IDEAS home Printed from https://ideas.repec.org/f/c/pfe264.html
   My authors  Follow this author

Matthias R. Fengler

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Matthias R. Fengler & Jeannine Polivka, 2024. "Proxy-identification of a structural MGARCH model for asset returns," Swiss Finance Institute Research Paper Series 24-55, Swiss Finance Institute.

    Cited by:

    1. Fengler, Matthias & Polivka, Jeannine, 2022. "Structural Volatility Impulse Response Analysis," Economics Working Paper Series 2211, University of St. Gallen, School of Economics and Political Science, revised Nov 2022.

  2. Matthias R. Fengler & Jeannine Polivka, 2024. "Structural Volatility Impulse Response Analysis," Swiss Finance Institute Research Paper Series 24-63, Swiss Finance Institute.

    Cited by:

    1. Hafner, Christian M. & Herwartz, Helmut, 2023. "Correlation impulse response functions," Finance Research Letters, Elsevier, vol. 57(C).

  3. Fengler, Matthias & Phan, Minh Tri, 2023. "A Topic Model for 10-K Management Disclosures," Economics Working Paper Series 2307, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Tri Minh Phan, 2024. "Sentiment-semantic word vectors: A new method to estimate management sentiment," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-22, December.
    2. Simon Jehnen & Joaqu'in Ordieres-Mer'e & Javier Villalba-D'iez, 2025. "FinTextSim: Enhancing Financial Text Analysis with BERTopic," Papers 2504.15683, arXiv.org.

  4. Brown, Martin & Fengler, Matthias & Huwyler, Jonas & Koeniger, Winfried & Lalive, Rafael & Rohrkemper, Robert, 2023. "Monitoring Consumption Switzerland: Data, Background, and Use Cases," Economics Working Paper Series 2301, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Burstein, Ariel & Lein, Sarah & Vogel, Jonathan, 2024. "Cross-border shopping: Evidence and welfare implications for Switzerland," Journal of International Economics, Elsevier, vol. 152(C).
    2. Winfried Koeniger & Peter Kress & Jonas Lehmann, 2024. "Consumption Expenditures in Austria & Germany: New Evidence Based on Transactional Data," Swiss Finance Institute Research Paper Series 24-105, Swiss Finance Institute.

  5. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2019. "Media-expressed tone, Option Characteristics, and Stock Return Predictability," IRTG 1792 Discussion Papers 2019-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

    Cited by:

    1. Fengler, Matthias & Koeniger, Winfried & Minger, Stephan, 2025. "The Transmission of Monetary Policy to the Cost of Hedging," Economics Working Paper Series 2501, University of St. Gallen, School of Economics and Political Science.
    2. Tri Minh Phan, 2024. "Sentiment-semantic word vectors: A new method to estimate management sentiment," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-22, December.
    3. Fengler, Matthias & Phan, Minh Tri, 2023. "A Topic Model for 10-K Management Disclosures," Economics Working Paper Series 2307, University of St. Gallen, School of Economics and Political Science.
    4. Chen, Chung-Chi & Huang, Yu-Lieh & Yang, Fang, 2024. "Semantics matter: An empirical study on economic policy uncertainty index," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1286-1302.

  6. Yi-Hsuan Chen, Cathy & Fengler, Matthias & Härdle, Wolfgang Karl & Liu, Yanchu, 2018. "Textual Sentiment, Option Characteristics, and Stock Return Predictability," Economics Working Paper Series 1808, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Packham, Natalie, 2018. "Optimal contracts under competition when uncertainty from adverse selection and moral hazard are present," IRTG 1792 Discussion Papers 2018-033, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Fan, Qingliang & Zhong, Wei, 2018. "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective," IRTG 1792 Discussion Papers 2018-052, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Cai, Zongwu & Fang, Ying & Lin, Ming & Su, Jia, 2018. "Inferences for a Partially Varying Coefficient Model With Endogenous Regressors," IRTG 1792 Discussion Papers 2018-047, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Joshua Zoen Git Hiew & Xin Huang & Hao Mou & Duan Li & Qi Wu & Yabo Xu, 2019. "BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability," Papers 1906.09024, arXiv.org, revised Jul 2022.
    5. Wang, Honglin & Yu, Fan & Zhou, Yinggang, 2018. "Property Investment and Rental Rate under Housing Price Uncertainty: A Real Options Approach," IRTG 1792 Discussion Papers 2018-051, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. Yan, Ji Gao, 2018. "Complete Convergence and Complete Moment Convergence for Maximal Weighted Sums of Extended Negatively Dependent Random Variables," IRTG 1792 Discussion Papers 2018-040, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    7. Zhong, Wei & Liu, Xi & Ma, Shuangge, 2018. "Variable selection and direction estimation for single-index models via DC-TGDR method," IRTG 1792 Discussion Papers 2018-050, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Guo, Li & Tao, Yubo & Härdle, Wolfgang Karl, 2018. "Understanding Latent Group Structure of Cryptocurrencies Market: A Dynamic Network Perspective," IRTG 1792 Discussion Papers 2018-032, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    9. Kalkbrener, Michael & Packham, Natalie, 2018. "Correlation Under Stress In Normal Variance Mixture Models," IRTG 1792 Discussion Papers 2018-035, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Packham, Natalie & Woebbeking, Fabian, 2018. "A factor-model approach for correlation scenarios and correlation stress-testing," IRTG 1792 Discussion Papers 2018-034, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Chiu, Hsin-Yu & Chiang, Mi-Hsiu & Kuo, Wei-Yu, 2018. "Predicative Ability of Similarity-based Futures Trading Strategies," IRTG 1792 Discussion Papers 2018-045, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    12. Guo, Shaojun & Li, Dong & Li, Muyi, 2018. "Strict Stationarity Testing and GLAD Estimation of Double Autoregressive Models," IRTG 1792 Discussion Papers 2018-049, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    13. Xiaojia Bao & Qingliang Fan, 2020. "The impact of temperature on gaming productivity: evidence from online games," Empirical Economics, Springer, vol. 58(2), pages 835-867, February.
    14. Packham, Natalie & Kalkbrener, Michael & Overbeck, Ludger, 2018. "Default probabilities and default correlations under stress," IRTG 1792 Discussion Papers 2018-037, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    15. Kuczmaszewska, Anna & Yan, Ji Gao, 2018. "On complete convergence in Marcinkiewicz-Zygmund type SLLN for random variables," IRTG 1792 Discussion Papers 2018-041, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    16. Koziuk, Andzhey & Spokoiny, Vladimir, 2018. "Toolbox: Gaussian comparison on Eucledian balls," IRTG 1792 Discussion Papers 2018-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. Chen, Haiqiang & Li, Yingxing & Lin, Ming & Zhu, Yanli, 2018. "A Regime Shift Model with Nonparametric Switching Mechanism," IRTG 1792 Discussion Papers 2018-048, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    18. Yatracos, Yannis G., 2018. "Residual'S Influence Index (Rinfin), Bad Leverage And Unmasking In High Dimensional L2-Regression," IRTG 1792 Discussion Papers 2018-060, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    19. Nasekin, Sergey & Chen, Cathy Yi-Hsuan, 2018. "Deep learning-based cryptocurrency sentiment construction," IRTG 1792 Discussion Papers 2018-066, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    20. Zbonakova, Lenka & Li, Xinjue & Härdle, Wolfgang Karl, 2018. "Penalized Adaptive Forecasting with Large Information Sets and Structural Changes," IRTG 1792 Discussion Papers 2018-039, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  7. Fengler, Matthias R. & Hin, Lin-Yee, 2014. "A simple and general approach to fitting the discount curve under no-arbitrage constraints," Economics Working Paper Series 1423, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Areski Cousin & Hassan Maatouk & Didier Rulli`ere, 2016. "Kriging of financial term-structures," Papers 1604.02237, arXiv.org.
    2. Hao Qin & Charlie Che & Ruozhong Yang & Liming Feng, 2024. "Robust and Fast Bass local volatility," Papers 2411.04321, arXiv.org, revised May 2025.
    3. Damir Filipović & Sander Willems, 2016. "Exact Smooth Term Structure Estimation," Swiss Finance Institute Research Paper Series 16-38, Swiss Finance Institute.

