Relevant bibliographies by topics / Jump Diffusion Model

Academic literature on the topic 'Jump Diffusion Model'

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Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Jump Diffusion Model"

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Hutzenthaler, Martin, and Jesse Earl Taylor. "Time reversal of some stationary jump diffusion processes from population genetics." Advances in Applied Probability 42, no. 4 (December 2010): 1147–71. http://dx.doi.org/10.1239/aap/1293113155.

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We describe the processes obtained by time reversal of a class of stationary jump diffusion processes that model the dynamics of genetic variation in populations subject to repeated bottlenecks. Assuming that only one lineage survives each bottleneck, the forward process is a diffusion on [0,1] that jumps to the boundary before diffusing back into the interior. We show that the behavior of the time-reversed process depends on whether the boundaries are accessible to the diffusive motion of the forward process. If a boundary point is inaccessible to the forward diffusion then time reversal leads to a jump diffusion that jumps immediately into the interior whenever it arrives at that point. If, instead, a boundary point is accessible then the jumps off of that point are governed by a weighted local time of the time-reversed process.
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Hutzenthaler, Martin, and Jesse Earl Taylor. "Time reversal of some stationary jump diffusion processes from population genetics." Advances in Applied Probability 42, no. 04 (December 2010): 1147–71. http://dx.doi.org/10.1017/s0001867800004560.

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We describe the processes obtained by time reversal of a class of stationary jump diffusion processes that model the dynamics of genetic variation in populations subject to repeated bottlenecks. Assuming that only one lineage survives each bottleneck, the forward process is a diffusion on [0,1] that jumps to the boundary before diffusing back into the interior. We show that the behavior of the time-reversed process depends on whether the boundaries are accessible to the diffusive motion of the forward process. If a boundary point is inaccessible to the forward diffusion then time reversal leads to a jump diffusion that jumps immediately into the interior whenever it arrives at that point. If, instead, a boundary point is accessible then the jumps off of that point are governed by a weighted local time of the time-reversed process.
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Davis, Mark, and Sébastien Lleo. "Jump-Diffusion Risk-Sensitive Asset Management II: Jump-Diffusion Factor Model." SIAM Journal on Control and Optimization 51, no. 2 (January 2013): 1441–80. http://dx.doi.org/10.1137/110825881.

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Liu, Weijie, Yan Shen, and Lijuan Shen. "Degradation Modeling for Lithium-Ion Batteries with an Exponential Jump-Diffusion Model." Mathematics 10, no. 16 (August 19, 2022): 2991. http://dx.doi.org/10.3390/math10162991.

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The degradation of Lithium-ion batteries is usually measured by capacity loss. When batteries deteriorate with usage, the capacities would generally have a declining trend. However, occasionally, considerable capacity regeneration may occur during the degradation process. To better capture the coexistence of capacity loss and regeneration, this paper considers a jump-diffusion model with jumps subject to the exponential distribution. For estimation of model parameters, a jump detection test is first adopted to identify jump arrival times and separate observation data into two series, jump series and diffusion series; then, with the help of probabilistic programming, the Markov chain Monte Carlo sampling algorithm is used to estimate the parameters for the jump and diffusion parts of the degradation model, respectively. The distribution functions of failure time and residual useful life are also approximated by the Monte Carlo simulation approach. Simulation results show the feasibility and good performance of the combined estimation method. Finally, real data analysis indicates that the jump-diffusion process model with the combined estimation method could give a more accurate estimation when predicting the failure time of the battery.
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Wang, Zhouwei, Qicheng Zhao, Min Zhu, and Tao Pang. "Jump Aggregation, Volatility Prediction, and Nonlinear Estimation of Banks’ Sustainability Risk." Sustainability 12, no. 21 (October 25, 2020): 8849. http://dx.doi.org/10.3390/su12218849.

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Extreme financial events usually lead to sharp jumps in stock prices and volatilities. In addition, jump clustering and stock price correlations contribute to the risk amplification acceleration mechanism during the crisis. In this paper, four Jump-GARCH models are used to forecast the jump diffusion volatility, which is used as the risk factor. The linear and asymmetric nonlinear effects are considered, and the value at risk of banks is estimated by support vector quantile regression. There are three main findings. First, in terms of the volatility process of bank stock price, the Jump Diffusion GARCH model is better than the Continuous Diffusion GARCH model, and the discrete jump volatility is significant. Secondly, due to the difference of the sensitivity of abnormal information shock, the jump behavior of bank stock price is heterogeneous. Moreover, CJ-GARCH models are suitable for most banks, while ARJI-R2-GARCH models are more suitable for small and medium sized banks. Thirdly, based on the jump diffusion volatility information, the performance of the support vector quantile regression is better than that of the parametric quantile regression and nonparametric quantile regression.
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Zheng, Yingchun, and Yunfeng Yang. "Wealth optimization models on jump-diffusion model." Journal of Interdisciplinary Mathematics 21, no. 1 (January 2, 2018): 201–12. http://dx.doi.org/10.1080/09720502.2017.1406629.

