Bibliografie tematiche / Affine Jump Diffusion

Letteratura scientifica selezionata sul tema "Affine Jump Diffusion"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 30 gennaio 2023

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Articoli di riviste sul tema "Affine Jump Diffusion"

1

Glasserman, Paul, e Kyoung-Kuk Kim. "Saddlepoint approximations for affine jump-diffusion models". Journal of Economic Dynamics and Control 33, n. 1 (gennaio 2009): 15–36. http://dx.doi.org/10.1016/j.jedc.2008.04.007.

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2

Li, Lingfei, Rafael Mendoza-Arriaga e Daniel Mitchell. "Analytical representations for the basic affine jump diffusion". Operations Research Letters 44, n. 1 (gennaio 2016): 121–28. http://dx.doi.org/10.1016/j.orl.2015.12.003.

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3

Filipović, Damir, Eberhard Mayerhofer e Paul Schneider. "Density approximations for multivariate affine jump-diffusion processes". Journal of Econometrics 176, n. 2 (ottobre 2013): 93–111. http://dx.doi.org/10.1016/j.jeconom.2012.12.003.

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4

Chung, Tsz Kin, e Yue Kuen Kwok. "Equity-credit modeling under affine jump-diffusion models with jump-to-default". Journal of Financial Engineering 01, n. 02 (giugno 2014): 1450017. http://dx.doi.org/10.1142/s2345768614500172.

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Abstract (sommario):
This paper considers the stochastic models for pricing credit-sensitive financial derivatives using the joint equity-credit modeling approach. The modeling of credit risk is embedded into a stochastic asset dynamics model by adding the jump-to-default (JtD) feature. We discuss the class of stochastic affine jump-diffusion (AJD) models with JtD and apply the models to price defaultable European options and credit default swaps. Numerical studies of the equity-credit models are also considered. The impact on the pricing behavior of derivative products with the added JtD feature is examined.
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5

Gapeev, Pavel V., e Yavor I. Stoev. "On the construction of non-affine jump-diffusion models". Stochastic Analysis and Applications 35, n. 5 (30 giugno 2017): 900–918. http://dx.doi.org/10.1080/07362994.2017.1333008.

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6

Da Fonseca, José, e Katja Ignatieva. "Jump activity analysis for affine jump-diffusion models: Evidence from the commodity market". Journal of Banking & Finance 99 (febbraio 2019): 45–62. http://dx.doi.org/10.1016/j.jbankfin.2018.11.014.

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7

FRAME, SAMUEL J., e CYRUS A. RAMEZANI. "BAYESIAN ESTIMATION OF ASYMMETRIC JUMP-DIFFUSION PROCESSES". Annals of Financial Economics 09, n. 03 (dicembre 2014): 1450008. http://dx.doi.org/10.1142/s2010495214500080.

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Abstract (sommario):
The hypothesis that asset returns are normally distributed has been widely rejected. The literature has shown that empirical asset returns are highly skewed and leptokurtic. The affine jump-diffusion (AJD) model improves upon the normal specification by adding a jump component to the price process. Two important extensions proposed by Ramezani and Zeng (1998) and Kou (2002) further improve the AJD specification by having two jump components in the price process, resulting in the asymmetric affine jump-diffusion (AAJD) specification. The AAJD specification allows the probability distribution of the returns to be asymmetrical. That is, the tails of the distribution are allowed to have different shapes and densities. The empirical literature on the "leverage effect" shows that the impact of innovations in prices on volatility is asymmetric: declines in stock prices are accompanied by larger increases in volatility than the reverse. The asymmetry in AAJD specification indirectly accounts for the leverage effect and is therefore more consistent with the empirical distributions of asset returns. As a result, the AAJD specification has been widely adopted in the portfolio choice, option pricing, and other branches of the literature. However, because of their complexity, empirical estimation of the AAJD models has received little attention to date. The primary objective of this paper is to contribute to the econometric methods for estimating the parameters of the AAJD models. Specifically, we develop a Bayesian estimation technique. We provide a comparison of the estimated parameters under the Bayesian and maximum likelihood estimation (MLE) methodologies using the S&P 500, the NASDAQ, and selected individual stocks. Focusing on the most recent spectacular market bust (2007–2009) and boom (2009–2010) periods, we examine how the parameter estimates differ under distinctly different economic conditions.
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8

Ignatieva, Katja, e Patrick Wong. "Modelling high frequency crude oil dynamics using affine and non-affine jump–diffusion models". Energy Economics 108 (aprile 2022): 105873. http://dx.doi.org/10.1016/j.eneco.2022.105873.

