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Appendices

  • René Carmona
Chapter
Part of the Springer Texts in Statistics book series (STS)

Abstract

This appendix gives a streamlined introduction to the basics of R. Following the prescriptions given in this appendix will take you through an introductory session intended for readers who are first time users of R. We do not expect that such a session will turn R-novices into experts. However, it should help beginners feel comfortable enough with the language to start practicing with the examples given in the book.

Keywords

Asset Price Option Price Risky Asset Implied Volatility Contingent Claim 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    S. Amari. Differential Geometrical Methods in Statistics, volume 28 of Lecture Notes in Statistics. Springer Verlag, New York, NY, 1985.Google Scholar
  2. 2.
    N. Anderson, F. Breedon, M. Deacon, A. Derry, and G. Murphy. Estimating and Interpreting the Yield Curve. Wiley, Chichester, 1996.Google Scholar
  3. 3.
    A. Antoniadis, J. Berruyer, and R. Carmona. Régression Non-linéaire et Applications. Economica, 1992.Google Scholar
  4. 4.
    P. Artzner, F. Delbaen, J.M. Eber, and D. Heath. Coherent measures of risk. Mathematical Finance, 9(3):203–228, 1999.CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    E. Banks, editor. Weather Risk Management, New York, NY, 2002. Palgrave.Google Scholar
  6. 6.
    T. Bollerslev. Generalized Auto Regressive Heteroskedasticity. Journal of Econometrics, 31:307–327, 1986.CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    T. Bollerslev, R.F Engle, and J.M. Wooldridge. ARCH models. In Handbook of Econometrics, IV, pages 2959–3038. 1994.Google Scholar
  8. 8.
    G.E.P. Box and G.M. Jenkins. Time Series Analysis: Forecasting and Control. Holden Day, San Francisco, revised edition, 1976.Google Scholar
  9. 9.
    L. Breiman, J.H. Friedman, R.A. Olshen, and C.I. Stone. Classification And Regression Trees. Wadsworth and Brooks Cole, Monterey, CA, 1984.MATHGoogle Scholar
  10. 10.
    P.J. Brockwell and R.A. Davis. Introduction to Time Series and Forecasting. Springer Texts in Statistics. Springer Verlag, New York, NY, 1996.CrossRefMATHGoogle Scholar
  11. 11.
    R.L. Brown, J. Durbin, and J.M. Evans. Techniques for testing the constancy of regression relationship over time (with comments). Journal of the Royal Statistical Society, 37:149–192, 1975.MATHMathSciNetGoogle Scholar
  12. 12.
    A. Bruce and H.Y. Gao. Applied Wavelet Analysis with S-Plus. Springer Verlag, New York, N.Y, 1996.Google Scholar
  13. 13.
    J.Y. Campbell, A.W. Lo, and A.C. MacKinlay. The Econometrics of Financial Markets. Princeton University Press, Princeton, N.J., 1997.MATHGoogle Scholar
  14. 14.
    R. Carmona, W. Hwang, and B. Torresani. Time Frequency Analysis: Continuous Wavelet and Gabor Transform, with an implementation in S-Plus. Academic Press, New York, N.Y., 1998.Google Scholar
  15. 15.
    R. Carmona and J. Morrisson. EVANESCE, an S-Plus library for heavy tail distributions and copulas. Technical report, Dept. of Operations Research & Financial Engineering, Princeton University, 2000. http://www.princeton.edu/~rcarmona.
  16. 16.
    R. Carmona, and J.P. Fouque and D. Vestal. Interacting Particle Systems for the Computation of CDO Tranche Spreads with Rare Defaults. Finance and Stochastics, 13:613–633, 2009.CrossRefMATHMathSciNetGoogle Scholar
  17. 17.
    R. Carmona and S. Crepey. Importance Sampling and Interacting Particle Systems for the Estimation of Markovian Credit Portfolio Loss Distributions. Intern. J. of Theoretical and Applied Finance, 13:577–602, 2010.CrossRefMATHMathSciNetGoogle Scholar
