Abstract
Using the introduction to Wiener processes, the Itô integral is defined, and all its main properties are proven. Then the stochastic differential is introduced, and Itô’s formula is proven. Major results from the Itô calculus, including the fundamental martingale representation theorem, are presented. Finally, an introduction to the Itô-Lévy calculus with respect to Lévy processes is introduced up to a generalization of Itô’s formula.
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References
Aalen O., Nonparametric inference for a family of counting processes, Annals of Statistics, 701–726, 6; 1978.
Aletti G. and Capasso V., Profitability in a multiple strategy market, Decis. Econ. Finance, 145–152, 26; 2003.
Andersen P. K., Borgan Ø., Gill R.D., and Keiding N., Statistical Models Based on Counting Processes, Springer, Heidelberg; 1993.
Anderson W. J., Continuous-Time Markov Chains: An Application-Oriented Approach, Springer, New York; 1991.
Applebaum D., Levy Processes and Stochastic Calculus, Cambridge University Press, Cambridge; 2004.
Arnold L., Stochastic Differential Equations: Theory and Applications, Wiley, New York; 1974.
Ash R. B., Real Analysis and Probability, Academic, London; 1972.
Ash R. B. and Gardner M. F., Topics in Stochastic Processes, Academic, London; 1975.
Aubin J.-P., Applied Abstract Analysis, Wiley, New York; 1977.
Bachelier L., Théorie de la spéculation, Ann. Sci. École Norm. Sup., 21–86, 17; 1900.
Bailey N. T. J., The Mathematical Theory of Infectious Diseases, Griffin, London; 1975.
Baldi P., Equazioni differenziali stocastiche, UMI, Bologna; 1984.
Bartholomew D. J., Continuous time diffusion models with random duration of interest, J. Math. Sociol., 187–199, 4; 1976.
Bauer H., Probability Theory and Elements of Measure Theory, Academic, London; 1981.
Becker N., Analysis of Infectious Disease Data, Chapman & Hall, London; 1989.
Belleni-Morante A. and McBride A. C., Applied Nonlinear Semigroups, Wiley, Chichester; 1998.
Bertoin J., Lévy Processes, Cambridge University Press, Cambridge, UK; 1996.
Bhattacharya R. N. and Waymire E. C., Stochastic Processes with Applications, Wiley, New York; 1990.
Bianchi A., Capasso V., and Morale D., Estimation and prediction of a nonlinear model for price herding in Complex Models and Intensive Computational Methods for Estimation and Prediction (C. Provasi, Ed.), CLUEP, Padova, 2005, pp. 365–370.
Billingsley P., Convergence of Probability Measures, Wiley, New York; 1968.
Billingsley P., Probability and Measure, Wiley, New York; 1986.
Black F. and Scholes M., The pricing of options and corporate liabilities, J. Pol. Econ., 637–654, 81; 1973.
Bohr H., Almost Periodic Functions, Chelsea, New York; 1947.
Borodin A. and Salminen P., Handbook of Brownian Motion: Facts and Formulae, Birkhä user, Boston; 1996.
Boyle P. and Tian Y., Pricing lookback and barrier options under the CEV process, J. Fin. Quant. Anal., 241–264, 34; 1999.
Brace A., Gatarek D., & Musiela M., The market model of interest rate dynamics, Math. Fin., 127–154, 7; 1997.
Breiman L., Probability, Addison-Wesley, Reading, MA; 1968.
Bremaud P., Point Processes and Queues: Martingale Dynamics, Springer, Heidelberg; 1981.
Burger M., Capasso V. and Morale D., On an aggregation model with long and short range interaction, Nonlinear Anal. Real World Appl., 939–958, 3; 2007.
Cai G. Q. and Lin Y. K., Stochastic analysis of the Lotka-Volterra model for ecosystems, Phys. Rev. E, 041910, 70; 2004.
Capasso V., A counting process approach for stochastic age-dependent population dynamics, in Biomathematics and Related Computational Problems (L. M. Ricciardi, Ed.), pp. 255–269, Kluwer, Dordrecht; 1988.
