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Relevant bibliographies by topics / Stochastic Volatility

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Academic literature on the topic 'Stochastic Volatility'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Stochastic Volatility"

1

Blanco, Belen. "Capturing the volatility smile: parametric volatility models versus stochastic volatility models." Public and Municipal Finance 5, no. 4 (2016): 15–22. http://dx.doi.org/10.21511/pmf.05(4).2016.02.

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Black-Scholes option pricing model (1973) assumes that all option prices on the same underlying asset with the same expiration date, but different exercise prices should have the same implied volatility. However, instead of a flat implied volatility structure, implied volatility (inverting the Black-Scholes formula) shows a smile shape across strikes and time to maturity. This paper compares parametric volatility models with stochastic volatility models in capturing this volatility smile. Results show empirical evidence in favor of parametric volatility models. Keywords: smile volatility, para

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SABANIS, SOTIRIOS. "STOCHASTIC VOLATILITY." International Journal of Theoretical and Applied Finance 05, no. 05 (2002): 515–30. http://dx.doi.org/10.1142/s021902490200150x.

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Hull and White [1] have priced a European call option for the case in which the volatility of the underlying asset is a lognormally distributed random variable. They have obtained their formula under the assumption of uncorrelated innovations in security price and volatility. Although the option pricing formula has a power series representation, the question of convergence has been left unanswered. This paper presents an iterative method for calculating all the higher order moments of volatility necessary for the process of proving convergence theoretically. Moreover, simulation results are gi

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Alghalith, Moawia, Christos Floros, and Konstantinos Gkillas. "Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility." Risks 8, no. 2 (2020): 35. http://dx.doi.org/10.3390/risks8020035.

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We propose novel nonparametric estimators for stochastic volatility and the volatility of volatility. In doing so, we relax the assumption of a constant volatility of volatility and therefore, we allow the volatility of volatility to vary over time. Our methods are exceedingly simple and far simpler than the existing ones. Using intraday prices for the Standard & Poor’s 500 equity index, the estimates revealed strong evidence that both volatility and the volatility of volatility are stochastic. We also proceeded in a Monte Carlo simulation analysis and found that the estimates were reasona

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Veraart, Almut E. D., and Luitgard A. M. Veraart. "Stochastic volatility and stochastic leverage." Annals of Finance 8, no. 2-3 (2010): 205–33. http://dx.doi.org/10.1007/s10436-010-0157-3.

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Sun, Ya, Meiyi Wang, and Hua Xie. "Volatility analysis of the flight block time based on the stochastic volatility model." Journal of Physics: Conference Series 2489, no. 1 (2023): 012002. http://dx.doi.org/10.1088/1742-6596/2489/1/012002.

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Abstract To effectively predict the volatility of flight block time, this paper constructs a stochastic volatility model based on actual flight block time data, solves the model parameters by the Markov chain Monte Carlo method, and uses the standard stochastic volatility (SV-N) model and thick-tailed stochastic volatility (SV-T) model to characterize the volatility of flight block time. The results show that the thick-tailed stochastic volatility model is better than the standard stochastic volatility model in describing the volatility of the segment runtime, and the thick-tailed stochastic v

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Mahatma, Yudi, and Ibnu Hadi. "Stochastic Volatility Estimation of Stock Prices using the Ensemble Kalman Filter." InPrime: Indonesian Journal of Pure and Applied Mathematics 3, no. 2 (2021): 136–43. http://dx.doi.org/10.15408/inprime.v3i2.20256.

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AbstractVolatility plays important role in options trading. In their seminal paper published in 1973, Black and Scholes assume that the stock price volatility, which is the underlying security volatility of a call option, is constant. But thereafter, researchers found that the return volatility was not constant but conditional to the information set available at the computation time. In this research, we improve a methodology to estimate volatility and interest rate using Ensemble Kalman Filter (EnKF). The price of call and put option used in the observation and the forecasting step of the EnK

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Guyon, Julien. "Stochastic Volatility Modeling." Quantitative Finance 17, no. 6 (2017): 825–28. http://dx.doi.org/10.1080/14697688.2017.1309181.

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Bandi, Federico M., and Roberto Renò. "NONPARAMETRIC STOCHASTIC VOLATILITY." Econometric Theory 34, no. 6 (2018): 1207–55. http://dx.doi.org/10.1017/s0266466617000457.