  8. Fengler, Matthias R. & Gisler, Katja I. M., 2014. "A variance spillover analysis without covariances: what do we miss?," Economics Working Paper Series 1409, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Lastrapes, William D. & Wiesen, Thomas F.P., 2021. "The joint spillover index," Economic Modelling, Elsevier, vol. 94(C), pages 681-691.
    2. Mensi, Walid & Shafiullah, Muhammad & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Volatility spillovers between strategic commodity futures and stock markets and portfolio implications: Evidence from developed and emerging economies," Resources Policy, Elsevier, vol. 71(C).
    3. Yin, Kedong & Liu, Zhe & Jin, Xue, 2020. "Interindustry volatility spillover effects in China’s stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    4. Bonato, Matteo, 2019. "Realized correlations, betas and volatility spillover in the agricultural commodity market: What has changed?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 184-202.
    5. Guan, Bo & Mazouz, Khelifa & Xu, Yongdeng, 2024. "Asymmetric volatility spillover between crude oil and other asset markets," Energy Economics, Elsevier, vol. 130(C).
    6. Chenyan Lyu & Hung Xuan Do & Rabindra Nepal & Tooraj Jamasb, 2023. "Volatility Spillovers and Carbon Price in the Nordic Wholesale Electricity Markets," CAMA Working Papers 2023-36, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Cubadda, Gianluca & Guardabascio, Barbara & Hecq, Alain, 2017. "A vector heterogeneous autoregressive index model for realized volatility measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 337-344.
    8. Helmut Herwartz & Alberto Saucedo, 2020. "Food–oil volatility spillovers and the impact of distinct biofuel policies on price uncertainties on feedstock markets," Agricultural Economics, International Association of Agricultural Economists, vol. 51(3), pages 387-402, May.
    9. Thomas F. P. Wiesen & Lakshya Bharadwaj, 2023. "Cryptocurrency Connectedness: Does Controlling for the Cross-Correlations Matter?," Applied Economics Letters, Taylor & Francis Journals, vol. 30(20), pages 2873-2880, November.
    10. Liow, Kim Hiang & Huang, Yuting, 2018. "The dynamics of volatility connectedness in international real estate investment trusts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 195-210.
    11. Jozef Baruník & Evžen Kocenda & Lukáš Vácha, 2015. "Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillover," CESifo Working Paper Series 5305, CESifo.
    12. Nyberg, Henri & Pönkä, Harri, 2016. "International sign predictability of stock returns: The role of the United States," Economic Modelling, Elsevier, vol. 58(C), pages 323-338.
    13. Licheng Sun & Liang Meng & Mohammad Najand, 2017. "The Role of U.S. Market on International Risk-Return Tradeoff Relations," The Financial Review, Eastern Finance Association, vol. 52(3), pages 499-526, August.
    14. Stefan Lyocsa & Peter Molnar & Igor Fedorko, 2016. "Forecasting Exchange Rate Volatility: The Case of the Czech Republic, Hungary and Poland," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(5), pages 453-475, October.
    15. Evžen Kočenda, 2017. "Survey of volatility and spillovers on financial markets," Working Papers 363, Leibniz Institut für Ost- und Südosteuropaforschung (Institute for East and Southeast European Studies).
    16. Amal Abricha & Amine Ben Amar & Makram Bellalah, 2024. "Commodity futures markets under stress and stress-free periods: Further insights from a quantile connectedness approach," Post-Print hal-04515196, HAL.
    17. Mohammad Isleimeyyeh & Amine Ben Amar & Stéphane Goutte & Ramzi Benkraiem, 2022. "Commodity markets dynamics: What do cross-commodities over different nearest-to-maturities tell us?," Post-Print hal-03674806, HAL.
    18. Dimitrios Vortelinos & Konstantinos Gkillas (Gillas) & Costas Syriopoulos & Argyro Svingou, 2017. "Asymmetric and nonlinear inter-relations of US stock indices," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 14(1), pages 78-129, December.
    19. Chanatásig-Niza, Evelyn & Ciarreta, Aitor & Zarraga, Ainhoa, 2022. "A volatility spillover analysis with realized semi(co)variances in Australian electricity markets," Energy Economics, Elsevier, vol. 111(C).
    20. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    21. Kang, Yong Joo & Park, Dojoon & Eom, Young Ho, 2024. "Global contagion of US COVID-19 panic news," Emerging Markets Review, Elsevier, vol. 59(C).
    22. Baruník, Jozef & Kočenda, Evžen & Vácha, Lukáš, 2017. "Asymmetric volatility connectedness on the forex market," Journal of International Money and Finance, Elsevier, vol. 77(C), pages 39-56.
    23. Buncic, Daniel & Gisler, Katja I. M., 2015. "Global Equity Market Volatility Spillovers: A Broader Role for the United States," Economics Working Paper Series 1508, University of St. Gallen, School of Economics and Political Science.
    24. Riza Demirer & Konstantinos Gkillas & Christos Kountzakis & Amaryllis Mavragani, 2020. "Risk Appetite and Jumps in Realized Correlation," Mathematics, MDPI, vol. 8(12), pages 1-11, December.
    25. Evzen Kocenda & Michala Moravcova, 2017. "Exchange Rate Co-movements, Hedging and Volatility Spillovers in New EU Forex Markets," Working Papers IES 2017/27, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Nov 2017.
    26. Cipollini, Andrea & Lo Cascio, Iolanda & Muzzioli, Silvia, 2018. "Risk aversion connectedness in five European countries," Economic Modelling, Elsevier, vol. 71(C), pages 68-79.
    27. Iwanicz-Drozdowska, Małgorzata & Rogowicz, Karol & Kurowski, Łukasz & Smaga, Paweł, 2021. "Two decades of contagion effect on stock markets: Which events are more contagious?," Journal of Financial Stability, Elsevier, vol. 55(C).
    28. Ben Amar, Amine & Goutte, Stéphane & Isleimeyyeh, Mohammad, 2022. "Asymmetric cyclical connectedness on the commodity markets: Further insights from bull and bear markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 386-400.
    29. Kim Hiang LIOW & Jeongseop SONG, 2019. "Market Integration Among the US and Asian Real Estate Investment Trusts in Crisis Times," International Real Estate Review, Global Social Science Institute, vol. 22(4), pages 463-512.
    30. Pedro Pires Ribeiro & José Dias Curto, 2017. "Volatility spillover effects in interbank money markets," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 153(1), pages 105-136, February.
    31. Lukas Boeckelmann & Arthur Stalla-Bourdillon, 2021. "Structural Estimation of Time-Varying Spillovers: An Application to International Credit Risk Transmission," Working papers 798, Banque de France.
    32. Maki, Daiki, 2024. "Evaluation of volatility spillovers for asymmetric realized covariance," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    33. Apergis, Nicholas & Baruník, Jozef & Lau, Marco Chi Keung, 2017. "Good volatility, bad volatility: What drives the asymmetric connectedness of Australian electricity markets?," Energy Economics, Elsevier, vol. 66(C), pages 108-115.
    34. Chen, Yufeng & Li, Wenqi & Qu, Fang, 2019. "Dynamic asymmetric spillovers and volatility interdependence on China’s stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 825-838.
    35. Lin, Mucai & Hong, Zhiwu & Su, Ge, 2024. "Transmission of liquidity and credit risks in the Chinese bond market: Analysis based on joint modeling of multiple yield curves," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 597-615.
    36. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.
    37. Fengler, Matthias R. & Herwartz, Helmut, 2015. "Measuring spot variance spillovers when (co)variances are time-varying - the case of multivariate GARCH models," MPRA Paper 72197, University Library of Munich, Germany, revised 10 Jun 2016.
    38. Mensi, Walid & Shafiullah, Muhammad & Vo, Xuan Vinh & Kang, Sang Hoon, 2022. "Asymmetric spillovers and connectedness between crude oil and currency markets using high-frequency data," Resources Policy, Elsevier, vol. 77(C).
    39. Sachapon Tungsong & Fabio Caccioli & Tomaso Aste, 2017. "Relation between regional uncertainty spillovers in the global banking system," Papers 1702.05944, arXiv.org.
    40. Huthaifa Alqaralleh & Awon Almajali & Alessandra Canepa, 2024. "Navigating Energy Market Cycles: Insights from a Comprehensive Analysis," International Journal of Energy Economics and Policy, Econjournals, vol. 14(5), pages 35-48, September.
    41. Nishimura, Yusaku & Sun, Bianxia, 2018. "The intraday volatility spillover index approach and an application in the Brexit vote," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 241-253.
    42. Caloia, Francesco Giuseppe & Cipollini, Andrea & Muzzioli, Silvia, 2018. "Asymmetric semi-volatility spillover effects in EMU stock markets," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 221-230.
    43. Wiesen, Thomas F.P. & Adekoya, Oluwasegun Babatunde & Oliyide, Johnson & Afatsao, Richard, 2024. "Does high volatility increase connectedness? A study of Asian equity markets," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    44. Mensi, Walid & Vo, Xuan Vinh & Kang, Sang Hoon, 2023. "Quantile spillovers and connectedness analysis between oil and African stock markets," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 60-83.
    45. Andrea Cipollini & Iolanda Lo Cascio & Silvia Muzzioli, 2015. "Financial connectedness among European volatility risk premia," Centro Studi di Banca e Finanza (CEFIN) (Center for Studies in Banking and Finance) 0058, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    46. Shahzad, Syed Jawad Hussain & Ferrer, Román & Ballester, Laura & Umar, Zaghum, 2017. "Risk transmission between Islamic and conventional stock markets: A return and volatility spillover analysis," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 9-26.
    47. Chung, Chien-Ping & Liao, Tzu-Hsiang & Lee, Hsiu-Chuan, 2021. "Volatility spillovers of A- and B-shares for the Chinese stock market and its impact on the Chinese index returns," Pacific-Basin Finance Journal, Elsevier, vol. 65(C).
    48. Syed Jawad Hussain Shahzad & Román Ferrer, 2020. "Dynamic spillover effects among tourism, economic growth and macro-finance risk factors," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 19(3), pages 173-194, September.
    49. Abosedra, Salah & Arayssi, Mahmoud & Ben Sita, Bernard & Mutshinda, Crispin, 2020. "Exploring GDP growth volatility spillovers across countries," Economic Modelling, Elsevier, vol. 89(C), pages 577-589.
    50. Bouri, Elie, 2023. "Spillovers in the joint system of conditional higher-order moments: US evidence from green energy, brown energy, and technology stocks," Renewable Energy, Elsevier, vol. 210(C), pages 507-523.
    51. Ni, Jianhui & Ruan, Jia, 2024. "Contagion effects of external monetary shocks on systemic financial risk in China: Evidence from the Euro area and Japan," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    52. Xiaoye Jin, 2024. "Salience theory value spillovers between China’s systemically important banks: evidence from quantile connectedness," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-39, December.
    53. Baklaci, Hasan Fehmi & Aydoğan, Berna & Yelkenci, Tezer, 2020. "Impact of stock market trading on currency market volatility spillovers," Research in International Business and Finance, Elsevier, vol. 52(C).
    54. Nekhili, Ramzi & Bouri, Elie, 2023. "Higher-order moments and co-moments' contribution to spillover analysis and portfolio risk management," Energy Economics, Elsevier, vol. 119(C).

  9. Audrino, Francesco & Fengler, Matthias, 2013. "Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data," Economics Working Paper Series 1311, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Fengler, Matthias & Koeniger, Winfried & Minger, Stephan, 2025. "The Transmission of Monetary Policy to the Cost of Hedging," Economics Working Paper Series 2501, University of St. Gallen, School of Economics and Political Science.
    2. Diego Amaya & Jean-François Bégin & Geneviève Gauthier, 2022. "The Informational Content of High-Frequency Option Prices," Management Science, INFORMS, vol. 68(3), pages 2166-2201, March.
    3. Stephen J. Taylor & Chi‐Feng Tzeng & Martin Widdicks, 2018. "Information about price and volatility jumps inferred from options prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(10), pages 1206-1226, October.
    4. Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.

  10. Fengler, Matthias R. & Mammen, Enno & Vogt, Michael, 2013. "Additive modeling of realized variance: tests for parametric specifications and structural breaks," Economics Working Paper Series 1332, University of St. Gallen, School of Economics and Political Science.

    Cited by:

    1. Buncic, Daniel & Gisler, Katja I. M., 2015. "Global Equity Market Volatility Spillovers: A Broader Role for the United States," Economics Working Paper Series 1508, University of St. Gallen, School of Economics and Political Science.

  11. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Irving Arturo De Lira Salvatierra & Andrew J. Patton, 2013. "Dynamic Copula Models and High Frequency Data," Working Papers 13-28, Duke University, Department of Economics.
    2. Fengler, Matthias R. & Gisler, Katja I. M., 2014. "A variance spillover analysis without covariances: what do we miss?," Economics Working Paper Series 1409, University of St. Gallen, School of Economics and Political Science.
    3. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    4. Dickhaus, Thorsten & Gierl, Jakob, 2012. "Simultaneous test procedures in terms of p-value copulae," SFB 649 Discussion Papers 2012-049, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Jean-David Fermanian, 2017. "Recent Developments in Copula Models," Econometrics, MDPI, vol. 5(3), pages 1-3, July.

  12. Fengler, Matthias R. & Okhrin, Ostap, 2012. "Realized copula," SFB 649 Discussion Papers 2012-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Irving Arturo De Lira Salvatierra & Andrew J. Patton, 2013. "Dynamic Copula Models and High Frequency Data," Working Papers 13-28, Duke University, Department of Economics.
    2. Fengler, Matthias R. & Gisler, Katja I. M., 2014. "A variance spillover analysis without covariances: what do we miss?," Economics Working Paper Series 1409, University of St. Gallen, School of Economics and Political Science.
    3. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    4. Dickhaus, Thorsten & Gierl, Jakob, 2012. "Simultaneous test procedures in terms of p-value copulae," SFB 649 Discussion Papers 2012-049, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Jean-David Fermanian, 2017. "Recent Developments in Copula Models," Econometrics, MDPI, vol. 5(3), pages 1-3, July.

  13. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.

    Cited by:

    1. Fengler, Matthias R. & Hin, Lin-Yee, 2014. "A simple and general approach to fitting the discount curve under no-arbitrage constraints," Economics Working Paper Series 1423, University of St. Gallen, School of Economics and Political Science.

  14. Matthias Fengler, 2010. "Option data and modeling BSM implied volatility," University of St. Gallen Department of Economics working paper series 2010 2010-32, Department of Economics, University of St. Gallen.

    Cited by:

    1. Cristian Homescu, 2011. "Implied Volatility Surface: Construction Methodologies and Characteristics," Papers 1107.1834, arXiv.org.
    2. Noshaba Zulfiqar & Saqib Gulzar, 2021. "Implied volatility estimation of bitcoin options and the stylized facts of option pricing," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    3. Gentle, James E. & Härdle, Wolfgang Karl, 2010. "Modeling asset prices," SFB 649 Discussion Papers 2010-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Härdle, Wolfgang Karl & Okhrin, Yarema & Wang, Weining, 2010. "Uniform confidence bands for pricing kernels," SFB 649 Discussion Papers 2010-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Bo Zhao & Stewart Hodges, 2013. "Parametric modeling of implied smile functions: a generalized SVI model," Review of Derivatives Research, Springer, vol. 16(1), pages 53-77, April.

  15. Matthias Fengler & Helmut Herwartz & Christian Werner, 2010. "A dynamic copula approach to recovering the index implied volatility skew," University of St. Gallen Department of Economics working paper series 2010 1132, Department of Economics, University of St. Gallen, revised Nov 2011.

