2004

DOI: 10.1002/JAE.751

Modeling the lifespan of human T lymphocyte subsets

Publisher: Elsevier BV

Date: 07-1997

DOI: 10.1016/S0025-5564(97)00007-2

Abstract: T lymphocytes may be classified as naive or memory cells, depending on whether they possess immunological memory. The central tenet of this work is that memory lymphocytes revert to naive lymphocytes in vivo. This phenomenon is modeled by a mechanism qualitatively similar to Demoivre's law of human mortality. Additionally, both lymphocyte subsets undergo decay. Model parameters and their standard errors are estimated by maximizing a likelihood function constructed by Kalman filtering of the experimental data on the assumption that it is contaminated by measurement noise of constant relative error.

A Comparative Study of Likelihood Approximations for Univariate Diffusions

Publisher: Oxford University Press (OUP)

Date: 06-11-2023

DOI: 10.1093/JJFINEC/NBAB021

Abstract: Maximum likelihood estimation of the parameters of stochastic differential equations commonly used in finance requires numerical approximation of their transitional probability density functions. This article undertakes a comparative study of the accuracy of Hermite polynomial expansion approximations for univariate diffusions and checks how the accuracy of the existing methods responds to increasing the order of the approximation. It is found that one class of expansion dealing with irreducible diffusions is particularly problematic due to the need to evaluate a number of troublesome integrals. A Gaussian quadrature is introduced which resolves the problem and improves the reliability of the expansion. A simulation study demonstrates all the methods in action and provides insight into the practical aspects of using these expansions. An empirical application using data on the VIX indicates that the proposed method based on the Gaussian quadrature performs very well when applied to financial data.

Forecasting spikes in electricity prices

Publisher: Elsevier BV

Date: 04-2012

DOI: 10.1016/J.IJFORECAST.2011.02.019

Strategic bidding and rebidding in electricity markets

Publisher: Elsevier BV

Date: 09-2016

DOI: 10.1016/J.ENECO.2016.07.011

A quasi-maximum likelihood method for estimating the parameters of multivariate diffusions

Publisher: Elsevier BV

Date: 2013

DOI: 10.1016/J.JECONOM.2012.09.002

Volatility transmission in global financial markets

Publisher: Elsevier BV

Date: 06-2015

DOI: 10.1016/J.JEMPFIN.2014.12.002

Detecting common dynamics in transitory components

Publisher: Walter de Gruyter GmbH

Date: 03-01-2011

DOI: 10.2202/1941-1928.1088

Testing for time-varying Granger causality

Publisher: SAGE Publications

Date: 06-2022

DOI: 10.1177/1536867X221106403

Abstract: The concept of Granger causality is an important tool in applied macroeconomics. Recently, recursive econometric methods have been developed to analyze the temporal stability of Granger-causal relationships. This article offers an implementation of these recursive procedures in Stata. An empirical ex le illustrates their use in analyzing the temporal stability of Granger causality among key U.S. macroeconomic series.

Estimating the Parameters of Stochastic Volatility Models Using Option Price Data

Publisher: Informa UK Limited

Date: 02-10-2015

DOI: 10.1080/07350015.2014.981634

Asymmetric price adjustment and the Phillips curve

Publisher: Elsevier BV

Date: 09-2002

DOI: 10.1016/S0164-0704(02)00044-7

Modelling interregional links in electricity price spikes

Publisher: Elsevier BV

Date: 09-2015

DOI: 10.1016/J.ENECO.2015.07.014

Forecasting day-ahead electricity load using a multiple equation time series approach

Publisher: Elsevier BV

Date: 06-2016

DOI: 10.1016/J.EJOR.2015.12.030

Asset pricing puzzles in finance: Introduction

Publisher: Elsevier BV

Date: 08-2006

DOI: 10.1016/J.NAJEF.2006.03.001

Mobius-Like Mappings and Their Use in Kernel Density Estimation

Publisher: Informa UK Limited

Date: 12-2003

DOI: 10.1198/016214503000000945

Using discrete-time techniques to test continuous-time models for nonlinearity in drift

Publisher: Elsevier BV

Date: 2004

DOI: 10.1016/J.MATCOM.2003.07.001

On the specification of the drift and diffusion functions for continuous-time models of the spot interest rate

Publisher: Wiley

Date: 12-2002

DOI: 10.1111/1468-0084.00277

Momentum in Australian stock returns

Publisher: SAGE Publications

Date: 09-2003

DOI: 10.1177/031289620302800202

Abstract: Medium-term momentum, or the tendency of investment strategies based on buying past winning stocks while selling past losing stocks to maintain above normal performance over a period, has been a well-documented feature of stock returns in the US. We investigate the performance of momentum investment strategies in portfolios of Australian stocks and examine some of the common explanations and empirical features of momentum. The paper establishes the presence of a strong medium-term momentum effect, which cannot be completely accounted for by any of the possible explanations considered in this paper.

Identifying aggregate demand and supply shocks in a small open economy

Publisher: Oxford University Press (OUP)

Date: 26-04-2007

DOI: 10.1093/OEP/GPL029

Semi‐parametric Forecasting of Spikes in Electricity Prices

Publisher: Wiley

Date: 26-08-2013

DOI: 10.1111/1475-4932.12072

On the efficacy of Fourier series approximations for pricing European options

Publisher: Scientific Research Publishing, Inc.

