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Economics and Finance

D-Index
54
Citations
14168
World Ranking
1121
National Ranking
33

Overview

Dick van Dijk is affiliated with Erasmus University Rotterdam in the Netherlands. Their research primarily covers the broad field of Economics, Econometrics and Finance, with a specific focus on Economics and Econometrics, Finance, and General Economics, Econometrics and Finance as subfields. They also explore topics related to Archeology and Urban Studies.

The scientist's work addresses several key topics, including:

  • Market Dynamics and Volatility
  • Financial Risk and Volatility Modeling
  • Monetary Policy and Economic Impact
  • Complex Systems and Time Series Analysis
  • Cultural Heritage Management and Preservation
  • Cultural Industries and Urban Development
  • Statistical Methods and Inference

Dick van Dijk has contributed to multiple peer-reviewed publications over recent years. Notable papers include:

  • Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings, 2020, Journal of Business and Economic Statistics
  • Moments, shocks and spillovers in Markov-switching VAR models, 2023, Journal of Econometrics
  • Accelerating peak dating in a dynamic factor Markov-switching model, 2023, International Journal of Forecasting
  • Pooling Dynamic Conditional Correlation Models, 2021, SSRN Electronic Journal
  • Robust Observation-Driven Models Using Proximal-Parameter Updates, 2022, SSRN Electronic Journal

In addition to journal articles, Dick van Dijk has published books with the European Organization for Nuclear Research. These include multiple editions of "The Mingei Handbook" and "The Mingei Handbook on Heritage Craft representation and preservation," all published in 2022.

The scientist has collaborated frequently with several co-authors, among them:

  • Arnaud Dubois
  • Bram van Os
  • Xenophon Zabulis
  • Nikolaos Partarakis
  • Ilia Adami

Their publications have appeared repeatedly in notable venues, including:

  • SSRN Electronic Journal
  • Journal of Business and Economic Statistics
  • International Journal of Forecasting
  • Zenodo (CERN European Organization for Nuclear Research)
  • Journal of Econometrics

Best Publications

  • Non-Linear Time Series Models in Empirical Finance

    Philip Hans Franses;Dick van Dijk

  • SMOOTH TRANSITION AUTOREGRESSIVE MODELS — A SURVEY OF RECENT DEVELOPMENTS

    Dick van Dijk;Timo Teräsvirta;Philip Hans Franses

  • Time Series Models for Business and Economic Forecasting

    Philip Hans Franses;Dick van Dijk;Anne Opschoor

  • Forecasting stock market volatility using (non-linear) Garch models

    Philip Hans Franses;Dick Van Dijk

  • Panel Smooth Transition Regression Models

    Andrés González;Timo Teräsvirta;Dick van Dijk;Yukai Yang

  • Measuring volatility with the realized range

    Martin Martens;Dick van Dijk

  • A Comparison of Biased Simulation Schemes for Stochastic Volatility Models

    Roger Lord;Remmert Koekkoek;Dick van Dijk

  • Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

    Timo Teräsvirta;Dick van Dijk;Marcelo C. Medeiros

  • Testing for Volatility Changes in U.S. Macroeconomic Time Series

    Marianne Sensier;Dick van Dijk

  • Contagion as a domino effect in global stock markets

    Thijs Markwat;Erik Kole;Dick van Dijk

  • Time-Varying Smooth Transition Autoregressive Models

    Stefan Lundbergh;Timo Teräsvirta;Dick van Dijk

  • Modelling Multiple Regimes in the Business Cycle

    Dick van Dijk;Philip Hans Franses

  • A multi‐level panel STAR model for US manufacturing sectors

    Dennis Fok;Dick van Dijk;Philip Hans Franses

  • Stock Selection Strategies in Emerging Markets

    Jaap van der Hart;Erica Slagter;Dick van Dijk

  • Asymmetric effects of federal funds target rate changes on S&P100 stock returns, volatilities and correlations

    Helena Chulia-Soler;Martin Martens;Dick van Dijk

  • Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements

    Martin Martens;Dick van Dijk;Michiel de Pooter

  • Likelihood-based scoring rules for comparing density forecasts in tails

    Cees Diks;Valentyn Panchenko;Dick van Dijk

  • Testing for ARCH in the presence of additive outliers

    Dick Van Dijk;Philip Hans Franses;André Lucas

  • Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?

    Michiel de Pooter;Martin Martens;Dick van Dijk

  • Forecasting day-ahead electricity prices: Utilizing hourly prices

    Eran Raviv;Kees E. Bouwman;Dick van Dijk;Dick van Dijk;Dick van Dijk

Frequent Co-Authors

Philip Hans Franses
Philip Hans Franses Erasmus University Rotterdam
Andre Lucas
Andre Lucas Vrije Universiteit Amsterdam
Denise R. Osborn
Denise R. Osborn University of Manchester
Timo Teräsvirta
Timo Teräsvirta Aarhus University
Siem Jan Koopman
Siem Jan Koopman Vrije Universiteit Amsterdam
Jonathan H. Wright
Jonathan H. Wright Johns Hopkins University
A. M. Robert Taylor
A. M. Robert Taylor University of Essex
Norman R. Swanson
Norman R. Swanson Rutgers, The State University of New Jersey
Michael P. Clements
Michael P. Clements University of Reading
Jeremy Smith
Jeremy Smith University of Warwick

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