    Cited by:

    1. ROMBOUTS, Jeroen V. K. & STENTOFT, Lars & VIOLANTE, Francesco, 2012. "The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options," LIDAM Discussion Papers CORE 2012003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Herwartz, Helmut & Raters, Fabian H.C., 2015. "Copula-MGARCH with continuous covariance decomposition," Economics Letters, Elsevier, vol. 133(C), pages 73-76.
    3. Saldías, Martín, 2013. "Systemic risk analysis using forward-looking Distance-to-Default series," Journal of Financial Stability, Elsevier, vol. 9(4), pages 498-517.
    4. Dahiru A. Balaa & Taro Takimotob, 2017. "Stock markets volatility spillovers during financial crises: A DCC-MGARCH with skewed-t density approach," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 17(1), pages 25-48, March.
    5. Fengler, Matthias & Melnikov, Alexander, 2017. "GARCH option pricing models with Meixner innovations," Economics Working Paper Series 1702, University of St. Gallen, School of Economics and Political Science.
    6. Félix, Luiz & Kräussl, Roman & Stork, Philip, 2013. "The 2011 European short sale ban on financial stocks: A cure or a curse?," CFS Working Paper Series 2013/17, Center for Financial Studies (CFS).

  16. Fengler, Matthias R. & Winter, Joachim, 2007. "Price variability and price dispersion in a stable monetary environment: Evidence from German retail markets," Munich Reprints in Economics 20338, University of Munich, Department of Economics.

    Cited by:

    1. Levy, Daniel, 2007. "Price Rigidity and Flexibility: New Empirical Evidence - Introduction to the Special Issue," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 28(7 (Specia), pages 639-647.
    2. Levy, Daniel, 2007. "Price Rigidity and Flexibility: New Empirical Evidence," MPRA Paper 2762, University Library of Munich, Germany.
    3. Ater, Itai & Gerlitz, Omri, 2017. "Round prices and price rigidity: Evidence from outlawing odd prices," Journal of Economic Behavior & Organization, Elsevier, vol. 144(C), pages 188-203.

  17. Borak, Szymon & Fengler, Matthias R. & Härdle, Wolfgang Karl, 2005. "DSFM fitting of implied volatility surfaces," SFB 649 Discussion Papers 2005-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Härdle, Wolfgang Karl & Mungo, Julius, 2007. "Long memory persistence in the factor of Implied volatility dynamics," SFB 649 Discussion Papers 2007-027, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Brüggemann, Ralf & Härdle, Wolfgang Karl & Mungo, Julius & Trenkler, Carsten, 2006. "VAR modeling for dynamic semiparametric factors of volatility strings," SFB 649 Discussion Papers 2006-011, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  18. Borak, Szymon & Fengler, Matthias R. & Härdle, Wolfgang Karl, 2005. "DSFM fitting of implied volatility surfaces," SFB 649 Discussion Papers 2005-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Härdle, Wolfgang Karl & Mungo, Julius, 2007. "Long memory persistence in the factor of Implied volatility dynamics," SFB 649 Discussion Papers 2007-027, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Brüggemann, Ralf & Härdle, Wolfgang Karl & Mungo, Julius & Trenkler, Carsten, 2006. "VAR modeling for dynamic semiparametric factors of volatility strings," SFB 649 Discussion Papers 2006-011, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  19. Fengler, Matthias R., 2005. "Arbitrage-free smoothing of the implied volatility surface," SFB 649 Discussion Papers 2005-019, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Olena Burkovska & Maximilian Ga{ss} & Kathrin Glau & Mirco Mahlstedt & Wim Schoutens & Barbara Wohlmuth, 2016. "Calibration to American Options: Numerical Investigation of the de-Americanization," Papers 1611.06181, arXiv.org.
    2. Orcan Ogetbil & Bernhard Hientzsch, 2020. "Extensions of Dupire Formula: Stochastic Interest Rates and Stochastic Local Volatility," Papers 2005.05530, arXiv.org, revised Feb 2023.
    3. Judith Glaser & Pascal Heider, 2012. "Arbitrage-free approximation of call price surfaces and input data risk," Quantitative Finance, Taylor & Francis Journals, vol. 12(1), pages 61-73, August.
    4. Boswijk, H. Peter & Laeven, Roger J.A. & Vladimirov, Evgenii, 2024. "Estimating option pricing models using a characteristic function-based linear state space representation," Journal of Econometrics, Elsevier, vol. 244(1).
    5. Martin Tegn'er & Stephen Roberts, 2019. "A Probabilistic Approach to Nonparametric Local Volatility," Papers 1901.06021, arXiv.org, revised Jan 2019.
    6. Kai Yin & Anirban Mondal, 2023. "Bayesian uncertainty quantification of local volatility model," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 290-324, May.
    7. Gianluca Cassese, 2019. "Nonparametric Estimates Of Option Prices And Related Quantities," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(07), pages 1-29, November.
    8. Zhonghao Xian & Xing Yan & Cheuk Hang Leung & Qi Wu, 2024. "Risk-Neutral Generative Networks," Papers 2405.17770, arXiv.org.
    9. Jim Gatheral & Antoine Jacquier, 2012. "Arbitrage-free SVI volatility surfaces," Papers 1204.0646, arXiv.org, revised Mar 2013.
    10. Mnacho Echenim & Emmanuel Gobet & Anne-Claire Maurice, 2022. "Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes," Papers 2207.02989, arXiv.org.
    11. Stefano Galluccio & Yann Le Cam, 2005. "Implied Calibration of Stochastic Volatility Jump Diffusion Models," Finance 0510028, University Library of Munich, Germany.
    12. Vedant Choudhary & Sebastian Jaimungal & Maxime Bergeron, 2023. "FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs," Papers 2303.00859, arXiv.org, revised Dec 2023.
    13. Yanlin Qu & Randall R. Rojas, 2017. "Closed-form Solutions of Relativistic Black-Scholes Equations," Papers 1711.04219, arXiv.org.
    14. Jan Maruhn & Morten Nalholm & Matthias Fengler, 2011. "Static hedges for reverse barrier options with robustness against skew risk: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(5), pages 711-727.
    15. Carol Alexander & Johannes Rauch, 2014. "Model-Free Discretisation-Invariant Swaps and S&P 500 Higher-Moment Risk Premia," Papers 1404.1351, arXiv.org, revised Feb 2016.
    16. Anindya Goswami & Nimit Rana, 2024. "A market resilient data-driven approach to option pricing," Papers 2409.08205, arXiv.org.
    17. Laurini, Márcio P., 2007. "Imposing No-Arbitrage Conditions In Implied Volatility Surfaces Using Constrained Smoothing Splines," Insper Working Papers wpe_89, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    18. Salazar Celis, Oliver & Liang, Lingzhi & Lemmens, Damiaan & Tempère, Jacques & Cuyt, Annie, 2015. "Determining and benchmarking risk neutral distributions implied from option prices," Applied Mathematics and Computation, Elsevier, vol. 258(C), pages 372-387.
    19. Li, Chen Xu & Li, Chenxu & Li, Chun, 2025. "Implied local volatility models," Journal of Empirical Finance, Elsevier, vol. 80(C).
    20. Frédéric Vrins & Linqi Wang, 2023. "Asymmetric short-rate model without lower bound," Quantitative Finance, Taylor & Francis Journals, vol. 23(2), pages 279-295, February.
    21. Pascal Albert & Michael Herold & Matthias Muck, 2023. "Estimation of rare disaster concerns from option prices—An arbitrage‐free RND‐based smile construction approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1807-1835, December.
    22. Gabriel Drimus & Walter Farkas, 2013. "Local volatility of volatility for the VIX market," Review of Derivatives Research, Springer, vol. 16(3), pages 267-293, October.
    23. Beer, Simone & Braun, Alexander, 2022. "Market-consistent valuation of natural catastrophe risk," Journal of Banking & Finance, Elsevier, vol. 134(C).
    24. Maxim Ulrich & Simon Walther, 2020. "Option-implied information: What’s the vol surface got to do with it?," Review of Derivatives Research, Springer, vol. 23(3), pages 323-355, October.
    25. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2019. "Option Implied Risk-Neutral Density Estimation: A Robust and Flexible Method," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 705-728, August.
    26. Pierre M. Blacque-Florentin & Badr Missaoui, 2015. "Nonparametric and arbitrage-free construction of call surfaces using l1-recovery," Papers 1506.06997, arXiv.org, revised Aug 2016.
    27. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2024. "A Semi-Closed Form Approximation of Arbitrage-Free Call Option Price Surface," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1431-1457, April.
    28. Курочкин С.В., 2016. "Выпуклость Множества Цен Опционов Как Необходимое И Достаточное Условие Отсутствия Арбитража," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 52(2), pages 103-111, апрель.
    29. Itkin, Andrey, 2015. "To sigmoid-based functional description of the volatility smile," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 264-291.
    30. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    31. Bo Zhao & Stewart Hodges, 2013. "Parametric modeling of implied smile functions: a generalized SVI model," Review of Derivatives Research, Springer, vol. 16(1), pages 53-77, April.
    32. Johannes Rauch & Carol Alexander, 2016. "Tail Risk Premia for Long-Term Equity Investors," Papers 1602.00865, arXiv.org.
    33. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.
    34. Dilip B. Madan & Wim Schoutens, 2019. "Arbitrage Free Approximations to Candidate Volatility Surface Quotations," JRFM, MDPI, vol. 12(2), pages 1-21, April.
    35. Alan L. Lewis, 2019. "Option-based Equity Risk Premiums," Papers 1910.14522, arXiv.org, revised Apr 2020.
    36. Bernales, Alejandro & Guidolin, Massimo, 2015. "Learning to smile: Can rational learning explain predictable dynamics in the implied volatility surface?," Journal of Financial Markets, Elsevier, vol. 26(C), pages 1-37.
    37. Seung Hwan Lee, 2014. "Estimation of risk-neutral measures using quartic B-spline cumulative distribution functions with power tails," Quantitative Finance, Taylor & Francis Journals, vol. 14(10), pages 1857-1879, October.
    38. Nicola F. Zaugg & Leonardo Perotti & Lech A. Grzelak, 2024. "Volatility Parametrizations with Random Coefficients: Analytic Flexibility for Implied Volatility Surfaces," Papers 2411.04041, arXiv.org, revised Nov 2024.
    39. Carol Alexander & Alexander Rubinov & Markus Kalepky & Stamatis Leontsinis, 2010. "Regime-Dependent Smile-Adjusted Delta Hedging," ICMA Centre Discussion Papers in Finance icma-dp2010-10, Henley Business School, University of Reading.
    40. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2020. "Detecting and repairing arbitrage in traded option prices," Papers 2008.09454, arXiv.org.
    41. Bender Christian & Thiel Matthias, 2020. "Arbitrage-free interpolation of call option prices," Statistics & Risk Modeling, De Gruyter, vol. 37(1-2), pages 55-78, January.
    42. Thomas Mazzoni, 2018. "Asymptotic Expansion of Risk-Neutral Pricing Density," IJFS, MDPI, vol. 6(1), pages 1-26, March.
    43. F. Leung & M. Law & S. K. Djeng, 2024. "Deterministic modelling of implied volatility in cryptocurrency options with underlying multiple resolution momentum indicator and non-linear machine learning regression algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-25, December.
    44. Abdulwahab Animoku & Ömür Uğur & Yeliz Yolcu-Okur, 2018. "Modeling and implementation of local volatility surfaces in Bayesian framework," Computational Management Science, Springer, vol. 15(2), pages 239-258, June.
    45. Miloš Kopa & Sebastiano Vitali & Tomáš Tichý & Radek Hendrych, 2017. "Implied volatility and state price density estimation: arbitrage analysis," Computational Management Science, Springer, vol. 14(4), pages 559-583, October.
    46. Sigurd Emil Rømer & Rolf Poulsen, 2020. "How Does the Volatility of Volatility Depend on Volatility?," Risks, MDPI, vol. 8(2), pages 1-18, June.
    47. Tahar Ferhati, 2020. "Robust Calibration For SVI Model Arbitrage Free," Working Papers hal-02490029, HAL.
    48. Tahar Ferhati, 2020. "SVI Model Free Wings," Working Papers hal-02517572, HAL.
    49. Sylvain Corlay, 2013. "B-spline techniques for volatility modeling," Papers 1306.0995, arXiv.org, revised Jun 2015.
    50. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    51. Gaoyue Guo & Antoine Jacquier & Claude Martini & Leo Neufcourt, 2012. "Generalised arbitrage-free SVI volatility surfaces," Papers 1210.7111, arXiv.org, revised May 2016.
    52. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "Semi-nonparametric estimation of the call-option price surface under strike and time-to-expiry no-arbitrage constraints," Journal of Econometrics, Elsevier, vol. 184(2), pages 242-261.
    53. Kim, Namhyoung & Lee, Jaewook, 2013. "No-arbitrage implied volatility functions: Empirical evidence from KOSPI 200 index options," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 36-53.
    54. Härdle, Wolfgang Karl & Hlávka, Zdeněk, 2005. "Dynamics of state price densities," SFB 649 Discussion Papers 2005-021, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    55. Pascal François & Rémi Galarneau‐Vincent & Geneviève Gauthier & Frédéric Godin, 2022. "Venturing into uncharted territory: An extensible implied volatility surface model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1912-1940, October.
    56. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2021. "Arbitrage-free neural-SDE market models," Papers 2105.11053, arXiv.org, revised Aug 2021.
    57. Benko, Michal & Härdle, Wolfgang Karl & Kneip, Alois, 2006. "Common functional principal components," SFB 649 Discussion Papers 2006-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    58. Hirbod Assa & Mostafa Pouralizadeh & Abdolrahim Badamchizadeh, 2019. "Sound Deposit Insurance Pricing Using a Machine Learning Approach," Risks, MDPI, vol. 7(2), pages 1-18, April.
    59. Mnacho Echenim & Emmanuel Gobet & Anne-Claire Maurice, 2023. "Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes," Post-Print hal-03715921, HAL.
    60. Areski Cousin & Djibril Gueye, 2021. "Kriging For Implied Volatility Surface," Working Papers hal-03274026, HAL.
    61. Shengli Chen & Zili Zhang, 2019. "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers 1912.11059, arXiv.org.

