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Letteratura scientifica selezionata sul tema "Stochastic Volatility"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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Articoli di riviste sul tema "Stochastic Volatility"

1

Blanco, Belen. "Capturing the volatility smile: parametric volatility models versus stochastic volatility models." Public and Municipal Finance 5, no. 4 (December 26, 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, parametric, stochastic, Black-Scholes. JEL Classification: C14 C68 G12 G13

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2

SABANIS, SOTIRIOS. "STOCHASTIC VOLATILITY." International Journal of Theoretical and Applied Finance 05, no. 05 (August 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 given that show the practical convergence of the series. These results have been obtained by using a displaced geometric Brownian motion as a volatility process.

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3

Alghalith, Moawia, Christos Floros, and Konstantinos Gkillas. "Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility." Risks 8, no. 2 (April 11, 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 reasonably accurate. Such evidence implies that the stochastic volatility models proposed in the literature with constant volatility of volatility may fail to approximate the discrete-time short rate dynamics.

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4

Veraart, Almut E. D., and Luitgard A. M. Veraart. "Stochastic volatility and stochastic leverage." Annals of Finance 8, no. 2-3 (May 21, 2010): 205–33. http://dx.doi.org/10.1007/s10436-010-0157-3.

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5

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 (May 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 volatility model is chosen to predict the volatility of the flight block time. Predicting the flight block time volatility in real time can provide a theoretical basis for traffic traveler planning.

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6

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 (November 10, 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 EnKF algorithm computed using the solution of Black-Scholes PDE. The state-space used in this method is the augmented state space, which consists of static variables: volatility and interest rate, and dynamic variables: call and put option price. The numerical experiment shows that the EnKF algorithm is able to estimate accurately the estimated volatility and interest rates with an RMSE value of 0.0506.Keywords: stochastic volatility; call option; put option; Ensemble Kalman Filter. AbstrakVolatilitas adalah faktor penting dalam perdagangan suatu opsi. Dalam makalahnya yang dipublikasikan tahun 1973, Black dan Scholes mengasumsikan bahwa volatilitas harga saham, yang merupakan volatilitas sekuritas yang mendasari opsi beli, adalah konstan. Akan tetapi, para peneliti menemukan bahwa volatilitas pengembalian tidaklah konstan melainkan tergantung pada kumpulan informasi yang dapat digunakan pada saat perhitungan. Pada penelitian ini dikembangkan metodologi untuk mengestimasi volatilitas dan suku bunga menggunakan metode Ensembel Kalman Filter (EnKF). Harga opsi beli dan opsi jual yang digunakan pada observasi dan pada tahap prakiraan pada algoritma EnKF dihitung menggunakan solusi persamaan Black-Scholes. Ruang keadaan yang digunakan adalah ruang keadaan yang diperluas yang terdiri dari variabel statis yaitu volatilitas dan suku bunga, dan variabel dinamis yaitu harga opsi beli dan harga opsi jual. Eksperimen numerik menunjukkan bahwa algoritma ENKF dapat secara akurat mengestimasi volatiltas dan suku bunga dengan RMSE 0.0506.Kata kunci: volatilitas stokastik; opsi beli; opsi jual; Ensembel Kalman Filter.

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7

Guyon, Julien. "Stochastic Volatility Modeling." Quantitative Finance 17, no. 6 (April 18, 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 (July 3, 2018): 1207–55. http://dx.doi.org/10.1017/s0266466617000457.

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Abstract (sommario):

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 of the bivariate process’ infinitesimal moments. For these infinitesimal moment estimates, we report an asymptotic theory relying on joint in-fill and long-span arguments which yields consistency and weak convergence under mild assumptions.

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9

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

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10

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

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Tesi sul tema "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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2

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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3

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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4

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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Abstract (sommario):

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 the performance of our model using the data of interest rates in Turkey.

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5

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 real-market option data for underlying assets including an equity (ABB stock) and a market index (EURO STOXX 50), for two separated time spans from Jan 2017 to Dec 2017 (before the COVID-19 pandemic) and from Nov 2019 to Nov 2020 (after the start of COVID-19 pandemic). We investigate the optimal way of dividing the set of option maturities into three classes, namely, the short-maturity, middle-maturity, and long-maturity classes. We calibrate our HJM-SV model to the data in the following way, for each class a single-factor Heston stochastic volatility model is calibrated to the corresponding market data. We address the question that how well the new HJM-SV model captures the feature of implied volatility surface given by the market data.