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We provide nonparametric methods for stochastic volatility modeling. Our methods allow for the joint evaluation of return and volatility dynamics with nonlinear drift and diffusion functions, nonlinear leverage effects, and jumps in returns and volatility with possibly state-dependent jump intensities, among other features. In the first stage, we identify spot volatility by virtue of jump-robust nonparametric estimates. Using observed prices and estimated spot volatilities, the second stage extracts the functions and parameters driving price and volatility dynamics from nonparametric estimates

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Capobianco, E. "Stochastic Volatility Systems." International Journal of Modelling and Simulation 17, no. 2 (1997): 137–42. http://dx.doi.org/10.1080/02286203.1997.11760322.

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Ilinski, Kirill, and Oleg Soloviev. "Stochastic volatility membrane." Wilmott 2004, no. 3 (2004): 74–81. http://dx.doi.org/10.1002/wilm.42820040317.

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Dissertations / Theses on the topic "Stochastic Volatility"

1

Andersson, Kristina. "Stochastic Volatility." Thesis, Uppsala University, Department of Mathematics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121722.

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Galiotos, Vassilis. "Stochastic Volatility and the Volatility Smile." Thesis, Uppsala University, Department of Mathematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120151.

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Le, Truc. "Stochastic volatility models." Monash University, School of Mathematical Sciences, 2005. http://arrow.monash.edu.au/hdl/1959.1/5181.

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Zeytun, Serkan. "Stochastic Volatility, A New Approach For Vasicek Model With Stochastic Volatility." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606561/index.pdf.

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In the original Vasicek model interest rates are calculated assuming that volatility remains constant over the period of analysis. In this study, we constructed a stochastic volatility model for interest rates. In our model we assumed not only that interest rate process but also the volatility process for interest rates follows the mean-reverting Vasicek model. We derived the density function for the stochastic element of the interest rate process and reduced this density function to a series form. The parameters of our model were estimated by using the method of moments. Finally, we tested th

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Cap, Thi Diu. "Implied volatility with HJM–type Stochastic Volatility model." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54938.

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In this thesis, we propose a new and simple approach of extending the single-factor Heston stochastic volatility model to a more flexible one in solving option pricing problems. In this approach, the volatility process for the underlying asset dynamics depends on the time to maturity of the option. As this idea is inspired by the Heath-Jarrow-Morton framework which models the evolution of the full dynamics of forward rate curves for various maturities, we name this approach as the HJM-type stochastic volatility (HJM-SV) model. We conduct an empirical analysis by calibrating this model to rea

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Jacquier, Antoine. "Implied volatility asymptotics under affine stochastic volatility models." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/6142.

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This thesis is concerned with the calibration of affine stochastic volatility models with jumps. This class of models encompasses most models used in practice and captures some of the common features of market data such as jumps and heavy tail distributions of returns. Two questions arise when one wants to calibrate such a model: (a) How to check its theoretical consistency with the relevant market characteristics? (b) How to calibrate it rigorously to market data, in particular to the so-called implied volatility, which is a normalised measure of option prices? These two questions form the ba

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Ozkan, Pelin. "Analysis Of Stochastic And Non-stochastic Volatility Models." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605421/index.pdf.

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Changing in variance or volatility with time can be modeled as deterministic by using autoregressive conditional heteroscedastic (ARCH) type models, or as stochastic by using stochastic volatility (SV) models. This study compares these two kinds of models which are estimated on Turkish / USA exchange rate data. First, a GARCH(1,1) model is fitted to the data by using the package E-views and then a Bayesian estimation procedure is used for estimating an appropriate SV model with the help of Ox code. In order to compare these models, the LR test statistic calculated for non-nested hypotheses is

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Vavruška, Marek. "Realised stochastic volatility in practice." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-165381.

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Realised Stochastic Volatility model of Koopman and Scharth (2011) is applied to the five stocks listed on NYSE in this thesis. Aim of this thesis is to investigate the effect of speeding up the trade data processing by skipping the cleaning rule requiring the quote data. The framework of the Realised Stochastic Volatility model allows the realised measures to be biased estimates of the integrated volatility, which further supports this approach. The number of errors in recorded trades has decreased significantly during the past years. Different sample lengths were used to construct one day-ah

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Hrbek, Filip. "Metody předvídání volatility." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264689.