  20. Fengler, Matthias R., 2005. "Arbitrage-free smoothing of the implied volatility surface," SFB 649 Discussion Papers 2005-019, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Olena Burkovska & Maximilian Ga{ss} & Kathrin Glau & Mirco Mahlstedt & Wim Schoutens & Barbara Wohlmuth, 2016. "Calibration to American Options: Numerical Investigation of the de-Americanization," Papers 1611.06181, arXiv.org.
    2. Orcan Ogetbil & Bernhard Hientzsch, 2020. "Extensions of Dupire Formula: Stochastic Interest Rates and Stochastic Local Volatility," Papers 2005.05530, arXiv.org, revised Feb 2023.
    3. Judith Glaser & Pascal Heider, 2012. "Arbitrage-free approximation of call price surfaces and input data risk," Quantitative Finance, Taylor & Francis Journals, vol. 12(1), pages 61-73, August.
    4. Boswijk, H. Peter & Laeven, Roger J.A. & Vladimirov, Evgenii, 2024. "Estimating option pricing models using a characteristic function-based linear state space representation," Journal of Econometrics, Elsevier, vol. 244(1).
    5. Martin Tegn'er & Stephen Roberts, 2019. "A Probabilistic Approach to Nonparametric Local Volatility," Papers 1901.06021, arXiv.org, revised Jan 2019.
    6. Kai Yin & Anirban Mondal, 2023. "Bayesian uncertainty quantification of local volatility model," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 290-324, May.
    7. Gianluca Cassese, 2019. "Nonparametric Estimates Of Option Prices And Related Quantities," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(07), pages 1-29, November.
    8. Zhonghao Xian & Xing Yan & Cheuk Hang Leung & Qi Wu, 2024. "Risk-Neutral Generative Networks," Papers 2405.17770, arXiv.org.
    9. Jim Gatheral & Antoine Jacquier, 2012. "Arbitrage-free SVI volatility surfaces," Papers 1204.0646, arXiv.org, revised Mar 2013.
    10. Mnacho Echenim & Emmanuel Gobet & Anne-Claire Maurice, 2022. "Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes," Papers 2207.02989, arXiv.org.
    11. Stefano Galluccio & Yann Le Cam, 2005. "Implied Calibration of Stochastic Volatility Jump Diffusion Models," Finance 0510028, University Library of Munich, Germany.
    12. Vedant Choudhary & Sebastian Jaimungal & Maxime Bergeron, 2023. "FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs," Papers 2303.00859, arXiv.org, revised Dec 2023.
    13. Yanlin Qu & Randall R. Rojas, 2017. "Closed-form Solutions of Relativistic Black-Scholes Equations," Papers 1711.04219, arXiv.org.
    14. Jan Maruhn & Morten Nalholm & Matthias Fengler, 2011. "Static hedges for reverse barrier options with robustness against skew risk: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(5), pages 711-727.
    15. Carol Alexander & Johannes Rauch, 2014. "Model-Free Discretisation-Invariant Swaps and S&P 500 Higher-Moment Risk Premia," Papers 1404.1351, arXiv.org, revised Feb 2016.
    16. Anindya Goswami & Nimit Rana, 2024. "A market resilient data-driven approach to option pricing," Papers 2409.08205, arXiv.org.
    17. Laurini, Márcio P., 2007. "Imposing No-Arbitrage Conditions In Implied Volatility Surfaces Using Constrained Smoothing Splines," Insper Working Papers wpe_89, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    18. Salazar Celis, Oliver & Liang, Lingzhi & Lemmens, Damiaan & Tempère, Jacques & Cuyt, Annie, 2015. "Determining and benchmarking risk neutral distributions implied from option prices," Applied Mathematics and Computation, Elsevier, vol. 258(C), pages 372-387.
    19. Li, Chen Xu & Li, Chenxu & Li, Chun, 2025. "Implied local volatility models," Journal of Empirical Finance, Elsevier, vol. 80(C).
    20. Frédéric Vrins & Linqi Wang, 2023. "Asymmetric short-rate model without lower bound," Quantitative Finance, Taylor & Francis Journals, vol. 23(2), pages 279-295, February.
    21. Pascal Albert & Michael Herold & Matthias Muck, 2023. "Estimation of rare disaster concerns from option prices—An arbitrage‐free RND‐based smile construction approach," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1807-1835, December.
    22. Gabriel Drimus & Walter Farkas, 2013. "Local volatility of volatility for the VIX market," Review of Derivatives Research, Springer, vol. 16(3), pages 267-293, October.
    23. Beer, Simone & Braun, Alexander, 2022. "Market-consistent valuation of natural catastrophe risk," Journal of Banking & Finance, Elsevier, vol. 134(C).
    24. Maxim Ulrich & Simon Walther, 2020. "Option-implied information: What’s the vol surface got to do with it?," Review of Derivatives Research, Springer, vol. 23(3), pages 323-355, October.
    25. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2019. "Option Implied Risk-Neutral Density Estimation: A Robust and Flexible Method," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 705-728, August.
    26. Pierre M. Blacque-Florentin & Badr Missaoui, 2015. "Nonparametric and arbitrage-free construction of call surfaces using l1-recovery," Papers 1506.06997, arXiv.org, revised Aug 2016.
    27. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2024. "A Semi-Closed Form Approximation of Arbitrage-Free Call Option Price Surface," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1431-1457, April.
    28. Курочкин С.В., 2016. "Выпуклость Множества Цен Опционов Как Необходимое И Достаточное Условие Отсутствия Арбитража," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 52(2), pages 103-111, апрель.
    29. Itkin, Andrey, 2015. "To sigmoid-based functional description of the volatility smile," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 264-291.
    30. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    31. Bo Zhao & Stewart Hodges, 2013. "Parametric modeling of implied smile functions: a generalized SVI model," Review of Derivatives Research, Springer, vol. 16(1), pages 53-77, April.
    32. Johannes Rauch & Carol Alexander, 2016. "Tail Risk Premia for Long-Term Equity Investors," Papers 1602.00865, arXiv.org.
    33. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.
    34. Dilip B. Madan & Wim Schoutens, 2019. "Arbitrage Free Approximations to Candidate Volatility Surface Quotations," JRFM, MDPI, vol. 12(2), pages 1-21, April.
    35. Alan L. Lewis, 2019. "Option-based Equity Risk Premiums," Papers 1910.14522, arXiv.org, revised Apr 2020.
    36. Bernales, Alejandro & Guidolin, Massimo, 2015. "Learning to smile: Can rational learning explain predictable dynamics in the implied volatility surface?," Journal of Financial Markets, Elsevier, vol. 26(C), pages 1-37.
    37. Seung Hwan Lee, 2014. "Estimation of risk-neutral measures using quartic B-spline cumulative distribution functions with power tails," Quantitative Finance, Taylor & Francis Journals, vol. 14(10), pages 1857-1879, October.
    38. Nicola F. Zaugg & Leonardo Perotti & Lech A. Grzelak, 2024. "Volatility Parametrizations with Random Coefficients: Analytic Flexibility for Implied Volatility Surfaces," Papers 2411.04041, arXiv.org, revised Nov 2024.
    39. Carol Alexander & Alexander Rubinov & Markus Kalepky & Stamatis Leontsinis, 2010. "Regime-Dependent Smile-Adjusted Delta Hedging," ICMA Centre Discussion Papers in Finance icma-dp2010-10, Henley Business School, University of Reading.
    40. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2020. "Detecting and repairing arbitrage in traded option prices," Papers 2008.09454, arXiv.org.
    41. Bender Christian & Thiel Matthias, 2020. "Arbitrage-free interpolation of call option prices," Statistics & Risk Modeling, De Gruyter, vol. 37(1-2), pages 55-78, January.
    42. Thomas Mazzoni, 2018. "Asymptotic Expansion of Risk-Neutral Pricing Density," IJFS, MDPI, vol. 6(1), pages 1-26, March.
    43. F. Leung & M. Law & S. K. Djeng, 2024. "Deterministic modelling of implied volatility in cryptocurrency options with underlying multiple resolution momentum indicator and non-linear machine learning regression algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-25, December.
    44. Abdulwahab Animoku & Ömür Uğur & Yeliz Yolcu-Okur, 2018. "Modeling and implementation of local volatility surfaces in Bayesian framework," Computational Management Science, Springer, vol. 15(2), pages 239-258, June.
    45. Miloš Kopa & Sebastiano Vitali & Tomáš Tichý & Radek Hendrych, 2017. "Implied volatility and state price density estimation: arbitrage analysis," Computational Management Science, Springer, vol. 14(4), pages 559-583, October.
    46. Sigurd Emil Rømer & Rolf Poulsen, 2020. "How Does the Volatility of Volatility Depend on Volatility?," Risks, MDPI, vol. 8(2), pages 1-18, June.
    47. Tahar Ferhati, 2020. "Robust Calibration For SVI Model Arbitrage Free," Working Papers hal-02490029, HAL.
    48. Tahar Ferhati, 2020. "SVI Model Free Wings," Working Papers hal-02517572, HAL.
    49. Sylvain Corlay, 2013. "B-spline techniques for volatility modeling," Papers 1306.0995, arXiv.org, revised Jun 2015.
    50. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    51. Gaoyue Guo & Antoine Jacquier & Claude Martini & Leo Neufcourt, 2012. "Generalised arbitrage-free SVI volatility surfaces," Papers 1210.7111, arXiv.org, revised May 2016.
    52. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "Semi-nonparametric estimation of the call-option price surface under strike and time-to-expiry no-arbitrage constraints," Journal of Econometrics, Elsevier, vol. 184(2), pages 242-261.
    53. Kim, Namhyoung & Lee, Jaewook, 2013. "No-arbitrage implied volatility functions: Empirical evidence from KOSPI 200 index options," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 36-53.
    54. Härdle, Wolfgang Karl & Hlávka, Zdeněk, 2005. "Dynamics of state price densities," SFB 649 Discussion Papers 2005-021, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    55. Pascal François & Rémi Galarneau‐Vincent & Geneviève Gauthier & Frédéric Godin, 2022. "Venturing into uncharted territory: An extensible implied volatility surface model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1912-1940, October.
    56. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2021. "Arbitrage-free neural-SDE market models," Papers 2105.11053, arXiv.org, revised Aug 2021.
    57. Benko, Michal & Härdle, Wolfgang Karl & Kneip, Alois, 2006. "Common functional principal components," SFB 649 Discussion Papers 2006-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    58. Hirbod Assa & Mostafa Pouralizadeh & Abdolrahim Badamchizadeh, 2019. "Sound Deposit Insurance Pricing Using a Machine Learning Approach," Risks, MDPI, vol. 7(2), pages 1-18, April.
    59. Mnacho Echenim & Emmanuel Gobet & Anne-Claire Maurice, 2023. "Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes," Post-Print hal-03715921, HAL.
    60. Areski Cousin & Djibril Gueye, 2021. "Kriging For Implied Volatility Surface," Working Papers hal-03274026, HAL.
    61. Shengli Chen & Zili Zhang, 2019. "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers 1912.11059, arXiv.org.