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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 backbone of this thesis, since they led to the following idea: instead of calibrating a model using a computer-intensive global optimisation algorithm, it should be more efficient to use a less robust—hence faster—algorithm, but with an accurate starting point. Henceforth deriving closed-form approximation formulae for the implied-volatility should provide a way to obtain such accurate initial points, thus ensuring a faster calibration. In this thesis we propose such a calibration approach based on the time-asymptotics of affine stochastic volatility models with jumps. Mathematically since this class of models is defined via its Laplace transform, the tools we naturally use are large deviations theory as well as complex saddle-point methods. Large deviations enable us to obtain the limiting behaviour (in small or large time) of the implied volatility, and saddle-point methods are needed to obtain more accurate results on the speed of convergence. We also provide numerical evidence in order to highlight the accuracy of the closed-form approximations thus obtained, and compare them to standard pricing methods based on real calibrated data.

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7

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 obtained.

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8

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-ahead forecasts of realised measures to examine the forecast precision sensitivity to the rolling window length. Use of the longest window length does not lead to the lowest mean square error. The dominance of the Realised Stochastic Volatility model in terms of the lowest mean square errors of one day-ahead out-of-sample forecasts has been confirmed.

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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 estimated as univariate models. I compared the models according to Mincer Zarnowitz regression. The most successfull model is the jump diffusion model with a stochastic volatility. On the second place they were the GJR- GARCH model and the jump diffusion model with a constant volatility. But the jump diffusion model with a constat volatilit provided much more overvalued results.The rest of the models were even worse. From the rest the IGARCH model is the best but provided undervalued results. All these findings correspond with R squared coefficient.

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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 gran volatilidad y viceversa. En este capítulo, como en toda la tesis, uso el método de estimación eficiente de momentos de Gallant y Tauchen (1996). De la estimación surgen dos modelos posibles de describir los datos, el modelo logarítmico con factor de volatilidad y retroalimentación y el modelo logarítmico con dos factores de volatilidad. Como no es posible elegir entre ellos basados en los tests efectuados en la fase de la estimación, tendremos que usar el método de reprogección para obtener mas herramientas de comparación. El modelo con un factor de volatilidad se comporta muy bien y es capaz de captar la "quiebra" de los mercados financieros de 1987.<br/>En el segundo capítulo, hago la evaluación del modelo con dos factores de volatilidad en términos de predicción y comparo esa predicción con las obtenidas con los modelos GARCH y ARFIMA. La evaluación de la predicción para los tres modelos es hecha con la ayuda del R2 de las regresiones individuales de la volatilidad "realizada" en una constante y en las predicciones. Los resultados empíricos indican un mejor comportamiento del modelo en tiempo continuo. Es más, los modelos GARCH y ARFIMA parecen tener problemas en seguir la marcha de la volatilidad "realizada". <br/>Finalmente, en el tercer capítulo hago una extensión del modelo de volatilidad estocástica de memoria larga de Harvey (2003). O sea, introduzco un factor de volatilidad de corto plazo. Este factor extra aumenta la curtosis y ayuda a captar la persistencia (que es captada con un proceso integrado fraccional, como en Harvey (1993)). Los resultados son probados y el modelo implementado empíricamente.<br>The purpose of my thesis is to model and forecast the volatility of the financial series of returns by using both continuous and discrete time stochastic volatility models.<br/>In my first chapter I try to fit the main characteristics of the financial series of returns such as: volatility persistence, volatility clustering and fat tails of the distribution of the returns.The estimated logarithmic stochastic volatility models are direct extensions of the Gallant and Tauchen's (2001) by including the feedback feature. This feature is of extreme importance because it allows to capture the low variability of the volatility factor when the factor is itself low (volatility clustering) and it also captures the increase in volatility persistence that occurs when there is an apparent change in the pattern of volatility at the very end of the sample. In this chapter, as well as in all the thesis, I use Efficient Method of Moments of Gallant and Tauchen (1996) as an estimation method. From the estimation step, two models come out, the logarithmic model with one factor of volatility and feedback (L1F) and the logarithmic model with two factors of volatility (L2). Since it is not possible to choose between them based on the diagnostics computed at the estimation step, I use the reprojection step to obtain more tools for comparing models. The L1F is able to reproject volatility quite well without even missing the crash of 1987.<br/>In the second chapter I fit the continuous time model with two factors of volatility of Gallant and Tauchen (2001) for the return of a Microsoft share. The aim of this chapter is to evaluate the volatility forecasting performance of the continuous time stochastic volatility model comparatively to the ones obtained with the traditional GARCH and ARFIMA models. In order to inquire into this, I estimate using the Efficient Method of Moments (EMM) of Gallant and Tauchen (1996) a continuous time stochastic volatility model for the logarithm of asset price and I filter the underlying volatility using the reprojection technique of Gallant and Tauchen (1998). Under the assumption that the model is correctly specified, I obtain a consistent estimator of the integrated volatility by fitting a continuous time stochastic volatility model to the data. The forecasting evaluation for the three estimated models is going to be done with the help of the R2 of the individual regressions of realized volatility on the volatility forecasts obtained from the estimated models. The empirical results indicate the better performance of the continuous time model in the out-of-sample periods compared to the ones of the traditional GARCH and ARFIMA models. Further, these two last models show difficulties in tracking the growth pattern of the realized volatility. This probably is due to the change of pattern in volatility in this last part of the sample.<br/> Finally, in the third chapter I come back to the model specification and I extend the long memory stochastic volatility model of Harvey (1993) by introducing a short run volatility factor. This extra factor increases kurtosis and helps the model capturing volatility persistence (that it is captured by a fractionally integrated process as in Harvey (1993) ). Futhermore, considering some restrictions of the parameters it is possible to fit the empirical fact of small first order autocorrelation of squared returns. All these results are proved theoretically and the model is implemented empirically using the S&P 500 composite index returns. The empirical results show the superiority of the model in fitting the main empirical facts of the financial series of returns.