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In this masterthesis I have rewied basic approaches to volatility estimating. These approaches are based on classical and Bayesian statistics. I have applied the volatility models for the purpose of volatility forecasting of a different foreign exchange (EURUSD, GBPUSD and CZKEUR) in the different period (from a second period to a day period). I formulate the models EWMA, GARCH, EGARCH, IGARCH, GJRGARCH, jump diffuison with constant volatility and jump diffusion model with stochastic volatility. I also proposed an MCMC algorithm in order to estimate the Bayesian models. All the models we estim

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Lopes, Moreira de Veiga Maria Helena. "Modelling and forecasting stochastic volatility." Doctoral thesis, Universitat Autònoma de Barcelona, 2004. http://hdl.handle.net/10803/4046.

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El objetivo de esta tesis es modelar y predecir la volatilidad de las series financieras con modelos de volatilidad en tiempo discreto y continuo.<br/>En mi primer capítulo, intento modelar las principales características de las series financieras, como a persistencia y curtosis. Los modelos de volatilidad estocástica estimados son extensiones directas de los modelos de Gallant y Tauchen (2001), donde incluyo un elemento de retro-alimentación. Este elemento es de extrema importancia porque permite captar el hecho de que períodos de alta volatilidad están, en general, seguidos de periodos de gr

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Books on the topic "Stochastic Volatility"

1

Takahashi, Makoto, Yasuhiro Omori, and Toshiaki Watanabe. Stochastic Volatility and Realized Stochastic Volatility Models. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0935-3.

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Hafner, Reinhold. Stochastic Implied Volatility. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17117-8.

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Neil, Shephard, ed. Stochastic volatility: Selected readings. Oxford University Press, 2005.

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Fornari, Fabio, and Antonio Mele. Stochastic Volatility in Financial Markets. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4533-0.

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Harvey, Andrew. The econometrics of stochastic volatility. London School of Economics Financial Markets Group, 1993.

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Bishwal, Jaya P. N. Parameter Estimation in Stochastic Volatility Models. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03861-7.

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7

Melino, Angelo. Pricing foreign currency options with stochastic volatility. Dept. of Economics; Institute for Policy Analysis, University of Toronto, 1988.

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8

Hafner, Reinhold. Stochastic implied volatility: A factor-based model. Springer, 2004.

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9

Sandmann, G. Maximum likelihood estimation of stochastic volatility models. London School of Economics, Financial Markets Group, 1996.

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Aït-Sahalia, Yacine. Maximum likelihood estimation of stochastic volatility models. National Bureau of Economic Research, 2004.

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Book chapters on the topic "Stochastic Volatility"

1

Chiarella, Carl, Xue-Zhong He, and Christina Sklibosios Nikitopoulos. "Stochastic Volatility." In Dynamic Modeling and Econometrics in Economics and Finance. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45906-5_15.

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Andersen, Torben G., and Luca Benzoni. "Stochastic Volatility." In Complex Systems in Finance and Econometrics. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-7701-4_38.

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Andersen, Torben G., and Luca Benzoni. "Stochastic Volatility." In Encyclopedia of Complexity and Systems Science. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27737-5_527-3.

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Lorig, Matthew, and Ronnie Sircar. "Stochastic Volatility." In Financial Signal Processing and Machine Learning. John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118745540.ch7.

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Privault, Nicolas. "Stochastic Volatility." In Introduction to Stochastic Finance with Market Examples, 2nd ed. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003298670-8.

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Andersen, Torben G., and Luca Benzoni. "Stochastic Volatility." In Encyclopedia of Complexity and Systems Science. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_527.

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Austing, Peter. "Stochastic Volatility." In Smile Pricing Explained. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137335722_7.

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Takahashi, Makoto, Yasuhiro Omori, and Toshiaki Watanabe. "Stochastic Volatility Model." In Stochastic Volatility and Realized Stochastic Volatility Models. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0935-3_2.

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Takahashi, Makoto, Yasuhiro Omori, and Toshiaki Watanabe. "Asymmetric Stochastic Volatility Model." In Stochastic Volatility and Realized Stochastic Volatility Models. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0935-3_3.

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Takahashi, Makoto, Yasuhiro Omori, and Toshiaki Watanabe. "Introduction." In Stochastic Volatility and Realized Stochastic Volatility Models. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0935-3_1.

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Conference papers on the topic "Stochastic Volatility"

1

Benjouad, Abdelghani, and Mohammed Kaicer. "Efficient Simulations for Pricing Barrier Options under Stochastic Volatility Model." In 2024 10th International Conference on Optimization and Applications (ICOA). IEEE, 2024. http://dx.doi.org/10.1109/icoa62581.2024.10753751.