  21. Fengler, Matthias R. & Härdle, Wolfgang Karl & Mammen, Enno, 2005. "A dynamic semiparametric factor model for implied volatility string dynamics," SFB 649 Discussion Papers 2005-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Borak, Szymon & Fengler, Matthias R. & Härdle, Wolfgang Karl, 2005. "DSFM fitting of implied volatility surfaces," SFB 649 Discussion Papers 2005-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Stefan Trück & Wolfgang Härdle & Rafal Weron, 2012. "The relationship between spot and futures CO2 emission allowance prices in the EU-ETS," HSC Research Reports HSC/12/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    3. Härdle, Wolfgang Karl & Majer, Piotr, 2012. "Yield curve modeling and forecasting using semiparametric factor dynamics," SFB 649 Discussion Papers 2012-048, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Härdle, Wolfgang & Hlávka, Zdenek, 2009. "Dynamics of state price densities," Journal of Econometrics, Elsevier, vol. 150(1), pages 1-15, May.
    5. Liu, Xialu & Xiao, Han & Chen, Rong, 2016. "Convolutional autoregressive models for functional time series," Journal of Econometrics, Elsevier, vol. 194(2), pages 263-282.
    6. Brüggemann, Ralf & Härdle, Wolfgang Karl & Mungo, Julius & Trenkler, Carsten, 2006. "VAR modeling for dynamic semiparametric factors of volatility strings," SFB 649 Discussion Papers 2006-011, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Lam, Clifford & Yao, Qiwei & Bathia, Neil, 2011. "Estimation of latent factors for high-dimensional time series," LSE Research Online Documents on Economics 31549, London School of Economics and Political Science, LSE Library.
    8. F. Leung & M. Law & S. K. Djeng, 2024. "Deterministic modelling of implied volatility in cryptocurrency options with underlying multiple resolution momentum indicator and non-linear machine learning regression algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-25, December.
    9. Benko, Michal & Härdle, Wolfgang Karl & Kneip, Alois, 2006. "Common functional principal components," SFB 649 Discussion Papers 2006-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  22. Fengler, Matthias R. & Härdle, Wolfgang Karl & Mammen, Enno, 2005. "A dynamic semiparametric factor model for implied volatility string dynamics," SFB 649 Discussion Papers 2005-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Borak, Szymon & Fengler, Matthias R. & Härdle, Wolfgang Karl, 2005. "DSFM fitting of implied volatility surfaces," SFB 649 Discussion Papers 2005-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Stefan Trück & Wolfgang Härdle & Rafal Weron, 2012. "The relationship between spot and futures CO2 emission allowance prices in the EU-ETS," HSC Research Reports HSC/12/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    3. Härdle, Wolfgang Karl & Majer, Piotr, 2012. "Yield curve modeling and forecasting using semiparametric factor dynamics," SFB 649 Discussion Papers 2012-048, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Härdle, Wolfgang & Hlávka, Zdenek, 2009. "Dynamics of state price densities," Journal of Econometrics, Elsevier, vol. 150(1), pages 1-15, May.
    5. Liu, Xialu & Xiao, Han & Chen, Rong, 2016. "Convolutional autoregressive models for functional time series," Journal of Econometrics, Elsevier, vol. 194(2), pages 263-282.
    6. Brüggemann, Ralf & Härdle, Wolfgang Karl & Mungo, Julius & Trenkler, Carsten, 2006. "VAR modeling for dynamic semiparametric factors of volatility strings," SFB 649 Discussion Papers 2006-011, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Lam, Clifford & Yao, Qiwei & Bathia, Neil, 2011. "Estimation of latent factors for high-dimensional time series," LSE Research Online Documents on Economics 31549, London School of Economics and Political Science, LSE Library.
    8. F. Leung & M. Law & S. K. Djeng, 2024. "Deterministic modelling of implied volatility in cryptocurrency options with underlying multiple resolution momentum indicator and non-linear machine learning regression algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-25, December.
    9. Benko, Michal & Härdle, Wolfgang Karl & Kneip, Alois, 2006. "Common functional principal components," SFB 649 Discussion Papers 2006-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  23. Fengler, Matthias R. & Härdle, Wolfgang & Mammen, Enno, 2003. "Implied volatility string dynamics," SFB 373 Discussion Papers 2003,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Janek, Agnieszka & Kluge, Tino & Weron, Rafal & Wystup, Uwe, 2010. "FX Smile in the Heston Model," MPRA Paper 25491, University Library of Munich, Germany.
    2. Mammen, Enno & Park, Byeong U. & Schienle, Melanie, 2012. "Additive models: Extensions and related models," SFB 649 Discussion Papers 2012-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Härdle, Wolfgang Karl & Myšičková, Alena, 2008. "Numerics of implied binomial trees," SFB 649 Discussion Papers 2008-044, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Matthias Fengler, 2009. "Arbitrage-free smoothing of the implied volatility surface," Quantitative Finance, Taylor & Francis Journals, vol. 9(4), pages 417-428.
    5. Kneip, Alois & Benko, Michal, 2005. "Common functional component modelling," SFB 649 Discussion Papers 2005-016, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Giacomini, Enzo & Härdle, Wolfgang Karl & Krätschmer, Volker, 2008. "Dynamic semiparametric factor models in risk neutral density estimation," SFB 649 Discussion Papers 2008-038, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Matthias Fengler & Wolfgang Härdle & Christophe Villa, 2003. "The Dynamics of Implied Volatilities: A Common Principal Components Approach," Review of Derivatives Research, Springer, vol. 6(3), pages 179-202, October.
    8. Boneva, Lena & Linton, Oliver & Vogt, Michael, 2015. "A semiparametric model for heterogeneous panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 327-345.
    9. Giacomini, Enzo & Härdle, Wolfgang Karl, 2007. "Statistics of risk aversion," SFB 649 Discussion Papers 2007-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    10. Szymon Borak & Rafał Weron, . "A semiparametric factor model for electricity forward curve dynamics," Journal of Energy Markets, Journal of Energy Markets.
    11. Hans Buehler, 2006. "Consistent Variance Curve Models," Finance and Stochastics, Springer, vol. 10(2), pages 178-203, April.
    12. René Carmona & Sergey Nadtochiy, 2009. "Local volatility dynamic models," Finance and Stochastics, Springer, vol. 13(1), pages 1-48, January.
    13. M. Benko & M. Fengler & W. Härdle & M. Kopa, 2007. "On extracting information implied in options," Computational Statistics, Springer, vol. 22(4), pages 543-553, December.
    14. Damiano Brigo & Francesco Rapisarda & Abir Sridi, 2013. "The arbitrage-free Multivariate Mixture Dynamics Model: Consistent single-assets and index volatility smiles," Papers 1302.7010, arXiv.org, revised Sep 2014.
    15. Song Song & Peter J. Bickel, 2011. "Large Vector Auto Regressions," Papers 1106.3915, arXiv.org.
    16. Bernales, Alejandro & Guidolin, Massimo, 2014. "Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 326-342.
    17. Bo Zhao & Stewart Hodges, 2013. "Parametric modeling of implied smile functions: a generalized SVI model," Review of Derivatives Research, Springer, vol. 16(1), pages 53-77, April.
    18. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.
    19. Grith, Maria & Härdle, Wolfgang Karl & Schienle, Melanie, 2010. "Nonparametric estimation of risk-neutral densities," SFB 649 Discussion Papers 2010-021, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    20. Belomestny, Denis & Reiß, Markus, 2006. "Spectral calibration of exponential Lévy Models [1]," SFB 649 Discussion Papers 2006-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    21. Michel van der Wel & Sait R. Ozturk & Dick van Dijk, 2015. "Dynamic Factor Models for the Volatility Surface," CREATES Research Papers 2015-13, Department of Economics and Business Economics, Aarhus University.
    22. Wallmeier, Martin, 2012. "Smile in Motion: An Intraday Analysis of Asymmetric Implied Volatility," FSES Working Papers 427, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    23. Song, Song & Härdle, Wolfgang Karl & Ritov, Ya'acov, 2010. "High dimensional nonstationary time series modelling with generalized dynamic semiparametric factor model," SFB 649 Discussion Papers 2010-039, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    24. Borak, Szymon & Härdle, Wolfgang Karl & Mammen, Enno & Park, Byeong U., 2007. "Time series modelling with semiparametric factor dynamics," SFB 649 Discussion Papers 2007-023, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    25. Härdle, Wolfgang Karl & Silyakova, Elena, 2012. "Implied basket correlation dynamics," SFB 649 Discussion Papers 2012-066, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    26. Hanousek, Jan & Novotný, Jan, 2012. "Price jumps in Visegrad-country stock markets: An empirical analysis," Emerging Markets Review, Elsevier, vol. 13(2), pages 184-201.
    27. Hans Buehler, 2006. "Consistent Variance Curve Models," Finance and Stochastics, Springer, vol. 10(2), pages 178-203, April.
    28. Härdle, Wolfgang Karl & Hlávka, Zdeněk, 2005. "Dynamics of state price densities," SFB 649 Discussion Papers 2005-021, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    29. Benko, Michal & Härdle, Wolfgang Karl & Kneip, Alois, 2006. "Common functional principal components," SFB 649 Discussion Papers 2006-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  24. Fengler, Matthias R. & Schwendner, Peter, 2003. "Correlation Risk Premia for Multi-Asset Equity Options," SFB 373 Discussion Papers 2003,10, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Aydınlı, Gökhan & Härdle, Wolfgang Karl & Neuwirth, E., 2003. "Computational Statistics with Spreadsheets Towards Efficiency, Reproducibility and Security," SFB 373 Discussion Papers 2003,26, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Farnaz Farzan & Khashayar Mahani & Kaveh Gharieh & Mohsen Jafari, 2015. "Microgrid investment under uncertainty: a real option approach using closed form contingent analysis," Annals of Operations Research, Springer, vol. 235(1), pages 259-276, December.
    3. Pellegrino, Tommaso & Sabino, Piergiacomo, 2014. "On the use of the moment-matching technique for pricing and hedging multi-asset spread options," Energy Economics, Elsevier, vol. 45(C), pages 172-185.

