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Books on the topic 'Continuous-time stochastic models'

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Author: Grafiati

Published: 10 March 2023

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1

Melino, Angelo. Estimation of continuous-time models in finance. Toronto: Dept. of Economics and Institute for Policy Analysis, University of Toronto, 1991.

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2

Jianfeng, Zhang, and SpringerLink (Online service), eds. Contract Theory in Continuous-Time Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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3

Optimal portfolios: Stochastic models for optimal investment and risk management in continuous time. Singapore: World Scientific, 1997.

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4

Antonio, Mele, ed. Stochastic volatility in financial markets: Crossing the bridge to continuous time. Boston, Mass: Kluwer Academic Publishers, 2000.

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5

Capasso, V. An introduction to continuous-time stochastic processes: Theory, models, and applications to finance, biology, and medicine. Boston: Birkhäuser, 2005.

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6

Fornari, Fabio. A simple approach to the estimation of continuous time CEV stochastic volatility models of the short-term rate. [Roma]: Banca d'Italia, 2001.

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7

David, Bakstein, and SpringerLink (Online service), eds. An Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine. 2nd ed. Boston: Birkhäuser Boston, 2012.

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8

Bergstrom, A. R. Gaussian estimation of mixed order continuous time dynamic models with unobservable stochastic trends from mixed stock and flow data. [Colchester]: University of Essex, Department of Economics, 1995.

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9

Vladas, Sidoravicius, and Smirnov S. (Stanislav) 1970-, eds. Probability and statistical physics in St. Petersburg: St. Petersburg School in Probability and Statistical Physics : June 18-29, 2012 : St. Petersburg State University, St. Petersburg, Russia. Providence, Rhode Island: American Mathematical Society, 2015.

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10

Zhang, Jianfeng, and Jakša Cvitanic. Contract Theory in Continuous-Time Models. Springer, 2014.

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11

Shreve, Steven. Stochastic Calculus for Finance Ii: Continuous-Time Models. Springer, 2010.

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12

Vassiliou, P.-C. G. Continuous-Time Asset Pricing Models in Applied Stochastic Finance. Wiley & Sons, Incorporated, John, 2014.

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13

Vassiliou, P. C. G. Continuous-Time Asset Pricing Models in Applied Stochastic Finance. Wiley & Sons, Incorporated, John, 2014.

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14

Vassiliou, P. C. G. Continuous-Time Asset Pricing Models in Applied Stochastic Finance. Wiley & Sons, Incorporated, John, 2014.

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15

Vassiliou, P. C. G. Continuous-Time Asset Pricing Models in Applied Stochastic Finance. Wiley & Sons, Incorporated, John, 2014.

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16

Hernandez-Lerma, Onesimo, and Xianping Guo. Continuous-Time Markov Decision Processes: Theory and Applications. Springer, 2010.

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17

Hernández-Lerma, Onésimo, and Xianping Guo. Continuous-Time Markov Decision Processes: Theory and Applications. Springer, 2012.

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18

Bergstrom, Albert Rex, and Khalid Ben Nowman. Continuous Time Econometric Model of the United Kingdom with Stochastic Trends. Cambridge University Press, 2010.

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19

Bergstrom, Albert Rex, and Khalid Ben Nowman. Continuous Time Econometric Model of the United Kingdom with Stochastic Trends. University of Cambridge ESOL Examinations, 2012.

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20

Bergstrom, Albert Rex, and Khalid Ben Nowman. Continuous Time Econometric Model of the United Kingdom with Stochastic Trends. Cambridge University Press, 2011.

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21

Bergstrom, Albert Rex, and Khalid Ben Nowman. A Continuous Time Econometric Model of the United Kingdom with Stochastic Trends. Cambridge University Press, 2007.

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22

Capasso, Vincenzo, and David Bakstein. An Introduction to Continuous-Time Stochastic Processes Theory, Models, and Applications to Finance, Biology, and Medicine. Birkhäuser Boston, 2004.

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23

(Translator), Anna Kennedy, ed. Financial Markets in Continuous Time. Springer, 2003.

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24

Financial Markets in Continuous Time. Springer London, Limited, 2010.

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25

Capasso, Vincenzo, and David Bakstein. Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine. Springer International Publishing AG, 2022.

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26

Fornari, Fabio, and Antonio Mele. Stochastic Volatility in Financial Markets: Crossing the Bridge to Continuous Time. Springer London, Limited, 2012.

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27

Capasso, Vincenzo, and David Bakstein. An Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine. Birkhäuser, 2016.

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28

Capasso, Vincenzo, and David Bakstein. An Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine. Birkhäuser, 2015.

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29

An Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine. Birkhäuser, 2012.

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30

Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine. Birkhauser Verlag, 2008.

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31

Capasso, Vincenzo, and David Bakstein. Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine. Springer International Publishing AG, 2021.

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32

Capasso, Vincenzo, and David Bakstein. Introduction to Continuous-Time Stochastic Processes: Theory, Models, and Applications to Finance, Biology, and Medicine. Birkhauser Verlag, 2015.