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Deng, Guohe. "Option Pricing under Two-Factor Stochastic Volatility Jump-Diffusion Model." Complexity 2020 (September 1, 2020): 1–15. http://dx.doi.org/10.1155/2020/1960121.

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Empirical evidence shows that single-factor stochastic volatility models are not flexible enough to account for the stochastic behavior of the skew, and certain financial assets may exhibit jumps in returns and volatility. This paper introduces a two-factor stochastic volatility jump-diffusion model in which two variance processes with jumps drive the underlying stock price and then considers the valuation on European style option. We derive a semianalytical formula for European vanilla option and develop a fast and accurate numerical algorithm for the computation of the option prices using the fast Fourier transform (FFT) technique. We compare the volatility smile and probability density of the proposed model with those of alternative models, including the normal jump diffusion model and single-factor stochastic volatility model with jumps, respectively. Finally, we provide some sensitivity analysis of the model parameters to the options and several calibration tests using option market data. Numerical examples show that the proposed model has more flexibility to capture the implied volatility term structure and is suitable for empirical work in practice.
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Jiahui, Yang, Zhou Shengwu, Zhou Haitao, and Guo Kaiqiang. "Pricing Vulnerable Option under Jump-Diffusion Model with Incomplete Information." Discrete Dynamics in Nature and Society 2019 (May 20, 2019): 1–8. http://dx.doi.org/10.1155/2019/5848375.

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In this paper, the closed-form pricing formula for the European vulnerable option with credit risk and jump risk under incomplete information was derived. Noise was introduced to the option writers assets while the underlying asset price and the value of corporation were assumed to follow the jump-diffusion processes. Finally the numerical experiment showed that jumps of underlying assets would increase the value of the option, but noise of corporation value was opposite.
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Li, Dan, Jing’an Cui, and Guohua Song. "Asymptotic Behaviour and Extinction of Delay Lotka-Volterra Model with Jump-Diffusion." Journal of Applied Mathematics 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/249504.

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This paper studies the effect of jump-diffusion random environmental perturbations on the asymptotic behaviour and extinction of Lotka-Volterra population dynamics with delays. The contributions of this paper lie in the following: (a) to consider delay stochastic differential equation with jumps, we introduce a proper initial data space, in which the initial data may be discontinuous function with downward jumps; (b) we show that the delay stochastic differential equation with jumps associated with our model has a unique global positive solution and give sufficient conditions that ensure stochastically ultimate boundedness, moment average boundedness in time, and asymptotic polynomial growth of our model; (c) the sufficient conditions for the extinction of the system are obtained, which generalized the former results and showed that the sufficiently large random jump magnitudes and intensity (average rate of jump events arrival) may lead to extinction of the population.
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Li, Hua, Yu-Hang Chen, and Bin-Ze Tang. "A revised jump-diffusion and rotation-diffusion model." Chinese Physics B 28, no. 5 (May 2019): 056105. http://dx.doi.org/10.1088/1674-1056/28/5/056105.

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Dissertations / Theses on the topic "Jump Diffusion Model"

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Frost, Daniel Allen. "The dual jump diffusion model for security prices." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12509.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.
Vita.
Includes bibliographical references (leaves 225-227).
by Daniel Allen Frost.
Ph.D.
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Berros, Jeremy. "American option pricing in a jump-diffusion model." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0025116.

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Tang, Furui. "Merton Jump-Diffusion Modeling of Stock Price Data." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-78351.

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In this thesis, we investigate two stock price models, the Black-Scholes (BS) model and the Merton Jump-Diffusion (MJD) model. Comparing the logarithmic return of the BS model and the MJD model with empirical stock price data, we conclude that the Merton Jump-Diffusion Model is substantially more suitable for the stock market. This is concluded visually not only by comparing the density functions but also by analyzing mean, variance, skewness and kurtosis of the log-returns. One technical contribution to the thesis is a suggested decision rule for initial guess of a maximum likelihood estimation of the MJD-modeled parameters.
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Nassar, Hiba. "Regularized Calibration of Jump-Diffusion Option Pricing Models." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-9063.