Capasso V., A counting process approach for age-dependent epidemic systems, in Stochastic Processes in Epidemic Theory (J. P. Gabriel et al., Eds.) pp. 118–128, Lecture Notes in Biomathematics, Vol. 86, Springer, Heidelberg; 1990.
Capasso V., Mathematical Structures of Epidemic Systems, Springer, Heidelberg; 1993. 2nd corrected printing; 2008.
Capasso V., Di Liddo A., and Maddalena L., Asymptotic behaviour of a nonlinear model for the geographical diffusion of innovations, Dynamic Systems and Applications, 207–220, 3; 1994.
Capasso V. and Morale D., Asymptotic behavior of a system of stochastic particles subject to nonlocal interactions. Stoch. Anal. Appl. 574–603, 27; 2009.
Capasso V., Morale D., and Sioli F., An agent-based model for “price herding”, applied to the automobile market, MIRIAM reports, Milan; 2003.
Carrillo J., Entropy solutions for nonlinear degenerate problems, Arch. Rat. Mech. Anal., 269–361, 147; 1999.
Carrillo J. A., McCann R. J. and Villani C., Kinetic equilibration rates for granular nedia and related equations: entropy dissipation and mass transportation estimates, Rev. Mat. Iberoam., 971–1018, 19; 2003.
Champagnat N., Ferriére R., and Méléard S. Unifying evolutionary dynamics: From individula stochastic processes to macroscopic models, Theor. Pop. Biol., 297–321, 69; 2006.
Chan K. C., Karolyi G.A., Longstaff F.A. and Sanders A. B., An empirical comparison of alternative models of the short-term interest rate, J. Fin., 1209–1227, 47; 1992.
Chiang C.L., Introduction to Stochastic Processes in Biostatistics, Wiley, New York; 1968.
Chow Y. S. and Teicher H., Probability Theory: Independence, Interchangeability, Martingales, Springer, New York; 1988.
Chung K. L., A Course in Probability Theory, Second Edition, Academic, New York; 1974.
Cox J. C., The constant elasticity of variance option pricing model, J. Portfolio Manage., 15–17, 22; 1996.
Cox J. C., Ross S. A. and Rubinstein M., Option pricing: A simplified approach, J. Fin. Econ., 229–263, 7; 1979.
Çynlar E., Introduction to Stochastic Processes, Prentice Hall, Englewood Cliffs, NJ; 1975.
Dalang R. C., Morton A. and Willinger W., Equivalent martingale measures and non-arbitrage in stochastic securities market models, Stochast. Stochast. Rep., 185–201, 29; 1990.
Daley D. and Vere-Jones D., An Introduction to the Theory of Point Processes, Springer, Berlin; 1988.
Daley D. and Vere-Jones D., An Introduction to the Theory of Point Processes. Volume II: General Theory and Structure, Springer, Heidelberg; 2008.
Darling D. A. D. and Siegert A. J. F., The first passage time problem for a continuum Markov process, Ann. Math. Stat., 624–639, 24; 1953.
Davis M. H. A., Piecewise-deterministic Markov processes: A general class of non-diffusion stochastic models, J. R. Stat. Soc. Ser. B, 353–388, 46; 1984.
Delbaen F. and Schachermeyer W., A general version of the fundamental theorem of asset pricing, Math. Annal., 463–520, 300; 1994.
De Masi A. and Presutti E., Mathematical Methods for Hydrodynamical Limits, Springer, Heidelberg; 1991.
Dieudonné J., Foundations of Modern Analysis, Academic, New York; 1960.
Di Nunno G., Øksendal B. and Proske F., Malliavin Calculus for Levy Processes with Applications to Finance, Springer, Berlin Heidelberg; 2009.
Donsker M. D. and Varadhan S. R. S., Large deviations from a hydrodynamical scaling limit, Comm. Pure Appl. Math., 243–270, 42; 1989.
Doob J. L., Stochastic Processes, Wiley, New York; 1953.