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9

Nunes, João Pedro Vidal, e Tiago Ramalho Viegas Alcaria. "Valuation of forward start options under affine jump-diffusion models". Quantitative Finance 16, n. 5 (31 luglio 2015): 727–47. http://dx.doi.org/10.1080/14697688.2015.1049200.

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10

Yun, Jaeho. "Out-of-sample density forecasts with affine jump diffusion models". Journal of Banking & Finance 47 (ottobre 2014): 74–87. http://dx.doi.org/10.1016/j.jbankfin.2014.06.024.

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Più fonti

Tesi sul tema "Affine Jump Diffusion"

1

Lahiri, Joydeep. "Affine jump diffusion models for the pricing of credit default swaps". Thesis, University of Reading, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529979.

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Zhang, Xiang. "Essays on empirical performance of affine jump-diffusion option pricing models". Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.552834.

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Abstract (sommario):
This thesis examines the empirical performance of option pricing models in the continuous- time affine jump-diffusion (AID) class. In models of this class, the underlying returns are governed by stochastic volatility diffusions and/or jumps and the dynamics of the whole system has affine dependence on the state variables. The thesis consists of three essays. The first essay calibrates a wide range of AID option pricing models to S&P 500 index options. The aim is to empirically identify how best to structure two types of risk components- stochastic volatility and jumps - within the framework of multi-factor AID specifications. Our specification analysis shows that the specifications with more-than-two diffusions perform well and that a three-factor specification should be preferred, in which jump intensities are allowed to depend on an independent diffusion process. Having identified the well-performing pricing model specifications, the second essay examines how such a model can be used to forecast realized volatility using only option prices as an input. To do so, the dynamics of volatility implied by the model are used to construct a forecasting equation in which the spot volatilities extracted from observed option prices act as the key predictors. The analysis indicates that the option-based multi-factor forecasting model outperforms other popular models in forecasting realized volatility of S&P 500 Index returns over most of the short-term horizons considered. The final essay investigates if a two-factor AJD model can fit option pricing patterns generated by a single-factor long memory volatility model. Our simulation experiments show that this model does well in this respect. Remarkably, however, at the fitted parameter values it does not generate the volatility auto-correlation patterns that are characteristic of long-memory volatility models.
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Bambe, Moutsinga Claude Rodrigue. "Transform analysis of affine jump diffusion processes with applications to asset pricing". Diss., Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-06112008-162807.

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4

Gleeson, Cameron Banking &amp Finance Australian School of Business UNSW. "Pricing and hedging S&P 500 index options : a comparison of affine jump diffusion models". Awarded by:University of New South Wales. School of Banking and Finance, 2005. http://handle.unsw.edu.au/1959.4/22379.

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Abstract (sommario):
This thesis examines the empirical performance of four Affine Jump Diffusion models in pricing and hedging S&P 500 Index options: the Black Scholes (BS) model, Heston???s Stochastic Volatility (SV) model, a Stochastic Volatility Price Jump (SVJ) model and a Stochastic Volatility Price-Volatility Jump (SVJJ) model. The SVJJ model structure allows for simultaneous jumps in price and volatility processes, with correlated jump size distributions. To the best of our knowledge this is the first empirical study to test the hedging performance of the SVJJ model. As part of our research we derive the SVJJ model minimum variance hedge ratio. We find the SVJ model displays the best price prediction. The SV model lacks the structural complexity to eliminate Black Scholes pricing biases, whereas our results indicate the SVJJ model suffers from overfitting. Despite significant evidence from in and out-of-sample pricing that the SV and SVJ models were better specified than the BS model, this did not result in an improvement in dynamic hedging performance. Overall the BS delta hedge and SV minimum variance hedge produced the lowest errors, although their performance across moneyness-maturity categories differed greatly. The SVJ model???s results were surprisingly poor given its superior performance in out-of-sample pricing. We attribute the inadequate performance of the jump models to the lower hedging ratios these models provided, which may be a result of the negative expected jump sizes.
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5

Ezzine, Ahmed. "Some topics in mathematical finance. Non-affine stochastic volatility jump diffusion models. Stochastic interest rate VaR models". Doctoral thesis, Universite Libre de Bruxelles, 2004. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211156.