Date: 2014

DOI: 10.4236/AM.2014.517267

Seeing the wood for the trees: A critical evaluation of methods to estimate the parameters of stochastic differential equations

Publisher: Oxford University Press (OUP)

Date: 08-03-2007

DOI: 10.1093/JJFINEC/NBM009

Econometric modelling with time series: Specification, estimation and testing

Publisher: Cambridge University Press

Date: 28-12-2010

DOI: 10.1017/CBO9781139043205

Abstract: This book provides a general framework for specifying, estimating and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalised method of moments estimation, nonparametric estimation and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.

Unobservable cyclical components in term premia of fixed-term financial instruments

Publisher: Elsevier BV

Date: 11-1995

DOI: 10.1016/0378-4754(94)00091-1

The dynamics of U.S. industrial production: A time-varying Granger causality perspective

Publisher: Elsevier BV

Date: 10-2021

DOI: 10.1016/J.ECOSTA.2021.10.012

Practitioners' Forum

Publisher: Elsevier BV

Date: 09-2008

DOI: 10.1016/S0313-5926(08)50024-5

Common trends in global volatility

Publisher: Elsevier BV

Date: 10-2016

DOI: 10.1016/J.JIMONFIN.2016.05.001

Selecting volatility forecasting models for portfolio allocation purposes

Publisher: Elsevier BV

Date: 07-2015

DOI: 10.1016/J.IJFORECAST.2013.11.007

On the efficacy of simulated maximum likelihood for estimating the parameters of stochastic differential equations

Publisher: Wiley

Date: 2003

DOI: 10.1111/1467-9892.00292

Testing the profitability of moving-average rules as a portfolio selection strategy

Publisher: Elsevier BV

Date: 11-2012

DOI: 10.1016/J.PACFIN.2012.04.003

Mixture distribution‐based forecasting using stochastic volatility models

Publisher: Wiley

Date: 09-2006

DOI: 10.1002/ASMB.647

Abstract: Many traditional econometric methods forecast the conditional distribution of asset returns by a point prediction of volatility. Alternatively, forecasts of this distribution may be generated from a mixture of distributions. This paper proposes a method by which information extracted from the estimation of a standard stochastic volatility model (using non‐linear filtering) can be used to generate mixture distribution forecasts. In general, it is found that forecasts based on mixture distributions are superior to those simply using point predictions of volatility. In terms of mixture distribution forecasts, the method proposed in this paper is found to be superior to a number of competing approaches. Copyright © 2006 John Wiley & Sons, Ltd.

The Devil is in the Detail: Hints for Practical Optimisation

Publisher: Elsevier BV

Date: 09-2008

DOI: 10.1016/S0313-5926(08)50025-7

Testing for Nonlinearity in Mean in the Presence of Heteroskedasticity

Publisher: Elsevier BV

Date: 09-2009

DOI: 10.1016/S0313-5926(09)50023-9

A closed-form approximation for pricing temperature-based weather derivatives

Publisher: Scientific Research Publishing, Inc.

Date: 2013

DOI: 10.4236/AM.2013.49182

Dollar-Deutschemark polarisation: Comparing the pound and franc

Publisher: Wiley

Date: 28-07-2003

DOI: 10.1111/1467-9485.5003007

Modelling spikes in electricity prices

Publisher: Wiley

Date: 12-2007

DOI: 10.1111/J.1475-4932.2007.00427.X

A Smooth Transition Logit Model of The Effects of Deregulation in the Electricity Market

Publisher: Wiley

Date: 18-03-2015

DOI: 10.1002/JAE.2452

Queensland University Of Technology

Location: Australia

View Organisation

Novel Econometric Techniques For Modelling And Forecasting Electricity Prices And Price Volatility In Australia

Start Date: 2008

End Date: 2010

Funder: Australian Research Council

Understanding And Modelling Weather Derivatives In Australia For The Purpose Of Their Accurate Pricing: A Statistical And Econometric Investigation

Start Date: 2006

End Date: 2008

Funder: Australian Research Council

Change Detection In Causal Relationships And Measurement Of Systemic Risk

Start Date: 2015

End Date: 2017

Funder: Australian Research Council

Novel Econometric Techniques For Dealing With Point Processes In High Frequency Financial Data With Applications To Financial Risk Management

Start Date: 2012

End Date: 2014

Funder: Australian Research Council

Linkage - International - Grant ID: LX0560332

Start Date: 11-2004

End Date: 06-2007

Amount: $55,624.00

Funder: Australian Research Council

Linkage - International - Grant ID: LX0455433

Start Date: 11-2004

End Date: 06-2008

Amount: $21,268.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0561082

Start Date: 08-2005

End Date: 12-2007

Amount: $26,295.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0666266

Start Date: 2006

End Date: 03-2009

Amount: $200,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0453501

Start Date: 09-2004

End Date: 12-2005

Amount: $406,097.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP150101716

Start Date: 06-2015

End Date: 12-2018

Amount: $404,700.00

Funder: Australian Research Council

Research Networks - Grant ID: RN0460246

Start Date: 08-2005

End Date: 11-2009

Amount: $1,750,000.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0354895

Start Date: 01-2004

End Date: 12-2005

Amount: $40,000.00

Funder: Australian Research Council

Linkage - International - Grant ID: LX0882226

Start Date: 2008

End Date: 06-2011

Amount: $32,700.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP120100837

Start Date: 2012

End Date: 12-2015

Amount: $100,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0344083

Start Date: 07-2003

End Date: 06-2006

Amount: $90,000.00

Funder: Australian Research Council