Dudley R. M., Real Analysis and Probability, Cambridge University Press, Cambridge; 2005.
Duffie D., Dynamic Asset Pricing Theory, Princeton University Press, Princeton, NJ; 1996.
Dupire B., Pricing with a smile, RISK, 18–20, 1; 1994.
Durrett R. and Levin S. A., The importance of being discrete (and spatial), Theor. Pop. Biol., 363–394, 46; 1994.
Dynkin E. B., Markov Processes, Springer, Berlin, Vols. 1–2; 1965.
Einstein A., Über die von der molekularkinetischen Theorie der Wärme geforderten Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen, Annal. Phys., 549–560, 17; 1905.
Embrechts P., Klüppelberg C. and Mikosch T., Modelling Extreme Events for Insurance and Finance, Springer, Berlin; 1997.
Epstein J. and Axtell R., Growing Artificial Societies–Social Sciences from the Bottom Up, Brookings Institution Press and MIT Press, Cambridge, MA; 1996.
Ethier S. N. and Kurtz T. G., Markov Processes, Characterization and Convergence, Wiley, New York; 1986.
Feller W., An Introduction to Probability Theory and Its Applications, Wiley, New York; 1971.
Flierl G., Grünbaum D., Levin S. A. and Olson D., From individuals to aggregations: The interplay between behavior and physics, J. Theor. Biol., 397–454, 196; 1999.
Fournier N. and Méléard S., A microscopic probabilistic description of a locally regulated population and macroscopic approximations, Ann. Appl. Prob., 1880–1919, 14; 2004.
Franke J., Härdle W. K. and Hafner C. M., Statistics of Financial Markets: An Introduction, Second Edition, Springer, Heidelberg; 2011.
Friedman A., Partial Differential Equations, Krieger, New York; 1963.
Friedman A., Partial Differential Equations of Parabolic Type, Prentice-Hall, London; 1964.
Friedman A., Stochastic Differential Equations and Applications, Academic, London; 1975. Two volumes bounded as one, Dover, Mineola, NY; 2004.
Fristedt B., and Gray L., A Modern Approach to Probability Theory, Birkhäuser, Boston; 1997.
Gard T. C., Introduction to Stochastic Differential Equations, Marcel Dekker, New York; 1988.
Gihman I. I. and Skorohod A. V., Stochastic Differential Equations, Springer, Berlin; 1972.
Gihman I. I. and Skorohod A. V., The Theory of Random Processes, Springer, Berlin; 1974.
Gnedenko B. V., The Theory of Probability, Chelsea, New York; 1963.
Grigoriu M., Stochastic Calculus: Applications to Science and Engineering, Birkhäuser, Boston; 2002.
Grünbaum D. and Okubo A., Modelling social animal aggregations, in Frontiers of Theoretical Biology (S. A. Levin, Ed.) pp. 296–325, Lectures Notes in Biomathematics, Vol. 100, Springer, New York; 1994.
Gueron S., Levin S. A. and Rubenstein D. I., The dynamics of herds: From individuals to aggregations, J. Theor. Biol., 85–98, 182; 1996.
Hagan P. S., Kumar D., Lesniewski A. and Woodward D. E., Managing smile risk, Wilmott Mag., 84–102, 1; 2002
Harrison J. M. and Kreps D. M., Martingales and arbitrage in multiperiod securities markets, J. Econ. Theory, 381–408, 20; 1979.
Harrison J. M. and Pliska S. R., Martingales and stochastic integrals in the theory of continuous trading, Stochast. Process. Appl., 215–260, 11; 1981.
Has’minskii R. Z., Stochastic Stability of Differential Equations, Sijthoff & Noordhoff, The Netherlands; 1980.
Heath D., Jarrow R. and Morton A., Bond pricing and the term structure of interest rates: A new methodology for contingent claims valuation, Econometrica, 77–105, 1; 1992.
Heston S. L., A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options, Rev. Fin. Stud., 327–343, 6; 1993.
Hunt P. J. and Kennedy J. E., Financial Derivatives in Theory and Practice, Wiley, New York; 2000.