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An important issue in finance is model calibration. The calibration problem is the inverse of the option pricing problem. Calibration is performed on a set of option prices generated from a given exponential L´evy model. By numerical examples, it is shown that the usual formulation of the inverse problem via Non-linear Least Squares is an ill-posed problem. To achieve well-posedness of the problem, some regularization is needed. Therefore a regularization method based on relative entropy is applied.
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Bu, Tianren. "Option pricing under exponential jump diffusion processes." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/option-pricing-under-exponential-jump-diffusion-processes(0dab0630-b8f8-4ee8-8bf0-8cd0b9b9afc0).html.

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The main contribution of this thesis is to derive the properties and present a closed from solution of the exotic options under some specific types of Levy processes, such as American put options, American call options, British put options, British call options and American knock-out put options under either double exponential jump-diffusion processes or one-sided exponential jump-diffusion processes. Compared to the geometric Brownian motion, exponential jump-diffusion processes can better incorporate the asymmetric leptokurtic features and the volatility smile observed from the market. Pricing the option with early exercise feature is the optimal stopping problem to determine the optimal stopping time to maximize the expected options payoff. Due to the Markovian structure of the underlying process, the optimal stopping problem is related to the free-boundary problem consisting of an integral differential equation and suitable boundary conditions. By the local time-space formula for semi-martingales, the closed form solution for the options value can be derived from the free-boundary problem and we characterize the optimal stopping boundary as the unique solution to a nonlinear integral equation arising from the early exercise premium (EEP) representation. Chapter 2 and Chapter 3 discuss American put options and American call options respectively. When pricing options with early exercise feature under the double exponential jump-diffusion processes, a non-local integral term will be found in the infinitesimal generator of the underlying process. By the local time-space formula for semi-martingales, we show that the value function and the optimal stopping boundary are the unique solution pair to the system of two integral equations. The significant contributions of these two chapters are to prove the uniqueness of the value function and the optimal stopping boundary under less restrictive assumptions compared to previous literatures. In the degenerate case with only one-sided jumps, we find that the results are in line with the geometric Brownian motion models, which extends the analytical tractability of the Black-Scholes analysis to alternative models with jumps. In Chapter 4 and Chapter 5, we examine the British payoff mechanism under one-sided exponential jump-diffusion processes, which is the first analysis of British options for process with jumps. We show that the optimal stopping boundaries of British put options with only negative jumps or British call options with only positive jumps can also be characterized as the unique solution to a nonlinear integral equation arising from the early exercise premium representation. Chapter 6 provides the study of American knock-out put options under negative exponential jump-diffusion processes. The conditional memoryless property of the exponential distribution enables us to obtain an analytical form of the arbitrage-free price for American knock-out put options, which is usually more difficult for many other jump-diffusion models.
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Düvelmeyer, Dana. "Some stability results of parameter identification in a jump diffusion model." Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200501234.

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In this paper we discuss the stable solvability of the inverse problem of parameter identification in a jump diffusion model. Therefore we introduce the forward operator of this inverse problem and analyze its properties. We show continuity of the forward operator and stability of the inverse problem provided that the domain is restricted in a specific manner such that techniques of compact sets can be exploited. Furthermore, we show that there is an asymptotical non-injectivity which causes instability problems whenever the jump intensity increases and the jump heights decay simultaneously.
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Starkloff, Hans-Jörg, Dana Düvelmeyer, and Bernd Hofmann. "A note on uniqueness of parameter identification in a jump diffusion model." Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200501325.

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In this note, we consider an inverse problem in a jump diffusion model. Using characteristic functions we prove the injectivity of the forward operator mapping the five parameters determining the model to the density function of the return distribution.
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Chen, Hongqing. "An Empirical Study on the Jump-diffusion Two-beta Asset Pricing Model." PDXScholar, 1996. https://pdxscholar.library.pdx.edu/open_access_etds/1325.