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Libri sul tema "Stochastic Volatility"

1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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

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

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

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

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Capitoli di libri sul tema "Stochastic Volatility"

1

Chiarella, Carl, Xue-Zhong He, and Christina Sklibosios Nikitopoulos. "Stochastic Volatility." In Dynamic Modeling and Econometrics in Economics and Finance, 315–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45906-5_15.

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2

Andersen, Torben G., and Luca Benzoni. "Stochastic Volatility." In Complex Systems in Finance and Econometrics, 694–726. New York, NY: 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, 1–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27737-5_527-3.

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4

Lorig, Matthew, and Ronnie Sircar. "Stochastic Volatility." In Financial Signal Processing and Machine Learning, 135–61. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118745540.ch7.

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5

Privault, Nicolas. "Stochastic Volatility." In Introduction to Stochastic Finance with Market Examples, 249–76. 2nd ed. Boca Raton: 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, 8783–815. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_527.

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7

Austing, Peter. "Stochastic Volatility." In Smile Pricing Explained, 71–95. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137335722_7.

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8

Takahashi, Makoto, Yasuhiro Omori, and Toshiaki Watanabe. "Stochastic Volatility Model." In Stochastic Volatility and Realized Stochastic Volatility Models, 7–30. Singapore: 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, 31–55. Singapore: 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, 1–6. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0935-3_1.

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Atti di convegni sul tema "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), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icoa62581.2024.10753751.

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2

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), 1–7. IEEE, 2024. https://doi.org/10.1109/iipem62726.2024.10925737.

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3

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, 807–13. Hamilton, Canada: PSE Press, 2024. http://dx.doi.org/10.69997/sct.141495.

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Abstract (sommario):

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 production sites in the supply chain network and a reduction in the quantity and cost of conventional ammonia imported over the supply chain transition horizon. These results show that local renewable ammonia production can act as a hedge against the volatility of the conventional ammonia market.

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4

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), 93–102. IEEE, 2025. https://doi.org/10.1109/iscbi64586.2025.11015399.

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5

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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6

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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7

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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8

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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9

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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10

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

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Rapporti di organizzazioni sul tema "Stochastic Volatility"

1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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7

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

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8

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

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9

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

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10

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

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