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6

Yuksel, Ayhan. "Credit Risk Modeling With Stochastic Volatility, Jumps And Stochastic Interest Rates". Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12609206/index.pdf.

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Abstract (sommario):
This thesis presents the modeling of credit risk by using structural approach. Three fundamental questions of credit risk literature are analyzed throughout the research: modeling single firm credit risk, modeling portfolio credit risk and credit risk pricing. First we analyze these questions under the assumptions that firm value follows a geometric Brownian motion and the interest rates are constant. We discuss the weaknesses of the geometric brownian motion assumption in explaining empirical properties of real data. Then we propose a new extended model in which asset value, volatility and interest rates follow affine jump diffusion processes. In our extended model volatility is stochastic, asset value and volatility has correlated jumps and interest rates are stochastic and have jumps. Finally, we analyze the modeling of single firm credit risk and credit risk pricing by using our extended model and show how our model can be used as a solution for the problems we encounter with simple models.
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7

McClelland, Andrew James. "Self excitation in equity indices". Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/63629/1/Andrew_McClelland_Thesis.pdf.

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A "self-exciting" market is one in which the probability of observing a crash increases in response to the occurrence of a crash. It essentially describes cases where the initial crash serves to weaken the system to some extent, making subsequent crashes more likely. This thesis investigates if equity markets possess this property. A self-exciting extension of the well-known jump-based Bates (1996) model is used as the workhorse model for this thesis, and a particle-filtering algorithm is used to facilitate estimation by means of maximum likelihood. The estimation method is developed so that option prices are easily included in the dataset, leading to higher quality estimates. Equilibrium arguments are used to price the risks associated with the time-varying crash probability, and in turn to motivate a risk-neutral system for use in option pricing. The option pricing function for the model is obtained via the application of widely-used Fourier techniques. An application to S&P500 index returns and a panel of S&P500 index option prices reveals evidence of self excitation.
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8

Cetinkaya, Sirzat. "Valuation Of Life Insurance Contracts Using Stochastic Mortality Rate And Risk Process Modeling". Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608214/index.pdf.

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Abstract (sommario):
In life insurance contracts, actuaries generally value premiums using deterministic mortality rates and interest rates. They have ignored them stochastically in most of the studies. However it is known that neither interest rates nor mortality rates are constant. It is also known that companies may encounter insolvency problems such as ruin, so the ruin probability need to be added to the valuation of the life insurance contracts process. Insurance companies should model their surplus processes to price some types of life insurance contracts and to see risk position. In this study, mortality rates and surplus processes are modeled and financial strength of companies are utilized when pricing life insurance contracts.
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9

Xu, Li. "Financial and computational models in electricity markets". Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/51849.

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This dissertation is dedicated to study the design and utilization of financial contracts and pricing mechanisms for managing the demand/price risks in electricity markets and the price risks in carbon emission markets from different perspectives. We address the issues pertaining to the efficient computational algorithms for pricing complex financial options which include many structured energy financial contracts and the design of economic mechanisms for managing the risks associated with increasing penetration of renewable energy resources and with trading emission allowance permits in the restructured electric power industry. To address the computational challenges arising from pricing exotic energy derivatives designed for various hedging purposes in electricity markets, we develop a generic computational framework based on a fast transform method, which attains asymptotically optimal computational complexity and exponential convergence. For the purpose of absorbing the variability and uncertainties of renewable energy resources in a smart grid, we propose an incentive-based contract design for thermostatically controlled loads (TCLs) to encourage end users' participation as a source of DR. Finally, we propose a market-based approach to mitigate the emission permit price risks faced by generation companies in a cap-and-trade system. Through a stylized economic model, we illustrate that the trading of properly designed financial options on emission permits reduces permit price volatility and the total emission reduction cost.
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10

Krebs, Daniel. "Pricing a basket option when volatility is capped using affinejump-diffusion models". Thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123395.