Hull J. and White A., Pricing interest rate derivative securities, Rev. Fin. Stud., 573–592, 4; 1990.
Ikeda N. and Watanabe S., Stochastic Differential Equations and Diffusion Processes, North-Holland, Kodansha; 1989.
Itô K. and McKean H. P., Diffusion processes and their sample paths, Springer, Berlin; 1965.
Jacobsen M., Statistical Analysis of Counting Processes, Springer, Heidelberg; 1982.
Jacod J. and Protter P., Probability Essentials, Springer, Heidelberg; 2000.
Jacod J. and Shiryaev A. N., Limit Theorems for Stochastic Processes, Springer Lecture Notes in Mathematics, Springer, Berlin; 1987.
Jamshidian F., LIBOR and swap market models and measures, Fin. Stochast., 43–67, 1; 1997.
Jeanblanc M., Yor M. and Chesney M., Mathematical Methods for Financial Markets, Springer, London; 2009.
Jelinski Z. and Moranda P., Software reliability research, in Statistical Computer Performance Evaluation, pp. 466–484, Academic, New York; 1972.
Kallenberg O., Foundations of Modern Probability, Springer, Berlin; 1997.
Karatzas I. and Shreve S. E., Brownian Motion and Stochastic Calculus, Springer, New York; 1991.
Karlin S. and Taylor H. M., A First Course in Stochastic Processes, Academic, New York; 1975.
Karlin S. and Taylor H. M., A Second Course in Stochastic Processes, Academic, New York; 1981.
Karr A. F., Point Processes and Their Statistical Inference, Marcel Dekker, New York; 1986.
Karr A. F., Point Processes and Their Statistical Inference, Second Edition Revised and expanded. Marcel Dekker, New York; 1991.
Klenke A., Probability Theory, Springer, Heidelberg; 2008.
Kloeden P. E. and Platen E., Numerical Solution of Stochastic Differential Equations, Springer, Heidelberg; 1999.
Kolmogorov A. N., Foundations of the Theory of Probability, Chelsea, New York; 1956.
Kolmogorov A. N. and Fomin E. S. V., Elements of Theory of Functions and Functional Analysis, Groylock, Moscow; 1961.
Kou S., A jump-diffusion model for option pricing, Manage. Sci., 1086–1101, 48; 2002.
Ladyzenskaja O. A., Solonnikov V. A. and Ural’ceva N.N., Linear and Quasi-linear Equations of Parabolic Type, AMS, Providence, RI, 1968.
Lamperti J., Stochastic Processes: A Survey of the Mathematical Theory, Springer, New York; 1977.
Lapeyre B., Pardoux E. and Sentis R. Introduction to Monte-Carlo methods for transport and diffusion equations, Oxford University Press, Oxford; 2003.
Last G. and Brandt A., Marked Point Processes on the Real Line: The Dynamic Approach, Springer, Heidelberg; 1995.
Lewis A. L., Option Valuation Under Stochastic Volatility, Finance Press, Newport Beach; 2000.
Lipster R. and Shiryaev A. N., Statistics of Random Processes, I: General Theory, Springer, Heidelberg; 1977.
Lipster R. and Shiryaev A. N., Statistics of Random Processes, II: Applications, Springer, Heidelberg; Second Edition, 2010.
Loève M., Probability Theory, Van Nostrand-Reinhold, Princeton, NJ; 1963.
Lu L., Optimal control of input rates of Stein’s models, Math. Med. Biol., 31–46, 28; 2011.
Ludwig D., Stochastic Population Theories, Lecture Notes in Biomathematics, Vol. 3, Springer, Heidelberg; 1974.
Lukacs E., Characteristic Functions, Griffin, London; 1970.
Mahajan V. and Wind Y., Innovation Diffusion Models of New Product Acceptance, Ballinger, Cambridge, MA; 1986.
Malrieu F., Convergence to equilibrium for granular media equations and their Euler scheme, Ann. Appl. Prob., 540–560, 13; 2003.