  25. Fengler, Matthias R. & Wang, Qihua, 2003. "Fitting the Smile Revisited: A Least Squares Kernel Estimator for the Implied Volatility Surface," SFB 373 Discussion Papers 2003,25, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Matthias Fengler & Wolfgang Härdle & Christophe Villa, 2003. "The Dynamics of Implied Volatilities: A Common Principal Components Approach," Review of Derivatives Research, Springer, vol. 6(3), pages 179-202, October.
    2. Vedant Choudhary & Sebastian Jaimungal & Maxime Bergeron, 2023. "FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs," Papers 2303.00859, arXiv.org, revised Dec 2023.
    3. Fengler, Matthias R. & Härdle, Wolfgang & Mammen, Enno, 2003. "Implied volatility string dynamics," SFB 373 Discussion Papers 2003,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    4. Fengler, Matthias R. & Härdle, Wolfgang Karl & Mammen, Enno, 2005. "A dynamic semiparametric factor model for implied volatility string dynamics," SFB 649 Discussion Papers 2005-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  26. Christophe Villa & M.R. Fengler & W.K. Hardle, 2003. "The dynamics of implied volatilities : a common principal components approach," Post-Print halshs-00069509, HAL.

    Cited by:

    1. Borak, Szymon & Fengler, Matthias R. & Härdle, Wolfgang Karl, 2005. "DSFM fitting of implied volatility surfaces," SFB 649 Discussion Papers 2005-022, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Fearghal Kearney & Han Lin Shang & Lisa Sheenan, 2019. "Implied volatility surface predictability: the case of commodity markets," Papers 1909.11009, arXiv.org.
    3. Vähämaa, Sami & Krylova, Elizaveta & Nikkinen, Jussi, 2005. "Cross-dynamics of volatility term structures implied by foreign exchange options," Working Paper Series 530, European Central Bank.
    4. Ci­zek, P. & Tamine, J. & Härdle, W., 2008. "Smoothed L-estimation of regression function," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5154-5162, August.
    5. Carol Alexander & Leonardo M. Nogueira, 2004. "Hedging with Stochastic and Local Volatility," ICMA Centre Discussion Papers in Finance icma-dp2004-10, Henley Business School, University of Reading, revised Dec 2004.
    6. Francesco Audrino & Dominik Colangelo, 2009. "Option trading strategies based on semi-parametric implied volatility surface prediction," University of St. Gallen Department of Economics working paper series 2009 2009-24, Department of Economics, University of St. Gallen.
    7. T. F. Coleman & Y. Kim & Y. Li & M. Patron, 2007. "Robustly Hedging Variable Annuities With Guarantees Under Jump and Volatility Risks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(2), pages 347-376, June.
    8. Da Fonseca, José & Gottschalk, Katrin, 2014. "Cross-hedging strategies between CDS spreads and option volatility during crises," Journal of International Money and Finance, Elsevier, vol. 49(PB), pages 386-400.
    9. He, Xin-Jiang & Zhu, Song-Ping, 2017. "How should a local regime-switching model be calibrated?," Journal of Economic Dynamics and Control, Elsevier, vol. 78(C), pages 149-163.
    10. Härdle, Wolfgang Karl & Myšičková, Alena, 2008. "Numerics of implied binomial trees," SFB 649 Discussion Papers 2008-044, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Kneip, Alois & Benko, Michal, 2005. "Common functional component modelling," SFB 649 Discussion Papers 2005-016, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    12. Čίžek, Pavel & Komorád, Karel, 2005. "Implied trinomial trees," SFB 649 Discussion Papers 2005-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. Fengler, Matthias R. & Wang, Qihua, 2003. "Fitting the Smile Revisited: A Least Squares Kernel Estimator for the Implied Volatility Surface," SFB 373 Discussion Papers 2003,25, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    14. Vedant Choudhary & Sebastian Jaimungal & Maxime Bergeron, 2023. "FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEs," Papers 2303.00859, arXiv.org, revised Dec 2023.
    15. Gang Li & Chu Zhang, 2010. "On the Number of State Variables in Options Pricing," Management Science, INFORMS, vol. 56(11), pages 2058-2075, November.
    16. Kim, Alisa & Trimborn, Simon & Härdle, Wolfgang Karl, 2021. "VCRIX — A volatility index for crypto-currencies," International Review of Financial Analysis, Elsevier, vol. 78(C).
    17. Xiu Xu & Cathy Yi-Hsuan Chen & Wolfgang Karl Härdle, 2019. "Dynamic credit default swap curves in a network topology," Quantitative Finance, Taylor & Francis Journals, vol. 19(10), pages 1705-1726, October.
    18. Laurini, Márcio P., 2007. "Imposing No-Arbitrage Conditions In Implied Volatility Surfaces Using Constrained Smoothing Splines," Insper Working Papers wpe_89, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    19. Bali, Juan Lucas & Boente, Graciela, 2017. "Robust estimators under a functional common principal components model," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 424-440.
    20. Chantziara, Thalia & Skiadopoulos, George, 2008. "Can the dynamics of the term structure of petroleum futures be forecasted? Evidence from major markets," Energy Economics, Elsevier, vol. 30(3), pages 962-985, May.
    21. Massimo Guidolin & Kai Wang, 2022. "The Empirical Performance of Option Implied Volatility Surface-Driven Optimal Portfolios," BAFFI CAREFIN Working Papers 22190, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    22. Beer, Simone & Braun, Alexander, 2022. "Market-consistent valuation of natural catastrophe risk," Journal of Banking & Finance, Elsevier, vol. 134(C).
    23. Mónica Fuentes & Sergio Godoy, 2005. "Sovereign Spread in Emerging Markets: A Principal Component Analysis," Working Papers Central Bank of Chile 333, Central Bank of Chile.
    24. Kun Li & Joseph D. Cursio & Yunchuan Sun, 2018. "Principal Component Analysis of Price Fluctuation in the Smart Grid Electricity Market," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    25. Yueh-Neng Lin & Shih-Kuo Yeh & Shih-Ching Chuan & Steven J. Jordan, 2008. "The link between intraday signals and call warrant mispricing," The Service Industries Journal, Taylor & Francis Journals, vol. 30(13), pages 2273-2288, November.
    26. Tanha, Hassan & Dempsey, Michael, 2016. "The evolving dynamics of the Australian SPI 200 implied volatility surface," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 44-57.
    27. Chen, Si & Zhou, Zhen & Li, Shenghong, 2016. "An efficient estimate and forecast of the implied volatility surface: A nonlinear Kalman filter approach," Economic Modelling, Elsevier, vol. 58(C), pages 655-664.
    28. Itkin, Andrey, 2015. "To sigmoid-based functional description of the volatility smile," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 264-291.
    29. Bernales, Alejandro & Guidolin, Massimo, 2014. "Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 326-342.
    30. Brüggemann, Ralf & Härdle, Wolfgang Karl & Mungo, Julius & Trenkler, Carsten, 2006. "VAR modeling for dynamic semiparametric factors of volatility strings," SFB 649 Discussion Papers 2006-011, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    31. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    32. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.
    33. Michel van der Wel & Sait R. Ozturk & Dick van Dijk, 2015. "Dynamic Factor Models for the Volatility Surface," CREATES Research Papers 2015-13, Department of Economics and Business Economics, Aarhus University.
    34. Fengler, Matthias R. & Härdle, Wolfgang & Mammen, Enno, 2003. "Implied volatility string dynamics," SFB 373 Discussion Papers 2003,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    35. Wallmeier, Martin, 2012. "Smile in Motion: An Intraday Analysis of Asymmetric Implied Volatility," FSES Working Papers 427, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    36. Fengler, Matthias R. & Härdle, Wolfgang Karl & Mammen, Enno, 2005. "A dynamic semiparametric factor model for implied volatility string dynamics," SFB 649 Discussion Papers 2005-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    37. Barletta, Andrea & Santucci de Magistris, Paolo & Sloth, David, 2019. "It only takes a few moments to hedge options," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 251-269.
    38. Martin Magris & Perttu Barholm & Juho Kanniainen, 2017. "Implied volatility smile dynamics in the presence of jumps," Papers 1711.02925, arXiv.org, revised May 2020.
    39. Miloš Kopa & Sebastiano Vitali & Tomáš Tichý & Radek Hendrych, 2017. "Implied volatility and state price density estimation: arbitrage analysis," Computational Management Science, Springer, vol. 14(4), pages 559-583, October.
    40. Han Shang, 2014. "A survey of functional principal component analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(2), pages 121-142, April.
    41. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    42. Benko, Michal & Härdle, Wolfgang Karl & Kneip, Alois, 2006. "Common functional principal components," SFB 649 Discussion Papers 2006-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    43. Sebastiano Vitali & Miloš Kopa & Gabriele Giana, 2023. "Implied volatility smoothing at COVID-19 times," Computational Management Science, Springer, vol. 20(1), pages 1-42, December.
    44. Sergey Nasekin & Wolfgang Karl Hardle, 2020. "Model-driven statistical arbitrage on LETF option markets," Papers 2009.09713, arXiv.org.
    45. Shengli Chen & Zili Zhang, 2019. "Forecasting Implied Volatility Smile Surface via Deep Learning and Attention Mechanism," Papers 1912.11059, arXiv.org.
    46. Panigirtzoglou, Nikolaos & Skiadopoulos, George, 2004. "A new approach to modeling the dynamics of implied distributions: Theory and evidence from the S&P 500 options," Journal of Banking & Finance, Elsevier, vol. 28(7), pages 1499-1520, July.

  27. Fengler, Matthias R. & Herwartz, Helmut, 2001. "Multivariate volatility models," SFB 373 Discussion Papers 2001,74, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Hafner, C.M. & Herwartz, H., 2002. "Testing for vector autoregressive dynamics under heteroskedasticity," Econometric Institute Research Papers EI 2002-36, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Fengler, Matthias R. & Schwendner, Peter, 2003. "Correlation Risk Premia for Multi-Asset Equity Options," SFB 373 Discussion Papers 2003,10, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

  28. Fengler, Matthias R. & Härdle, Wolfgang & Schmidt, Peter, 2001. "The analysis of implied volatilities," SFB 373 Discussion Papers 2001,73, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

    Cited by:

    1. Härdle, Wolfgang & Schmidt, Peter, 2000. "Common factors governing VDAX movements and the maximum loss," SFB 373 Discussion Papers 2000,97, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Fengler, Matthias R. & Wang, Qihua, 2003. "Fitting the Smile Revisited: A Least Squares Kernel Estimator for the Implied Volatility Surface," SFB 373 Discussion Papers 2003,25, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

Articles

  1. Matthias R Fengler & Jeannine Polivka, 2025. "Structural Volatility Impulse Response Analysis," Journal of Financial Econometrics, Oxford University Press, vol. 23(2), pages 951-971.
    See citations under working paper version above.
  2. Martin Brown & Matthias R. Fengler & Jonas Huwyler & Winfried Koeniger & Rafael Lalive & Robert Rohrkemper, 2023. "Monitoring consumption Switzerland: data, background, and use cases," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-16, December.
    See citations under working paper version above.
  3. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    See citations under working paper version above.
  4. Matthias R. Fengler & Helmut Herwartz, 2018. "Measuring Spot Variance Spillovers when (Co)variances are Time†varying – The Case of Multivariate GARCH Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(1), pages 135-159, February.