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This dissertation focuses on testing and exploring the usage of the jump-diffusion two-beta asset pricing model. Daily and monthly security returns from both NYSE and AMEX are employed to form various samples for the empirical study. The maximum likelihood estimation is employed to estimate parameters of the jump-diffusion processes. A thorough study on the existence of jump-diffusion processes is carried out with the likelihood ratio test. The probability of existence of the jump process is introduced as an indicator of "switching" between the diffusion process and the jump process. This new empirical method marks a contribution to future studies on the jump-diffusion process. It also makes the jump-diffusion two-beta asset pricing model operational for financial analyses. Hypothesis tests focus on the specifications of the new model as well as the distinction between it and the conventional capital asset pricing model. Both parametric and non-parametric tests are carried out in this study. Comparing with previous models on the risk-return relationship, such as the capital asset pricing model, the arbitrage pricing theory and various multi-factor models, the jump-diffusion two-beta asset pricing model is simple and intuitive. It possesses more explanatory power when the jump process is dominant. This characteristic makes it a better model in explaining the January effect. Extra effort is put in the study of the January Effect due to the importance of the phenomenon. Empirical findings from this study agree with the model in that the systematic risk of an asset is the weighted average of both jump and diffusion betas. It is also found that the systematic risk of the conventional CAPM does not equal the weighted average of jump and diffusion betas.
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Lee, Brendan Chee-Seng Banking &amp Finance Australian School of Business UNSW. "Incorporating discontinuities in value-at-risk via the poisson jump diffusion model and variance gamma model." Awarded by:University of New South Wales, 2007. http://handle.unsw.edu.au/1959.4/37201.

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We utilise several asset pricing models that allow for discontinuities in the returns and volatility time series in order to obtain estimates of Value-at-Risk (VaR). The first class of model that we use mixes a continuous diffusion process with discrete jumps at random points in time (Poisson Jump Diffusion Model). We also apply a purely discontinuous model that does not contain any continuous component at all in the underlying distribution (Variance Gamma Model). These models have been shown to have some success in capturing certain characteristics of return distributions, a few being leptokurtosis and skewness. Calibrating these models onto the returns of an index of Australian stocks (All Ordinaries Index), we then use the resulting parameters to obtain daily estimates of VaR. In order to obtain the VaR estimates for the Poisson Jump Diffusion Model and the Variance Gamma Model, we introduce the use of an innovation from option pricing techniques, which concentrates on the more tractable characteristic functions of the models. Having then obtained a series of VaR estimates, we then apply a variety of criteria to assess how each model performs and also evaluate these models against the traditional approaches to calculating VaR, such as that suggested by J.P. Morgan???s RiskMetrics. Our results show that whilst the Poisson Jump Diffusion model proved the most accurate at the 95% VaR level, neither the Poisson Jump Diffusion or Variance Gamma models were dominant in the other performance criteria examined. Overall, no model was clearly superior according to all the performance criteria analysed, and it seems that the extra computational time required to calibrate the Poisson Jump Diffusion and Variance Gamma models for the purposes of VaR estimation do not provide sufficient reward for the additional effort than that currently employed by Riskmetrics.
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Reducha, Wojciech. "Parameter Estimation of the Pareto-Beta Jump-Diffusion Model in Times of Catastrophe Crisis." Thesis, Högskolan i Halmstad, Tillämpad matematik och fysik (CAMP), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16027.

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Jump diffusion models are being used more and more often in financial applications. Consisting of a Brownian motion (with drift) and a jump component, such models have a number of parameters that have to be set at some level. Maximum Likelihood Estimation (MLE) turns out to be suitable for this task, however it is computationally demanding. For a complicated likelihood function it is seldom possible to find derivatives. The global maximum of a likelihood function defined for a jump diffusion model can however, be obtained by numerical methods. I chose to use the Bound Optimization BY Quadratic Approximation (BOBYQA) method which happened to be effective in this case. However, results of Maximum Likelihood Estimation (MLE) proved to be hard to interpret.
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Books on the topic "Jump Diffusion Model"

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Bentzen, Eric. The international capital asset pricing model with returns that follow poisson jump-diffusion processes. Stockholm: Stockholm University, Institute for International Economic Studies, 1992.

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Calvet, Laurent E. Multifrequency jump-diffusions: An equilibrium approach. Cambridge, Mass: National Bureau of Economic Research, 2006.

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Durham, J. Benson. Jump-diffusion processes and affine term structure models: Additional closed-form approximate solutions, distributional assumptions for jumps, and parameter estimates. Washington, D.C: Federal Reserve Board, 2005.

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Duffie, Darrell. Transform analysis and asset pricing for affine jump-diffusions. Cambridge, MA: National Bureau of Economic Research, 1999.