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33

Björk, Tomas. Arbitrage Theory in Continuous Time. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198851615.001.0001.

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The fourth edition of this textbook on pricing and hedging of financial derivatives, now also including dynamic equilibrium theory, continues to combine sound mathematical principles with economic applications. Concentrating on the probabilistic theory of continuous time arbitrage pricing of financial derivatives, including stochastic optimal control theory and optimal stopping theory, the book is designed for graduate students in economics and mathematics, and combines the necessary mathematical background with a solid economic focus. It includes a solved example for every new technique presented, contains numerous exercises, and suggests further reading in each chapter. All concepts and ideas are discussed, not only from a mathematics point of view, but the mathematical theory is also always supplemented with lots of intuitive economic arguments. In the substantially extended fourth edition Tomas Björk has added completely new chapters on incomplete markets, treating such topics as the Esscher transform, the minimal martingale measure, f-divergences, optimal investment theory for incomplete markets, and good deal bounds. There is also an entirely new part of the book presenting dynamic equilibrium theory. This includes several chapters on unit net supply endowments models, and the Cox–Ingersoll–Ross equilibrium factor model (including the CIR equilibrium interest rate model). Providing two full treatments of arbitrage theory—the classical delta hedging approach and the modern martingale approach—the book is written in such a way that these approaches can be studied independently of each other, thus providing the less mathematically oriented reader with a self-contained introduction to arbitrage theory and equilibrium theory, while at the same time allowing the more advanced student to see the full theory in action.

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34

Hernandez-Lerma, Onesimo, and Xianping Guo. Continuous-Time Markov Decision Processes: Theory and Applications (Stochastic Modelling and Applied Probability Book 62). Springer, 2009.

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35

Fornari, Fabio, and Antonio Mele. Stochastic Volatility in Financial Markets: Crossing the Bridge to Continuous Time (Dynamic Modeling and Econometrics in Economics and Finance). Springer, 2000.

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36

Schehr, Grégory, Alexander Altland, Yan V. Fyodorov, Neil O'Connell, and Leticia F. Cugliandolo, eds. Stochastic Processes and Random Matrices. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198797319.001.0001.

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The field of stochastic processes and random matrix theory (RMT) has been a rapidly evolving subject during the past fifteen years where the continuous development and discovery of new tools, connections, and ideas have led to an avalanche of new results. These breakthroughs have been made possible thanks, to a large extent, to the recent development of various new techniques in RMT. Matrix models have been playing an important role in theoretical physics for a long time and they are currently also a very active domain of research in mathematics. An emblematic example of these recent advances concerns the theory of growth phenomena in the Kardar–Parisi–Zhang (KPZ) universality class where the joint efforts of physicists and mathematicians during the past twenty years have unveiled the beautiful connections between this fundamental problem of statistical mechanics and the theory of random matrices, namely the fluctuations of the largest eigenvalue of certain ensemble of random matrices. These chapters not only cover this topic in detail but also present more recent developments that have emerged from these discoveries, for instance in the context of low-dimensional heat transport (on the physics side) or in the context of integrable probability (on the mathematical side).

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37

Back, Kerry E. Asset Pricing and Portfolio Choice Theory. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190241148.001.0001.

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This book is intended as a textbook for asset pricing theory courses at the Ph.D. or Masters in Quantitative Finance level and as a reference for financial researchers. The first two parts of the book explain portfolio choice and asset pricing theory in single‐period, discrete‐time, and continuous‐time models. For valuation, the focus throughout is on stochastic discount factors and their properties. Traditional factor models, including the CAPM, are related to or derived from stochastic discount factors. A chapter on stochastic calculus provides the needed tools for analyzing continuous‐time models. A chapter on “ex‐plaining puzzles” and the last two parts of the book provide introductions to a number of current topics in asset pricing research, including rare disasters, long‐run risks, external and internal habits, real options, corporate financing options, asymmetric and incomplete information, heterogeneous beliefs, and non‐expected‐utility preferences. Each chapter includes a “Notes and References” section and exercises for students.

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38

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Graphs on structured spaces. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0010.

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This chapter moves beyond viewing nodes as homogeneous dots set on a plane. To introduce more complicated underlying space, multiplex networks (which are defined with layers of interaction on the same underlying node set) and temporal (time-dependent) networks are discussed. It shown that despite the much more complicated underlying space, many of the techniques developed in earlier chapters can be applied. Heterogeneous nodes are introduced as an extension of the stochastic block model for community structure, then extended using methods developed in earlier chapters to more general (continuous) node attributes such as fitness. The chapter closes with a discussion of the intersections and similarities between the many alternative models for capturing topological features that have been presented in the book.

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39

Strand, Jon. Implications of a Lowered Damage Trajectory for Mitigation in a Continuous-Time Stochastic Model. The World Bank, 2011. http://dx.doi.org/10.1596/1813-9450-5724.

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