    Cited by:

    1. Hafner, Christian M. & Herwartz, Helmut & Maxand, Simone, 2022. "Identification of structural multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 227(1), pages 212-227.
    2. Apostolakis, George N. & Floros, Christos & Gkillas, Konstantinos & Wohar, Mark, 2024. "Volatility spillovers across the spot and futures oil markets after news announcements," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    3. Helmut Herwartz & Alberto Saucedo, 2020. "Food–oil volatility spillovers and the impact of distinct biofuel policies on price uncertainties on feedstock markets," Agricultural Economics, International Association of Agricultural Economists, vol. 51(3), pages 387-402, May.
    4. Francesco Caloia & Andrea Cipollini & Silvia Muzzioli, 2018. "On the financial connectedness of the commodity market: a replication of the Diebold and Yilmaz (2012) study," Department of Economics 0131, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    5. Lukas Boeckelmann & Arthur Stalla-Bourdillon, 2021. "Structural Estimation of Time-Varying Spillovers: An Application to International Credit Risk Transmission," Working papers 798, Banque de France.
    6. BenSaïda, Ahmed, 2019. "Good and bad volatility spillovers: An asymmetric connectedness," Journal of Financial Markets, Elsevier, vol. 43(C), pages 78-95.
    7. Apostolakis, George N. & Floros, Christos & Gkillas, Konstantinos & Wohar, Mark, 2021. "Political uncertainty, COVID-19 pandemic and stock market volatility transmission," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    8. Matthias R. Fengler & Jeannine Polivka, 2024. "Proxy-identification of a structural MGARCH model for asset returns," Swiss Finance Institute Research Paper Series 24-55, Swiss Finance Institute.
    9. Fengler, Matthias & Polivka, Jeanine, 2022. "Identifying Structural Shocks to Volatility through a Proxy-MGARCH Model," VfS Annual Conference 2022 (Basel): Big Data in Economics 264010, Verein für Socialpolitik / German Economic Association.
    10. Caloia, Francesco Giuseppe & Cipollini, Andrea & Muzzioli, Silvia, 2019. "How do normalization schemes affect net spillovers? A replication of the Diebold and Yilmaz (2012) study," Energy Economics, Elsevier, vol. 84(C).

  5. Fengler, Matthias R. & Okhrin, Ostap, 2016. "Managing risk with a realized copula parameter," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 131-152.

    Cited by:

    1. Ostap Okhrin & Anastasija Tetereva, 2017. "The Realized Hierarchical Archimedean Copula in Risk Modelling," Econometrics, MDPI, vol. 5(2), pages 1-31, June.
    2. Quanrui Song & Jianxu Liu & Songsak Sriboonchitta, 2019. "Risk Measurement of Stock Markets in BRICS, G7, and G20: Vine Copulas versus Factor Copulas," Mathematics, MDPI, vol. 7(3), pages 1-16, March.
    3. Niels Gillmann & Ostap Okhrin, 2023. "Adaptive local VAR for dynamic economic policy uncertainty spillover," Papers 2302.02808, arXiv.org.
    4. Katja Ignatieva & Natalia Ponomareva, 2017. "Commodity currencies and commodity prices: modelling static and time-varying dependence," Applied Economics, Taylor & Francis Journals, vol. 49(15), pages 1491-1512, March.
    5. Jean-David Fermanian, 2017. "Recent Developments in Copula Models," Econometrics, MDPI, vol. 5(3), pages 1-3, July.
    6. Rewat Khanthaporn, 2022. "Analysis of Nonlinear Comovement of Benchmark Thai Government Bond Yields," PIER Discussion Papers 183, Puey Ungphakorn Institute for Economic Research.

  6. Audrino, Francesco & Fengler, Matthias R., 2015. "Are classical option pricing models consistent with observed option second-order moments? Evidence from high-frequency data," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 46-63.
    See citations under working paper version above.
  7. Fengler, M.R. & Mammen, E. & Vogt, M., 2015. "Specification and structural break tests for additive models with applications to realized variance data," Journal of Econometrics, Elsevier, vol. 188(1), pages 196-218.

    Cited by:

    1. Buncic, Daniel & Gisler, Katja I. M., 2015. "Global Equity Market Volatility Spillovers: A Broader Role for the United States," Economics Working Paper Series 1508, University of St. Gallen, School of Economics and Political Science.
    2. Peter Malec, 2016. "A Semiparametric Intraday GARCH Model," Cambridge Working Papers in Economics 1633, Faculty of Economics, University of Cambridge.
    3. Mammen, Enno & Martínez-Miranda, María Dolores & Nielsen, Jens Perch & Vogt, Michael, 2021. "Calendar effect and in-sample forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 31-52.
    4. Luo, Jiawen & Demirer, Riza & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil and gold volatilities with sentiment indicators under structural breaks," Energy Economics, Elsevier, vol. 105(C).
    5. Xu Gong & Boqiang Lin, 2022. "Predicting the volatility of crude oil futures: The roles of leverage effects and structural changes," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 610-640, January.
    6. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    7. Lu, Botao & Ma, Feng & Wang, Jiqian & Ding, Hui & Wahab, M.I.M., 2021. "Harnessing the decomposed realized measures for volatility forecasting: Evidence from the US stock market," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 672-689.

  8. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "Semi-nonparametric estimation of the call-option price surface under strike and time-to-expiry no-arbitrage constraints," Journal of Econometrics, Elsevier, vol. 184(2), pages 242-261.

    Cited by:

    1. Fearghal Kearney & Han Lin Shang & Lisa Sheenan, 2019. "Implied volatility surface predictability: the case of commodity markets," Papers 1909.11009, arXiv.org.
    2. Matúš Maciak & Sebastiano Vitali, 2024. "Using interpolated implied volatility for analysing exogenous market changes," Computational Management Science, Springer, vol. 21(1), pages 1-21, June.
    3. Marius Chevallier & Stefano De Marco & Pierre-Emmanuel L'evy-dit-Vehel, 2025. "An Optimal Transport approach to arbitrage correction: Application to volatility Stress-Tests," Papers 2501.12195, arXiv.org.
    4. Dillschneider, Yannick & Maurer, Raimond, 2019. "Functional Ross recovery: Theoretical results and empirical tests," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    5. Shuang Zhou & Liyuan Jiang & Keren Li & Fangfang Wang & Jie Yang, 2024. "Natural Cubic Spline Approximation of Risk-Neutral Density," IJFS, MDPI, vol. 12(4), pages 1-31, December.
    6. Kentaro Hoshisashi & Carolyn E. Phelan & Paolo Barucca, 2023. "No-Arbitrage Deep Calibration for Volatility Smile and Skewness," Papers 2310.16703, arXiv.org, revised Jan 2024.
    7. Fengler, Matthias R. & Hin, Lin-Yee, 2014. "A simple and general approach to fitting the discount curve under no-arbitrage constraints," Economics Working Paper Series 1423, University of St. Gallen, School of Economics and Political Science.
    8. Li, Chen Xu & Li, Chenxu & Li, Chun, 2025. "Implied local volatility models," Journal of Empirical Finance, Elsevier, vol. 80(C).
    9. Taboga, Marco, 2016. "Option-implied probability distributions: How reliable? How jagged?," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 453-469.
    10. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2019. "Option Implied Risk-Neutral Density Estimation: A Robust and Flexible Method," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 705-728, August.
    11. Pierre M. Blacque-Florentin & Badr Missaoui, 2015. "Nonparametric and arbitrage-free construction of call surfaces using l1-recovery," Papers 1506.06997, arXiv.org, revised Aug 2016.
    12. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2024. "A Semi-Closed Form Approximation of Arbitrage-Free Call Option Price Surface," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1431-1457, April.
    13. Gianluca Cassese, 2015. "Non Parametric Estimates of Option Prices Using Superhedging," Papers 1502.03978, arXiv.org.
    14. Cao, Yi & Liu, Xiaoquan & Zhai, Jia, 2021. "Option valuation under no-arbitrage constraints with neural networks," European Journal of Operational Research, Elsevier, vol. 293(1), pages 361-374.
    15. Li, Yifan & Nolte, Ingmar & Pham, Manh Cuong, 2024. "Parametric risk-neutral density estimation via finite lognormal-Weibull mixtures," Journal of Econometrics, Elsevier, vol. 241(2).
    16. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2020. "Detecting and repairing arbitrage in traded option prices," Papers 2008.09454, arXiv.org.
    17. Dalderop, Jeroen, 2020. "Nonparametric filtering of conditional state-price densities," Journal of Econometrics, Elsevier, vol. 214(2), pages 295-325.
    18. Bender Christian & Thiel Matthias, 2020. "Arbitrage-free interpolation of call option prices," Statistics & Risk Modeling, De Gruyter, vol. 37(1-2), pages 55-78, January.
    19. Ana M. Monteiro & Antonio A. F. Santos, 2020. "Conditional risk-neutral density from option prices by local polynomial kernel smoothing with no-arbitrage constraints," Review of Derivatives Research, Springer, vol. 23(1), pages 41-61, April.
    20. Miloš Kopa & Sebastiano Vitali & Tomáš Tichý & Radek Hendrych, 2017. "Implied volatility and state price density estimation: arbitrage analysis," Computational Management Science, Springer, vol. 14(4), pages 559-583, October.
    21. Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle: survey and outlook," Annals of Finance, Springer, vol. 14(3), pages 289-329, August.
    22. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    23. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2021. "Arbitrage-free neural-SDE market models," Papers 2105.11053, arXiv.org, revised Aug 2021.
    24. Sebastiano Vitali & Miloš Kopa & Gabriele Giana, 2023. "Implied volatility smoothing at COVID-19 times," Computational Management Science, Springer, vol. 20(1), pages 1-42, December.
    25. Ana M. Monteiro & António A. F. Santos, 2022. "Option prices for risk‐neutral density estimation using nonparametric methods through big data and large‐scale problems," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(1), pages 152-171, January.

  9. Fengler, Matthias R. & Gisler, Katja I.M., 2015. "A variance spillover analysis without covariances: What do we miss?," Journal of International Money and Finance, Elsevier, vol. 51(C), pages 174-195.
    See citations under working paper version above.
  10. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "A simple and general approach to fitting the discount curve under no-arbitrage constraints," Finance Research Letters, Elsevier, vol. 15(C), pages 78-84.
    See citations under working paper version above.
  11. Matthias R. Fengler & Helmut Herwartz & Christian Werner, 2012. "A Dynamic Copula Approach to Recovering the Index Implied Volatility Skew," Journal of Financial Econometrics, Oxford University Press, vol. 10(3), pages 457-493, June.
    See citations under working paper version above.
  12. Matthias Fengler, 2009. "Arbitrage-free smoothing of the implied volatility surface," Quantitative Finance, Taylor & Francis Journals, vol. 9(4), pages 417-428.
    See citations under working paper version above.
  13. Matthias R. Fengler & Joachim K. Winter, 2007. "Price variability and price dispersion in a stable monetary environment: evidence from German retail markets," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 28(7), pages 789-801.
    See citations under working paper version above.
  14. M. Benko & M. Fengler & W. Härdle & M. Kopa, 2007. "On extracting information implied in options," Computational Statistics, Springer, vol. 22(4), pages 543-553, December.

