| Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data T Lux, M Segnon, R Gupta Energy Economics 56, 117-133, 2016 | 119 | 2016 |
| The role of economic policy uncertainty in predicting US recessions: A mixed-frequency Markov-switching vector autoregressive approach M Balcilar, R Gupta, M Segnon Economics 10 (1), 20160027, 2016 | 104 | 2016 |
| Modeling and forecasting the volatility of carbon dioxide emission allowance prices: A review and comparison of modern volatility models M Segnon, T Lux, R Gupta Renewable and Sustainable Energy Reviews 69, 692-704, 2017 | 101 | 2017 |
| Forecasting the price of gold H Hassani, ES Silva, R Gupta, MK Segnon Applied Economics 47 (39), 4141-4152, 2015 | 90 | 2015 |
| Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks M Segnon, R Gupta, B Wilfling International Journal of Forecasting 40 (1), 29-43, 2024 | 36 | 2024 |
| Forecasting US GNP growth: The role of uncertainty M Segnon, R Gupta, S Bekiros, ME Wohar Journal of Forecasting 37 (5), 541-559, 2018 | 33 | 2018 |
| High-frequency volatility forecasting of US housing markets M Segnon, R Gupta, K Lesame, ME Wohar The Journal of Real Estate Finance and Economics 62, 283-317, 2021 | 28 | 2021 |
| Multifractal models in finance: Their origin, properties, and applications T Lux, M Segnon | 26 | 2018 |
| Forecasting volatility in bitcoin market M Segnon, S Bekiros Annals of Finance 16 (3), 435-462, 2020 | 19 | 2020 |
| Multifractal models in finance: Their origin, properties, and applications M Segnon, T Lux Kiel working paper, 2013 | 16 | 2013 |
| Forecasting market risk of portfolios: copula-Markov switching multifractal approach M Segnon, M Trede The European Journal of Finance 24 (14), 1123-1143, 2018 | 15 | 2018 |
| Modeling and forecasting crude oil price volatility: Evidence from historical and recent data T Lux, M Segnon, R Gupta FinMaP-Working Paper, 2015 | 13 | 2015 |
| Forecasting home sales in the four census regions and the aggregate US economy using singular spectrum analysis H Hassani, Z Ghodsi, R Gupta, M Segnon Computational Economics 49, 83-97, 2017 | 12 | 2017 |
| Revisiting the twin deficits hypothesis: a quantile cointegration analysis over the period 1791-2013 N Antonakakis, J Cunado, R Gupta, M Segnon Journal of Applied Economics 22 (1), 117-131, 2019 | 11 | 2019 |
| Forecasting volatility in cryptocurrency markets M Segnon, S Bekiros Center for Quantitative Econmics 79 (1), 1-37, 2019 | 5 | 2019 |
| Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-type Volatility Models M Segnon, T Lux, R Gupta FinMap-Working Paper, 2015 | 5 | 2015 |
| Multifractal models in finance: Their origin, properties M Segnon, T Lux and applications, 2013 | 5 | 2013 |
| Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data M Segnon, CK Lau, B Wilfling, R Gupta Studies in Nonlinear Dynamics & Econometrics 26 (1), 73-98, 2022 | 2 | 2022 |
| Long memory conditional heteroscedasticity in count data M Segnon, M Stapper CQE Working Papers, 2019 | 2 | 2019 |
| Financial-market volatility prediction with multiplicative Markov-switching MIDAS components B Schulte-Tillman, M Segnon, B Wilfling CQE Working Papers, 2022 | 1 | 2022 |
