| Non-linear time series models in empirical finance PH Franses, D Van Dijk Cambridge university press, 2000 | 1770 | 2000 |
| Smooth transition autoregressive models—a survey of recent developments D Dijk, T Teräsvirta, PH Franses Econometric reviews 21 (1), 1-47, 2002 | 1649 | 2002 |
| Panel smooth transition regression models A Gonzalez, T Teräsvirta, D Van Dijk, Y Yang | 1138* | 2017 |
| Forecasting stock market volatility using (non‐linear) Garch models PH Franses, D Van Dijk Journal of forecasting 15 (3), 229-235, 1996 | 575 | 1996 |
| Measuring volatility with the realized range M Martens, D Van Dijk Journal of Econometrics 138 (1), 181-207, 2007 | 460 | 2007 |
| A comparison of biased simulation schemes for stochastic volatility models R Lord, R Koekkoek, DV Dijk Quantitative Finance 10 (2), 177-194, 2010 | 446 | 2010 |
| Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination T Teräsvirta, D Van Dijk, MC Medeiros International Journal of Forecasting 21 (4), 755-774, 2005 | 359 | 2005 |
| Testing for volatility changes in US macroeconomic time series M Sensier, D Dijk Review of Economics and Statistics 86 (3), 833-839, 2004 | 326 | 2004 |
| Time-varying smooth transition autoregressive models S Lundbergh, T Teräsvirta, D Van Dijk Journal of Business & Economic Statistics 21 (1), 104-121, 2003 | 280 | 2003 |
| Contagion as a domino effect in global stock markets T Markwat, E Kole, D Van Dijk Journal of Banking & Finance 33 (11), 1996-2012, 2009 | 277 | 2009 |
| Modeling multiple regimes in the business cycle D Van Dijk, PH Franses Macroeconomic dynamics 3 (3), 311-340, 1999 | 273 | 1999 |
| Asymmetric effects of federal funds target rate changes on S&P100 stock returns, volatilities and correlations H Chuliá, M Martens, D Van Dijk Journal of Banking & Finance 34 (4), 834-839, 2010 | 237 | 2010 |
| Stock selection strategies in emerging markets J Van der Hart, E Slagter, D Van Dijk Journal of Empirical Finance 10 (1-2), 105-132, 2003 | 237 | 2003 |
| A multi‐level panel STAR model for US manufacturing sectors D Fok, D Van Dijk, PH Franses Journal of Applied Econometrics 20 (6), 811-827, 2005 | 228 | 2005 |
| Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements M Martens, D Van Dijk, M De Pooter International Journal of forecasting 25 (2), 282-303, 2009 | 189 | 2009 |
| Likelihood-based scoring rules for comparing density forecasts in tails C Diks, V Panchenko, D Van Dijk Journal of Econometrics 163 (2), 215-230, 2011 | 187 | 2011 |
| Testing for ARCH in the presence of additive outliers D Van Dijk, PH Franses, A Lucas Journal of Applied Econometrics 14 (5), 539-562, 1999 | 173 | 1999 |
| Predicting the daily covariance matrix for s&p 100 stocks using intraday data—but which frequency to use? M Pooter, M Martens, D Dijk Econometric Reviews 27 (1-3), 199-229, 2008 | 166 | 2008 |
| Forecasting day-ahead electricity prices: Utilizing hourly prices E Raviv, KE Bouwman, D Van Dijk Energy Economics 50, 227-239, 2015 | 165 | 2015 |
| A multivariate STAR analysis of the relationship between money and output P Rothman, DJC Dijk, PHBF Franses Econometric Institute, 1999 | 153* | 1999 |