    Cited by:

    1. Matúš Maciak & Sebastiano Vitali, 2024. "Using interpolated implied volatility for analysing exogenous market changes," Computational Management Science, Springer, vol. 21(1), pages 1-21, June.
    2. Cristian Homescu, 2011. "Implied Volatility Surface: Construction Methodologies and Characteristics," Papers 1107.1834, arXiv.org.
    3. Judith Glaser & Pascal Heider, 2012. "Arbitrage-free approximation of call price surfaces and input data risk," Quantitative Finance, Taylor & Francis Journals, vol. 12(1), pages 61-73, August.
    4. Martin Tegn'er & Stephen Roberts, 2019. "A Probabilistic Approach to Nonparametric Local Volatility," Papers 1901.06021, arXiv.org, revised Jan 2019.
    5. David Volkmann, 2021. "Explaining S&P500 option returns: an implied risk-adjusted approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(2), pages 665-685, June.
    6. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2019. "Option Implied Risk-Neutral Density Estimation: A Robust and Flexible Method," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 705-728, August.
    7. Arindam Kundu & Sumit Kumar & Nutan Kumar Tomar, 2024. "A Semi-Closed Form Approximation of Arbitrage-Free Call Option Price Surface," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1431-1457, April.
    8. Zdeněk Drábek & Miloš Kopa & Matúš Maciak & Michal Pešta & Sebastiano Vitali, 2023. "Investment disputes and their explicit role in option market uncertainty and overall risk instability," Computational Management Science, Springer, vol. 20(1), pages 1-25, December.
    9. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    10. Matthias Fengler, 2010. "Option data and modeling BSM implied volatility," University of St. Gallen Department of Economics working paper series 2010 2010-32, Department of Economics, University of St. Gallen.
    11. Miloš Kopa & Sebastiano Vitali & Tomáš Tichý & Radek Hendrych, 2017. "Implied volatility and state price density estimation: arbitrage analysis," Computational Management Science, Springer, vol. 14(4), pages 559-583, October.
    12. Tahar Ferhati, 2020. "Robust Calibration For SVI Model Arbitrage Free," Working Papers hal-02490029, HAL.
    13. Maciak, Matúš, 2021. "Quantile LASSO with changepoints in panel data models applied to option pricing," Econometrics and Statistics, Elsevier, vol. 20(C), pages 166-175.
    14. Tahar Ferhati, 2020. "SVI Model Free Wings," Working Papers hal-02517572, HAL.
    15. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "Semi-nonparametric estimation of the call-option price surface under strike and time-to-expiry no-arbitrage constraints," Journal of Econometrics, Elsevier, vol. 184(2), pages 242-261.
    16. Kim, Namhyoung & Lee, Jaewook, 2013. "No-arbitrage implied volatility functions: Empirical evidence from KOSPI 200 index options," Journal of Empirical Finance, Elsevier, vol. 21(C), pages 36-53.
    17. Sebastiano Vitali & Miloš Kopa & Gabriele Giana, 2023. "Implied volatility smoothing at COVID-19 times," Computational Management Science, Springer, vol. 20(1), pages 1-42, December.
    18. Dietmar P. J. Leisen, 2017. "The shape of small sample biases in pricing kernel estimations," Quantitative Finance, Taylor & Francis Journals, vol. 17(6), pages 943-958, June.
    19. Maciak, Matúš, 2021. "Quantile LASSO in arbitrage-free option markets," Econometrics and Statistics, Elsevier, vol. 18(C), pages 106-116.

  15. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.

    Cited by:

    1. Augusto Blanc-Blocquel & Luis Ortiz-Gracia & Rodolfo Oviedo, 2023. "Hedging At-the-money Digital Options Near Maturity," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-18, March.
    2. Lu, Xiaoping & Putri, Endah R.M., 2020. "A semi-analytic valuation of American options under a two-state regime-switching economy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    3. Leonidas S. Rompolis & Elias Tzavalis, 2017. "Pricing and hedging contingent claims using variance and higher order moment swaps," Quantitative Finance, Taylor & Francis Journals, vol. 17(4), pages 531-550, April.
    4. Philipp Mayer & Natalie Packham & Wolfgang Schmidt, 2015. "Static hedging under maturity mismatch," Finance and Stochastics, Springer, vol. 19(3), pages 509-539, July.
    5. Jan Maruhn & Morten Nalholm & Matthias Fengler, 2011. "Static hedges for reverse barrier options with robustness against skew risk: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(5), pages 711-727.
    6. Lee, Hangsuck & Choi, Yang Ho & Lee, Gaeun, 2022. "Multi-step barrier products and static hedging," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    7. Jeonggyu Huh & Jaegi Jeon & Yong-Ki Ma, 2020. "Static Hedges of Barrier Options Under Fast Mean-Reverting Stochastic Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 185-210, January.
    8. Johannes Siven & Rolf Poulsen, 2009. "Auto-static for the people: risk-minimizing hedges of barrier options," Review of Derivatives Research, Springer, vol. 12(3), pages 193-211, October.
    9. Thorsten Rheinlander & Michael Schmutz, 2012. "Quasi self-dual exponential L\'evy processes," Papers 1201.5132, arXiv.org.
    10. Yunbi An & Wulin Suo, 2009. "An Empirical Comparison of Option‐Pricing Models in Hedging Exotic Options," Financial Management, Financial Management Association International, vol. 38(4), pages 889-914, December.
    11. Hansjörg Albrecher & Philipp Mayer, 2010. "Semi-Static Hedging Strategies For Exotic Options," World Scientific Book Chapters, in: Rüdiger Kiesel & Matthias Scherer & Rudi Zagst (ed.), Alternative Investments And Strategies, chapter 14, pages 345-373, World Scientific Publishing Co. Pte. Ltd..
    12. Ilya Molchanov & Michael Schmutz, 2009. "Exchangeability type properties of asset prices," Papers 0901.4914, arXiv.org, revised Apr 2011.
    13. Grzegorz Krzy.zanowski & Marcin Magdziarz, 2020. "A computational weighted finite difference method for American and barrier options in subdiffusive Black-Scholes model," Papers 2003.05358, arXiv.org, revised Dec 2020.

  16. Matthias Fengler & Wolfgang Härdle & Christophe Villa, 2003. "The Dynamics of Implied Volatilities: A Common Principal Components Approach," Review of Derivatives Research, Springer, vol. 6(3), pages 179-202, October.
    See citations under working paper version above.
  17. Matthias R. Fengler & Wolfgang K. Härdle & Enno Mammen, 0. "A semiparametric factor model for implied volatility surface dynamics," Journal of Financial Econometrics, Oxford University Press, vol. 5(2), pages 189-218.

    Cited by:

    1. Fearghal Kearney & Han Lin Shang & Lisa Sheenan, 2019. "Implied volatility surface predictability: the case of commodity markets," Papers 1909.11009, arXiv.org.
    2. Cao, Xiaofeng & Okhrin, Ostap & Odening, Martin & Ritter, Matthias, 2014. "Modelling spatiotemporal variability of temperature," SFB 649 Discussion Papers 2014-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Francesco Audrino & Dominik Colangelo, 2009. "Option trading strategies based on semi-parametric implied volatility surface prediction," University of St. Gallen Department of Economics working paper series 2009 2009-24, Department of Economics, University of St. Gallen.
    4. Da Fonseca, José & Gottschalk, Katrin, 2014. "Cross-hedging strategies between CDS spreads and option volatility during crises," Journal of International Money and Finance, Elsevier, vol. 49(PB), pages 386-400.
    5. Yaxiong Zeng & Diego Klabjan, 2017. "Online Adaptive Machine Learning Based Algorithm for Implied Volatility Surface Modeling," Papers 1706.01833, arXiv.org, revised Jun 2018.
    6. Mammen, Enno & Park, Byeong U. & Schienle, Melanie, 2012. "Additive models: Extensions and related models," SFB 649 Discussion Papers 2012-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. Giacomini, Enzo & Härdle, Wolfgang Karl & Krätschmer, Volker, 2008. "Dynamic semiparametric factor models in risk neutral density estimation," SFB 649 Discussion Papers 2008-038, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    8. Francesco Audrino & Dominik Colagelo, 2007. "Forecasting Implied Volatility Surfaces," University of St. Gallen Department of Economics working paper series 2007 2007-42, Department of Economics, University of St. Gallen.
    9. Boneva, Lena & Linton, Oliver & Vogt, Michael, 2015. "A semiparametric model for heterogeneous panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 188(2), pages 327-345.
    10. Stefan Trück & Wolfgang Härdle & Rafal Weron, 2012. "The relationship between spot and futures CO2 emission allowance prices in the EU-ETS," HSC Research Reports HSC/12/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    11. Giacomini, Enzo & Härdle, Wolfgang Karl, 2007. "Statistics of risk aversion," SFB 649 Discussion Papers 2007-025, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    12. Härdle Wolfgang Karl & Silyakova Elena, 2016. "Implied basket correlation dynamics," Statistics & Risk Modeling, De Gruyter, vol. 33(1-2), pages 1-20, September.
    13. Szymon Borak & Rafał Weron, . "A semiparametric factor model for electricity forward curve dynamics," Journal of Energy Markets, Journal of Energy Markets.
    14. Jan Maruhn & Morten Nalholm & Matthias Fengler, 2011. "Static hedges for reverse barrier options with robustness against skew risk: an empirical analysis," Quantitative Finance, Taylor & Francis Journals, vol. 11(5), pages 711-727.
    15. Georgios Chalamandaris & Andrianos Tsekrekos, 2013. "Explanatory Factors and Causality in the Dynamics of Volatility Surfaces Implied from OTC Asian–Pacific Currency Options," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 327-358, March.
    16. Oliver Linton & Søren Feodor Nielsen & Jens Perch Nielsen, 2009. "Nonparametric Regression with a Latent Time Series," STICERD - Econometrics Paper Series 538, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    17. Taboga, Marco, 2016. "Option-implied probability distributions: How reliable? How jagged?," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 453-469.
    18. Song Song & Peter J. Bickel, 2011. "Large Vector Auto Regressions," Papers 1106.3915, arXiv.org.
    19. Bernales, Alejandro & Guidolin, Massimo, 2014. "Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 326-342.
    20. Fengler, Matthias & Hin, Lin-Yee, 2011. "Semi-nonparametric estimation of the call price surface under strike and time-to-expiry no-arbitrage constraints," Economics Working Paper Series 1136, University of St. Gallen, School of Economics and Political Science, revised May 2013.
    21. Ostap Okhrin & Stefan Trück, 2015. "Editorial to the special issue on Applicable semiparametrics of computational statistics," Computational Statistics, Springer, vol. 30(3), pages 641-646, September.
    22. Bastien Baldacci, 2020. "High-frequency dynamics of the implied volatility surface," Papers 2012.10875, arXiv.org.
    23. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.
    24. Bernales, Alejandro & Guidolin, Massimo, 2015. "Learning to smile: Can rational learning explain predictable dynamics in the implied volatility surface?," Journal of Financial Markets, Elsevier, vol. 26(C), pages 1-37.
    25. Michel van der Wel & Sait R. Ozturk & Dick van Dijk, 2015. "Dynamic Factor Models for the Volatility Surface," CREATES Research Papers 2015-13, Department of Economics and Business Economics, Aarhus University.
    26. Wallmeier, Martin, 2012. "Smile in Motion: An Intraday Analysis of Asymmetric Implied Volatility," FSES Working Papers 427, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    27. Song, Song & Härdle, Wolfgang Karl & Ritov, Ya'acov, 2010. "High dimensional nonstationary time series modelling with generalized dynamic semiparametric factor model," SFB 649 Discussion Papers 2010-039, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    28. Borak, Szymon & Härdle, Wolfgang Karl & Mammen, Enno & Park, Byeong U., 2007. "Time series modelling with semiparametric factor dynamics," SFB 649 Discussion Papers 2007-023, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    29. Chen, Likai & Wang, Weining & Wu, Wei Biao, 2017. "Dynamic semiparametric factor model with a common break," SFB 649 Discussion Papers 2017-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    30. Ulze, Markus & Stadler, Johannes & Rathgeber, Andreas W., 2021. "No country for old distributions? On the comparison of implied option parameters between the Brownian motion and variance gamma process," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 163-184.
    31. Wolfgang Karl Hardle & Elena Silyakova, 2020. "Implied Basket Correlation Dynamics," Papers 2009.09770, arXiv.org.
    32. Härdle, Wolfgang Karl & Silyakova, Elena, 2012. "Implied basket correlation dynamics," SFB 649 Discussion Papers 2012-066, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    33. Hanousek, Jan & Novotný, Jan, 2012. "Price jumps in Visegrad-country stock markets: An empirical analysis," Emerging Markets Review, Elsevier, vol. 13(2), pages 184-201.
    34. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    35. Choros-Tomczyk, Barbara & Härdle, Wolfgang Karl & Okhrin, Ostap, 2013. "CDO surfaces dynamics," SFB 649 Discussion Papers 2013-032, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    36. Fengler, Matthias R. & Hin, Lin-Yee, 2015. "Semi-nonparametric estimation of the call-option price surface under strike and time-to-expiry no-arbitrage constraints," Journal of Econometrics, Elsevier, vol. 184(2), pages 242-261.
    37. Choroś-Tomczyk, Barbara & Härdle, Wolfgang Karl & Okhrin, Ostap, 2016. "A semiparametric factor model for CDO surfaces dynamics," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 151-163.
    38. Sergey Nasekin & Wolfgang Karl Hardle, 2020. "Model-driven statistical arbitrage on LETF option markets," Papers 2009.09713, arXiv.org.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.