Mandelbrot K. and van Ness J., Fractional Brownian motions, fractional noises and applications, SIAM Rev., 422–437, 10; 1968.
Mao X., Stochastic Differential Equations and Applications, Horwood, Chichester; 1997.
Mao X., Marion G. and Renshaw E., Environmental Brownian noise suppresses explosions in population dynamics, Stoch. Proc. Appl., 95–110, 97; 2002.
Mao X, et al. Stochastic differential delay equations of population dynamics, J. Math. Anal. Appl., 296–320, 304;2005.
Markowich P. A. and Villani C., On the trend to equilibrium for the Fokker-Planck equation: An interplay between physics and functional analysis, Math. Contemp., 1–31, 19; 2000.
Medvegyev P., Stochastic Integration Theory, Oxford University Press, Oxford; 2007.
Méléard S., Asymptotic behaviour of some interacting particle systems: McKean–Vlasov and Boltzmann models, in Probabilistic Models for Nonlinear Partial Differential Equations (D. Talay, L. Tubaro, Eds.) pp. 42–95, Lecture Notes in Mathematics, Vol. 1627, CIME Subseries, Springer, Heidelberg; 1996.
Merton R. C., Theory of rational option pricing, Bell J. Econ. Manage. Sci., 141–183, 4; 1973.
Merton R. C., Option pricing when underlying stock returns are discontinuous, J. Fin. Econ., 125–144, 3; 1976.
Métivier M., Notions fondamentales de la théorie des probabilités, Dunod, Paris; 1968.
Meyer P. A., Probabilités et Potentiel, Ilermann, Paris; 1966.
Mikosch T., Non-Life Insurance Mathematics, Springer, Berlin Heidelberg; Second Edition, 2009.
Miltersen K. R., Sandmann K. and Sondermann D., Closed form solutions for term structure derivatives with log-normal interest rates, J. Fin., 409–430, 52; 1997.
Morale D., Capasso V. and Oelschläger K., An interacting particle system modelling aggregation behaviour: From individuals to populations, J. Math. Biol.; 2004.
Musiela M. and Rutkowski M., Martingale Methods in Financial Modelling, Springer, Berlin; 1998.
Nagai T. and Mimura M., Some nonlinear degenerate diffusion equations related to population dynamics, J. Math. Soc. Japan, 539–561, 35; 1983.
Neveu J., Mathematical Foundations of the Calculus of Probability, Holden-Day, San Francisco; 1965.
Nowman K. B., Gaussian estimation of single-factor continuous time models of the term structure of interest rate, J. Fin., 1695–1706, 52; 1997.
Oelschläger K., A law of large numbers for moderately interacting diffusion processes, Z. Wahrsch. verw. Geb., 279–322, 69; 1985.
Oelschläger K., Large systems of interacting particles and the porous medium equation, J. Differential Equations, 294–346, 88; 1990.
Øksendal B., Stochastic Differential Equations, Springer, Berlin; 1998.
Okubo A., Dynamical aspects of animal grouping: Swarms, school, flocks and herds, Adv. BioPhys., 1–94, 22; 1986.
Parzen E., Stochastic Processes, Holden-Day, San Francisco; 1962.
Pascucci A., Calcolo Stocastico per la Finanza, Springer Italia, Milano; 2008.
Pazy A., Semigroups of Linear Operators and Applications to Partial Differential Equations, Springer, New York; 1983.
Pliska S. R., Introduction to Mathematical Finance: Discrete-Time Models, Blackwell, Oxford; 1997.
Protter P., Stochastic Integration and Differential Equations, Springer, Berlin; 1990. Second Edition 2004.
Rebolledo R., Central limit theorems for local martingales, Z. Wahrsch. verw. Geb., 269–286, 51; 1980.
Revuz D. and Yor M., Continuous Martingales and Brownian Motion, Springer, Heidelberg; 1991.
Robert P., Stochastic Networks and Queues, Springer, Heidelberg; 2003.
Rogers L. C. G. and Williams D., Diffusions, Markov Processes and Martingales, Vol. 1, Wiley, New York; 1994.