  18. 18.
    R. Carmona and M. Tehranchi. Interest Rate Modes: an Infinite Dimensional Stochastic Analysis Perspective. Springer Verlag, New York N.Y., 2010.Google Scholar
  19. 19.
    R. Carmona, P. Del Moral, P. Hu and N. Oudjane. An introduction to particle methods in Finance. in Numerical Methods in Finance eds R. Carmona, P. Del Moral, P. Hu and N. Oudjane, pp. 1–45, Springer Verlag, 2012.Google Scholar
  20. 20.
    R. Carmona and M. Croulon. A Survey of Commodity Markets and Structural Approaches to Modeling Electricity. In Energy Markets, Proceedings of the WPI Special Year eds. F. Benth, Springer Verlag, pp. 1–42, 2012.Google Scholar
  21. 21.
    J.M. Chambers. Programming with Data: A Guide to the S Language. MathSoft, Seattle WA, 1998.CrossRefMATHGoogle Scholar
  22. 22.
    N.H. Chan. Time Series: Applications to Finance. John Wiley & Sons, New York, N.Y., 2002.Google Scholar
  23. 23.
    W.S. Cleveland. The Elements of Graphing Data. Hobart Press, Summit, N.J., 1994.Google Scholar
  24. 24.
    L. Clewlow and C. Strickland. Energy Derivatives, Pricing and Risk Management. Lacima Publications, London, 2000.Google Scholar
  25. 25.
    P. Dalgaard. Introductory Statistics with R. Springer-Verlag, New York, 2002.MATHGoogle Scholar
  26. 26.
    C. de Boor. A Practical Guide to Splines. Springer Verlag, New York, N.Y., 1978.CrossRefMATHGoogle Scholar
  27. 27.
    D. Duffie and J. Singleton. Credit Risk: Pricing, Measement, and Management. 2003.Google Scholar
  28. 28.
    P. Embrechts, R. Frey, and A. McNeil. Quantitative Risk Management: Concepts, Techniques and Tools. Princeton University Press, Princeton, NJ, 2005.Google Scholar
  29. 29.
    P. Embrechts, C. Klüppelberg, and T. Mikosch. Modelling Extremal Events for Insurance and Finance. Springer-Verlag, New York, 2000.Google Scholar
  30. 30.
    R.F. Engle. Autoregressive conditional heteroskedasticity with estimates of the variance of the United Kingdom inflation. Econometrica, pages 987–1006, 1982.Google Scholar
  31. 31.
    R.F. Engle and T. Bollerslev. Modeling the persistence of conditional variances. Econometric Reviews, 5:1–50, 1986.CrossRefMATHMathSciNetGoogle Scholar
  32. 32.
    R.F. Engle and C. Granger. Cointegration and error-correction: Representation, estimation and testing. Econometrica, 55:251–276, 1987.CrossRefMATHMathSciNetGoogle Scholar
  33. 33.
    J. A. Greenwood et al. Extreme Value Theory based on the r largest annual events: a robust approach. Water Resources Research, 15:1049–1054, 1979.CrossRefGoogle Scholar
  34. 34.
    E.F. Fama and R.R. Biss. The information in long-maturity forward rates. American Economic Review, 77:680–692, 1987.Google Scholar
  35. 35.
    J. Fan and Q. Yao. Nonlinear Time Series: Nonparametric and Parametric Methods. Springer Verlag, New York, N.Y., 2003.CrossRefGoogle Scholar
  36. 36.
    H. Foellmer and A. Schied. Stochastic Finance: An Introduction in Discrete Time. De Gruyter, Berlin, 2002.CrossRefGoogle Scholar
  37. 37.
    Bank for International Settlements. Zero-coupon yield curves. Technical report, March 1999.Google Scholar
  38. 38.
    J.P Fouque, G. Papanicolaou, and R. Sircar. Derivatives in Financial Markets with Stochastic Volatility. Cambridge University Press, London, 2000.Google Scholar
  39. 39.
    R. Gençay, F. Selçuk, and B. Whitcher. An Introduction to Wavelets and Other Filtering Methods in Finance and Economics. Academic Press, New York, N.Y., 2002.MATHGoogle Scholar
  40. 40.