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This thesis considers the price and characteristics of an exotic option called the Volatility-Cap-Target-Level(VCTL) option. The payoff function is a simple European option style but the underlying value is a dynamic portfolio which is comprised of two components: A risky asset and a non-risky asset. The non-risky asset is a bond and the risky asset can be a fund or an index related to any asset category such as equities, commodities, real estate, etc. The main purpose of using a dynamic portfolio is to keep the realized volatility of the portfolio under control and preferably below a certain maximum level, denoted as the Volatility-Cap-Target-Level (VCTL). This is attained by a variable allocation between the risky asset and the non-risky asset during the maturity of the VCTL-option. The allocation is reviewed and if necessary adjusted every 15th day. Adjustment depends entirely upon the realized historical volatility of the risky asset. Moreover, it is assumed that the risky asset is governed by a certain group of stochastic differential equations called affine jump-diffusion models. All models will be calibrated using out-of-the money European call options based on the Deutsche-Aktien-Index(DAX). The numerical implementation of the portfolio diffusions and the use of Monte Carlo methods will result in different VCTL-option prices. Thus, to price a nonstandard product and to comply with good risk management, it is advocated that the financial institution use several research models such as the SVSJ- and the Seppmodel in addition to the Black-Scholes model. Keywords: Exotic option, basket option, risk management, greeks, affine jumpdiffusions, the Black-Scholes model, the Heston model, Bates model with lognormal jumps, the Bates model with log-asymmetric double exponential jumps, the Stochastic-Volatility-Simultaneous-Jumps(SVSJ)-model, the Sepp-model.
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Libri sul tema "Affine Jump Diffusion"

1

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

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2

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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Capitoli di libri sul tema "Affine Jump Diffusion"

1

Regis, Luca, e Petar Jevtić. "Stochastic Mortality Models and Pandemic Shocks". In Springer Actuarial, 61–74. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78334-1_4.

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AbstractAfter decades of worldwide steady improvements in life expectancy, the COVID-19 pandemic produced a shock that had an extraordinary immediate impact on mortality rates globally. This shock had largely heterogeneous effects across cohorts, socio-economic groups, and nations. It represents a remarkable departure from the secular trends that most of the mortality models have been constructed to capture. Thus, this chapter aims to review the existing literature on stochastic mortality, discussing the features that these models should have in order to be able to incorporate the behaviour of mortality rates following shocks such as the one produced by the COVID-19 pandemic. Multi-population models are needed to describe the heterogeneous impact of pandemic shocks across cohorts of individuals. However, very few of them so far have included jumps. We contribute to the literature by describing a general framework for multi-population models with jumps in continuous-time, using affine jump-diffusive processes.
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2

"Affine Jump-Diffusion Processes". In Financial Derivative and Energy Market Valuation, 605–44. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118501788.ch18.

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Atti di convegni sul tema "Affine Jump Diffusion"

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Miguel Bravo, Jorge. "Pricing Survivor Bonds with Affine-Jump Diffusion Stochastic Mortality Models". In ICEEG '21: 2021 The 5th International Conference on E-Commerce, E-Business and E-Government. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3466029.3466037.

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2

Shi, Guoqing, Chuanzhe Liu e Yuhua Hou. "Study on the pricing of credit default swap with affine jump-diffusions processes". In 2006 6th International Conference on Intelligent Systems Design and Applications. IEEE, 2006. http://dx.doi.org/10.1109/isda.2006.251.

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Rapporti di organizzazioni sul tema "Affine Jump Diffusion"

1

Duffie, Darrell, Jun Pan e Kenneth Singleton. Transform Analysis and Asset Pricing for Affine Jump-Diffusions. Cambridge, MA: National Bureau of Economic Research, aprile 1999. http://dx.doi.org/10.3386/w7105.

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