Rolski T., Schmidli H., Schmidt V. and Teugels J., Stochastic Processes for Insurance and Finance, Wiley, New York; 1999.
Roozen H., Equilibrium and extinction in stochastic population dynamics, Bull. Math. Biol., 671–696, 49; 1987.
Samorodnitsky G. and Taqqu M. S., Stable Non-Gaussian Random Processes, Chapman & Hall/CRC Press, Boca Ration, FL; 1994.
Sato K. I., Lévy Processes and Infinitely Divisible Distributions, Cambridge University Press, Cambridge, UK; 1999.
Schuss Z., Theory and Applications of Stochastic Differential Equations, Wiley, New York; 1980.
Schuss Z., Theory and Applications of Stochastic Processes: An Analytical Approach, Springer, New York; 2010.
Shiryaev A. N., Probability, Springer, New York; 1995.
Shiryaev A. N. and A. S. Cherny Vector stochastic integrals and the fundamental theorems of asset pricing, Tr. Mat. Inst., Steklova, 12–56, 237; 2002.
Skellam J. G., Random dispersal in theoretical populations, Biometrika, 196–218, 38; 1951.
Skorohod A. V., Studies in the Theory of Random Processes, Dover, New York; 1982.
Skorohod A. V., Asymptotic Methods in the Theory of Stochastic Differential Equations, AMS, Providence, RI; 1989.
Sobczyk K., Stochastic Differential Equations: With Applications to Physics and Engineering, Kluwer, Dordrecht; 1991.
Stein R. B., A theoretical analysis of neuronal variability, Biophys. J., 173–194, 5; 1965.
Stein R. B., Some models of neuronal variability, Biophys. J., 37–68, 7; 1967.
Taira K., Diffusion Processes and Partial Differential Equations, Academic, New York; 1988.
Tan W. Y., Stochastic Models with Applications to Genetics, Cancers, AIDS and Other Biomedical Systems, World Scientific, Singapore; 2002.
Tucker H.G., A Graduate Course in Probability, Academic Press, New York; 1967.
Tuckwell H. C., On the first exit time problem for temporarily homogeneous Markov process, J. Appl. Prob., 39–48, 13; 1976.
Tuckwell H. C., Stochastic Processes in the Neurosciences, SIAM, Philadelphia; 1989.
Vasicek O., An equilibrium characterisation of the term structure, J. Fin. Econ., 177–188, 5; 1977.
Ventcel’ A. D., A Course in the Theory of Stochastic Processes, Nauka, Moscow (in Russian); 1975. Second Edition 1996.
Veretennikov A. Y., On subexponential mixing rate for Markov processes, Theory Prob. Appl., 110–122, 49; 2005.
Wang F. J. S., Gaussian approximation of some closed stochastic epidemic models, J. Appl. Prob., 221–231, 14; 1977.
Warburton K. and Lazarus J., Tendency-distance models of social cohesion in animal groups, J. Theor. Biol., 473–488, 150; 1991.
Wax N., Selected Papers on Noise and Stochastic Processes, Dover, New York; 1954.
Williams D., Probability with Martingales, Cambridge University Press, Cambridge, UK; 1991.
Wilmott P., Dewynne J. N. and Howison S. D.,Option Pricing: Mathematical Models and Computation, Oxford Financial Press, Oxford; 1993.
Wu F., Mao X. and Chen K., A highly sensitive mean-reverting process in finance and the Euler–Maruyama approximations, J. Math. Anal. Appl., 540–554, 348; 2008.
Yang G., Stochastic epidemics as point processes, in Mathematics in Biology and Medicine (V. Capasso, E. Grosso, S. L. Paveri-Fontana, Eds.), pp. 135–144, Lectures Notes in Biomathematics, Vol. 57, Springer, Heidelberg; 1985.
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Capasso, V., Bakstein, D. (2012). The Itô Integral. In: An Introduction to Continuous-Time Stochastic Processes. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-0-8176-8346-7_3
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