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K P, Premalatha, Vinoth Kumar V, Thilak Reddy, and Navaneetha Kumar V. "AI-Enhanced Stochastic Volatility Modelling: A Comparative Analysis with Black-Scholes and Binomial Models." In 2024 International Conference on Intelligent & Innovative Practices in Engineering & Management (IIPEM). IEEE, 2024. https://doi.org/10.1109/iipem62726.2024.10925737.

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Mitrai, Ilias, Matthew J. Palys, and Prodromos Daoutidis. "Optimal Transition of Ammonia Supply Chain Networks via Stochastic Programming." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.141495.

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This paper considers the optimal incorporation of renewable ammonia production facilities into existing supply chain networks which import ammonia from conventional producers while accounting for uncertainty in this conventional ammonia price. We model the supply chain transition problem as a two-stage stochastic optimization problem which is formulated as a Mixed Integer Linear Programming problem. We apply the proposed approach to a case study on Minnesota's ammonia supply chain. We find that accounting for conventional price uncertainty leads to earlier incorporation of in-state renewable p

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Sun, Yongbo. "Application of Copula in S&P 500 Index Option Pricing with non-Gaussian Stochastic Volatility Models." In 2025 7th International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2025. https://doi.org/10.1109/iscbi64586.2025.11015399.

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Tian, Yu, Zili Zhu, Fima Klebaner, and Kais Hamza. "A Hybrid Stochastic Volatility Model Incorporating Local Volatility." In 2012 Fourth International Conference on Computational and Information Sciences (ICCIS). IEEE, 2012. http://dx.doi.org/10.1109/iccis.2012.20.

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Gonzaga, Alex C. "Seasonal long-memory stochastic volatility." In 11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013: ICNAAM 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4826027.

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Simandl, Miroslav, and Tomas Soukup. "Gibbs sampler to stochastic volatility models." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076061.

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Hsu, Ai-Chi, Hsiao-Fen Hsiao, and Shih-Jui Yang. "A Grey-Artificial Neural Network Stochastic Volatility Model for Return Volatility." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5301917.

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Kanniainen, Juho. "Cause of Stock Return Stochastic Volatility: Query by Way of Stochastic Calculus." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0003.

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Yu, Jun, and Zhenlin Yang. "A class of nonlinear stochastic volatility models." In 9th Joint Conference on Information Sciences. Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.87.

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Reports on the topic "Stochastic Volatility"

1

Campbell, John, Stefano Giglio, Christopher Polk, and Robert Turley. An Intertemporal CAPM with Stochastic Volatility. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w18411.

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Ait-Sahalia, Yacine, and Robert Kimmel. Maximum Likelihood Estimation of Stochastic Volatility Models. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10579.

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Fernandez-Villaverde, Jesus, Pablo Guerrón-Quintana, and Juan Rubio-Ramírez. Estimating Dynamic Equilibrium Models with Stochastic Volatility. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w18399.

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Mulligan, Casey. Robust Aggregate Implications of Stochastic Discount Factor Volatility. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10210.

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Kristensen, Dennis, and Shin Kanaya. Estimation of stochastic volatility models by nonparametric filtering. Institute for Fiscal Studies, 2015. http://dx.doi.org/10.1920/wp.cem.2015.0915.

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Jang, Minsung. MCMC Estimation of Return Dynamics with Stochastic Volatility. Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-994.

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Trolle, Anders, and Eduardo Schwartz. Unspanned Stochastic Volatility and the Pricing of Commodity Derivatives. National Bureau of Economic Research, 2006. http://dx.doi.org/10.3386/w12744.

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Alizadeh, Sassan, Michael Brandt, and Francis Diebold. High- and Low-Frequency Exchange Rate Volatility Dynamics: Range-Based Estimation of Stochastic Volatility Models. National Bureau of Economic Research, 2001. http://dx.doi.org/10.3386/w8162.

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Diebold, Francis, Frank Schorfheide, and Minchul Shin. Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility. National Bureau of Economic Research, 2016. http://dx.doi.org/10.3386/w22615.

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Baldivieso, Sebastian. Sensitivity Diagnostics and Adaptive Tuning of the Multivariate Stochastic Volatility Model. Portland State University Library, 2020. http://dx.doi.org/10.15760/etd.7296.

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