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Book chapters on the topic "Jump Diffusion Model"

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Wang, Shiu-Huei. "Jump diffusion model." In Encyclopedia of Finance, 676–88. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/978-0-387-26336-6_69.

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Wang, Shin-Huei. "Jump Diffusion Model." In Encyclopedia of Finance, 1073–91. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91231-4_44.

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Wang, Shin-Huei. "Jump Diffusion Model." In Encyclopedia of Finance, 525–34. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-5360-4_44.

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Wang, Mingming, and Allanus Tsoi. "CPPI in the Jump-Diffusion Model." In State-Space Models, 247–76. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7789-1_12.

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Krutchenko, R. N., and A. V. Melnikov. "Quantile hedging for a jump-diffusion finanaicl market model." In Mathematical Finance, 215–29. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8291-0_20.

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Karimov, Azar. "Stock Prices Follow a Double Exponential Jump-Diffusion Model." In Contributions to Management Science, 37–71. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65009-8_5.

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Steinrücke, Lea, Rudi Zagst, and Anatoliy Swishchuk. "The LIBOR Market Model: A Markov-Switching Jump Diffusion Extension." In Hidden Markov Models in Finance, 85–116. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7442-6_4.

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Gormin, Anatoly, and Yuri Kashtanov. "Options Pricing for Several Maturities in a Jump-Diffusion Model." In Monte Carlo and Quasi-Monte Carlo Methods 2010, 385–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27440-4_20.

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Reynoso, Bor, F. Baltazar-Larios, and Laura Eslava. "Maximum Likelihood Estimation for a Markov-Modulated Jump-Diffusion Model." In Interdisciplinary Statistics in Mexico, 177–92. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12778-6_11.

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Siu, Tak Kuen. "A Hidden Markov-Modulated Jump Diffusion Model for European Option Pricing." In Hidden Markov Models in Finance, 185–209. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-1-4899-7442-6_8.

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Conference papers on the topic "Jump Diffusion Model"

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Guo, Meihui, Yu-Chun Chang, and Shih-Feng Huang. "Pricing American Options in a Jump Diffusion Model." In 2011 IEEE 14th International Conference on Computational Science and Engineering (CSE). IEEE, 2011. http://dx.doi.org/10.1109/cse.2011.48.

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Li, Xuying, and Xianbin Gu. "A Jump-Diffusion Model of Shipping Freight Rate." In 2009 Second International Conference on Future Information Technology and Management Engineering (FITME). IEEE, 2009. http://dx.doi.org/10.1109/fitme.2009.83.

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Xue, Hong, Jun Li, Shan Yang, and Xiao-rui Wu. "The Bond Pricing Model on Fractional Jump-Diffusion Process." In 2011 International Conference on Computer and Management (CAMAN). IEEE, 2011. http://dx.doi.org/10.1109/caman.2011.5778794.

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Gu, Cong, Shenghong Li, and Bo Zhou. "Ruin Probabilities for Markov-Modulated Jump-Diffusion Risk Model." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5303625.

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Chen, Xianzhe, and Jun Zhang. "Supply chain risks analysis by using jump-diffusion model." In 2008 Winter Simulation Conference (WSC). IEEE, 2008. http://dx.doi.org/10.1109/wsc.2008.4736124.

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Wang, Ziyi, Grady Williams, and Evangelos A. Theodorou. "Information Theoretic Model Predictive Control on Jump Diffusion Processes." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8815263.

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Guoqing Yan and F. B. Hanson. "Option pricing for a stochastic-volatility jump-diffusion model with log-uniform jump-amplitudes." In 2006 American Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/acc.2006.1657175.

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Kliber, Paweł. "PORTFOLIO ANALYSIS IN JUMP-DIFFUSION MODEL WITH POWER-LAW TAILS." In 26th and the 27th International Academic Conference (Istanbul, Prague). International Institute of Social and Economic Sciences, 2016. http://dx.doi.org/10.20472/iac.2016.027.023.

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Yang, Yunfeng, Huihui Bai, and Yinchun Zheng. "Optimal Consumption and Investment Strategies in a Jump-Diffusion Model." In 2018 14th International Conference on Computational Intelligence and Security (CIS). IEEE, 2018. http://dx.doi.org/10.1109/cis2018.2018.00110.

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Wang La-sheng. "The stability of exchange rate model with diffusion and Poisson jump." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5689663.

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Reports on the topic "Jump Diffusion Model"

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Chen, Hongqing. An Empirical Study on the Jump-diffusion Two-beta Asset Pricing Model. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1324.

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