My current research is concentrated around instabilities of the financial system, market risks and credit risk modeling on P2P lending markets.
72
Scopus Publications
2923
Scholar Citations
29
Scholar h-index
55
Scholar i10-index
Scopus Publications
A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets Shafqat Iqbal, Štefan Lyócsa Journal of Forecasting, 2026 This study introduces a realized volatility fuzzy time series (RV‐FTS) model that applies a fuzzy c‐means clustering algorithm to estimate time‐varying latent volatility states and their corresponding membership degrees. These memberships are used to construct a fuzzified volatility estimate as a weighted average of cluster centroids. The final volatility forecast is generated through an exponentially weighted moving average (EWMA) mechanism that combines the most recent fuzzified volatility estimate with the previous forecast, governed by the smoothing parameter . The two hyperparameters are estimated using a rolling‐window cross‐validation approach. Our empirical study is based on volatility forecasts for 14 major stock market indices, covering more than 20 years of data. We predict 1‐ to 22‐day‐ahead volatility and compare the RV‐FTS model with nine standard volatility model benchmarks: generalized autoregressive conditional heteroscedasticity (GARCH), ARFIMA, AR, heterogeneous autoregressive (HAR), EWMA, and random forest models, as well as conditional combination forecasts. We find that, in the short‐term, day‐ahead setting, the RV‐FTS model tends to outperform the benchmark models under the mean squared error loss and performs similarly to the best models under the QLIKE loss. The conditional combination forecast shows that across all markets and multiple forecast horizons, there are periods when the weight of the RV‐FTS model in the conditional combination of eight models reaches 50% or more. The volatility timing strategy also shows that the RV‐FTS model leads to higher cost‐ and risk‐adjusted returns compared with a benchmark volatility model.
Has ChatGPT Disrupted the Education Sector in the U.S.? Erik Haugom, Štefan Lyócsa, Martina Halousková Social Science Computer Review, 2026 The introduction of ChatGPT and other tools based on artificial intelligence (AI) has the potential to revolutionize the field of education. We study how the public release of ChatGPT and the increased attention on this new large language model from OpenAI are associated with the expected returns of publicly traded firms that operate in the education sector. We also perform separate subgroup analyses for the traditional education sector and the so-called education technology sector. Using linear and threshold CAPM-GARCH models, we find that after the public release of ChatGPT, both the education sector as a whole and the education technology sector have underperformed benchmarks. Our results show that increased attention leads to lower next-day returns in the education sector as a whole and the education technology sector in particular. Additionally, during periods of higher attention, expected returns tend to decline in these two sectors. We also show that the introduction of ChatGPT or the increased interest in this AI tool in the population does not affect the traditional education sector. The introduction of ChatGPT thus has a heterogeneous effect across the various education sectors we examine, with the education technology sector receiving most of the disruption.
A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets S Iqbal, Š Lyócsa Journal of Forecasting 45 (3), 1261-1291 , 2026 2026
Has ChatGPT disrupted the Education Sector in the US? E Haugom, Š Lyócsa, M Halousková Social Science Computer Review 44 (2), 185-208 , 2026 2026 Citations: 5
Attention to renewable energy: A risk-factor for stocks is the renewable energy sector Š Lyócsa, J Tabaček Research in International Business and Finance, 103204 , 2025 2025 Citations: 1
Cross-border and cross-regional electricity transmission: Is there a price impact in south Norway? V Bendiksen, LOF Løining, Š Lyócsa Energy Economics, 108878 , 2025 2025 Citations: 1
Peer-to-peer loan returns: heterogeneous effects across quantiles Š Lyócsa, P Vašaničová, O Deev Applied Economics Letters 32 (7), 960-965 , 2025 2025 Citations: 5
Forecasting US equity market volatility with attention and sentiment to the economy M Halousková, Š Lyócsa arXiv preprint arXiv:2503.19767 , 2025 2025 Citations: 6
The Robustness of Sentiment Factors in Quantitative Investment Strategies O Deev, Š Lyócsa, T Plíhal, D Stašek Masarykova univerzita , 2025 2025
Do hurricanes cause storm on the stock market? The case of US energy companies R Horvath, A Kalistova, Š Lyócsa, M Miškufová, M Moravcova International Review of Financial Analysis 97, 103816 , 2025 2025 Citations: 6
What drives the uranium sector risk? The role of attention, economic and geopolitical uncertainty Š Lyócsa, N Todorova Energy Economics 140, 107980 , 2024 2024 Citations: 22
Macroeconomic environment and the future performance of loans: Evidence from three peer-to-peer platforms E Baumöhl, Š Lyócsa, P Vašaničová International Review of Financial Analysis 95, 103416 , 2024 2024 Citations: 9
Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I) relevance of implied volatility Š Lyócsa, T Plíhal, T Výrost International Journal of Forecasting 40 (4), 1275-1301 , 2024 2024 Citations: 9
Forecasting of clean energy market volatility: The role of oil and the technology sector Š Lyócsa, N Todorova Energy Economics 132, 107451 , 2024 2024 Citations: 25
The tipping point of electricity price attention: When a problem becomes a problem E Haugom, Š Lyócsa, M Halousková Economics Letters 235, 111547 , 2024 2024 Citations: 5
Liquidity Benchmarks and Proxies: Predicting Price Variation on the US Equity Market D Stašek, S Lyocsa Available at SSRN 4606040 , 2024 2024 Citations: 2
The US banking crisis in 2023: Intraday attention and price variation of banks at risk Š Lyócsa, M Halousková, E Haugom Finance Research Letters 57, 104209 , 2023 2023 Citations: 18
Using online job postings to predict key labour market indicators M Štefánik, Š Lyócsa, M Bilka Social Science Computer Review 41 (5), 1630-1649 , 2023 2023 Citations: 8
The tipping point of electricity price attention E Haugom, S Lyocsa, M Halousková Available at SSRN 4471745 , 2023 2023
The financial impact of ChatGPT for the higher education industry in the US E Haugom, S Lyocsa, M Halousková SSRN Scholarly Paper. Rochester, NY. https://doi. org/10.2139/ssrn 4573714 , 2023 2023 Citations: 2
The looming crisis in the Chinese stock market? Left-tail exposure analysis of Chinese stocks to Evergrande O Deev, Š Lyócsa, T Výrost Finance Research Letters 49, 103154 , 2022 2022 Citations: 14
MOST CITED SCHOLAR PUBLICATIONS
Fear of the coronavirus and the stock markets Š Lyócsa, E Baumöhl, T Výrost, P Molnár Finance research letters 36, 101735 , 2020 2020 Citations: 313
Impact of macroeconomic news, regulation and hacking exchange markets on the volatility of bitcoin Š Lyócsa, P Molnár, T Plíhal, M Širaňová Journal of Economic Dynamics and Control 119, 103980 , 2020 2020 Citations: 197
Granger causality stock market networks: Temporal proximity and preferential attachment T Výrost, Š Lyócsa, E Baumöhl Physica A: Statistical Mechanics and its Applications 427, 262-276 , 2015 2015 Citations: 141
YOLO trading: Riding with the herd during the GameStop episode Š Lyócsa, E Baumöhl, T Výrost Finance Research Letters 46, 102359 , 2022 2022 Citations: 140
Networks of volatility spillovers among stock markets E Baumöhl, E Kočenda, Š Lyócsa, T Výrost Physica A: Statistical Mechanics and its Applications 490, 1555-1574 , 2018 2018 Citations: 103
Determinants of Commercial Banks' Efficiency: Evidence from 11 CEE Countries* D Pancurová, S Lyócsa Finance a Uver 63 (2), 152 , 2013 2013 Citations: 95
Stock market volatility forecasting: Do we need high-frequency data? Š Lyócsa, P Molnár, T Výrost International Journal of Forecasting 37 (3), 1092-1110 , 2021 2021 Citations: 92
Directional predictability from stock market sector indices to gold: A cross-quantilogram analysis E Baumöhl, Š Lyócsa Finance Research Letters 23, 152-164 , 2017 2017 Citations: 91
Stock market oscillations during the corona crash: The role of fear and uncertainty Š Lyócsa, P Molnár Finance Research Letters 36, 101707 , 2020 2020 Citations: 88
Volatility and dynamic conditional correlations of worldwide emerging and frontier markets E Baumöhl, Š Lyócsa Economic Modelling 38, 175-183 , 2014 2014 Citations: 87
Asymmetric volatility in equity markets around the world JB Horpestad, Š Lyócsa, P Molnár, TB Olsen The North American Journal of Economics and Finance 48, 540-554 , 2019 2019 Citations: 86
Network-based asset allocation strategies T Výrost, Š Lyócsa, E Baumöhl The North American Journal of Economics and Finance 47, 516-536 , 2019 2019 Citations: 78
Stationarity of time series and the problem of spurious regression E Baumöhl, Š Lyócsa MPRA Paper 27926 , 2009 2009 Citations: 78
Russia’s ruble during the onset of the Russian invasion of Ukraine in early 2022: the role of implied volatility and attention Š Lyócsa, T Plíhal Finance Research Letters 48, 102995 , 2022 2022 Citations: 76
Stock market networks: The dynamic conditional correlation approach Š Lyócsa, T Výrost, E Baumöhl Physica A: Statistical Mechanics and its Applications 391 (16), 4147-4158 , 2012 2012 Citations: 69
Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets Š Lyócsa, P Vašaničová, B Hadji Misheva, MD Vateha Financial Innovation 8 (1), 32 , 2022 2022 Citations: 64
Return spillovers around the globe: A network approach Š Lyócsa, T Výrost, E Baumöhl Economic Modelling 77, 133-146 , 2019 2019 Citations: 57
On the evaluation of Six Sigma projects M Tkáč, Š Lyócsa Quality and reliability engineering international 26 (1), 115-124 , 2010 2010 Citations: 57
Stock market contagion in Central and Eastern Europe: unexpected volatility and extreme co-exceedance R Horváth, Š Lyócsa, E Baumöhl The European Journal of Finance 24 (5), 391-412 , 2018 2018 Citations: 52
Exploiting dependence: Day-ahead volatility forecasting for crude oil and natural gas exchange-traded funds Š Lyócsa, P Molnár Energy 155, 462-473 , 2018 2018 Citations: 46