    P. Glasserman. Monte Carlo Methods in Financial Engineering. Springer-Verlag, New York, N.Y., 2004.MATHGoogle Scholar
  41. 41.
    C. Gouriéroux. ARCH Models and Financial Applications. Springer Verlag, New York, N.Y., 1997.CrossRefMATHGoogle Scholar
  42. 42.
    C. Gourieroux and J. Jasiak. Financial Eeconometrics: Problems, Models and Methods. Princeton University Press, Princeton, NJ, 2001.Google Scholar
  43. 43.
    W. Haerdle. Non-parametric Regression. Springer Verlag, New York, N.Y., 1994.Google Scholar
  44. 44.
    J.D. Hamilton. Time Series Analysis. Princeton University Press, Princeton, N.J., 1994.MATHGoogle Scholar
  45. 45.
    T. Hastie, Tibshirani, and J. Friedman. The Elements of Statistical Learning, Data mining, Inference and Prediction. Springer Verlag, New York, N.Y., 2001.Google Scholar
  46. 46.
    J. R. M. Hosking. L-moments: Analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society, Series B, 52(1):105–124, 1990.MATHMathSciNetGoogle Scholar
  47. 47.
    J. R. M. Hosking and J. R. Wallis. Parameter and quantile estimation for the generalized Pareto distribution. Technometrics, 29(3):339–349, 1987.CrossRefMATHMathSciNetGoogle Scholar
  48. 48.
    J. R. M. Hosking, J. R. Wallis, and E. F. Wood. Estimation of the generalized extreme value distribution by the Method of Probability-Weighted Moments. Technometrics, 27(3):251–261, 1985.CrossRefMathSciNetGoogle Scholar
  49. 49.
    P.J. Huber. Robust Statistics. John Wiley & Sons, New York, N.Y., 1981.CrossRefMATHGoogle Scholar
  50. 50.
    J.C. Hul. Options, Futures, and Other Derivatives. Prentice Hall, New York, N.Y., 6th edition, 2006.Google Scholar
  51. 51.
    J.M. Hutchinson, A.W. Lo, and T. Poggio. A nonparametric approach to the pricing and hedging of derivative securities via learning networks. Journal of Finance, 49, 1994.Google Scholar
  52. 52.
    S. Johansen. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press, Oxford, 1995.CrossRefMATHGoogle Scholar
  53. 53.
    P. Jorion. Value at Risk: The New Benchmark for Managing Financial Risk. McGraw Hill, New York, N.Y., 2nd edition, 2000.Google Scholar
  54. 54.
    J. Rice. Mathematical Statistics and Data Analysis. Duxbury Press, Belmont, CA, 2nd edition, 1995.MATHGoogle Scholar
  55. 55.
    C.J. Kim and C.R. Nelson. State-Space Models with Regime Switching. Cambridge, MA, 1999.Google Scholar
  56. 56.
    G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5:1–25, 1996.MathSciNetGoogle Scholar
  57. 57.
    T. Kohonen. Self-organizing Maps. Springer Verlag, New York, N.Y., 1995.CrossRefGoogle Scholar
  58. 58.
    D. Lamberton and B. Lapeyre. Introduction to Stochastic Calculus Applied to Finance. CRC Press, 1996.Google Scholar
  59. 59.
    D. Lando. Credit Risk Modeling: Theory and Aplications. 2004.Google Scholar
  60. 60.
    R. Litterman and J. Scheinkman. Common factors affecting bond returns. Journal of Fixed Income, 1:49–53.Google Scholar
  61. 61.
    F.M. Longin. From value at risk to stress testing: The extreme value approach. Journal of Banking and Finance, 24:10971130, 2000.Google Scholar
  62. 62.
    F. A. Longstaff and R. S. Schwartz. Valuing American options by simulation : A simple least-square approach. Review of Financial Studies, 14:113–147, 2001.CrossRefGoogle Scholar
  63. 63.
    G. Lindren M. R. Leadbetter and H. Rootzén. Extremes and Related Properties of Random Sequences and Processes. Springer-Verlang, New York, 1983.Google Scholar
  64. 64.
    B.B. Mandelbrot. New methods in statistical economics. Journal of Political Economy, 71:421–440, 1963.CrossRefGoogle Scholar
  65. 65.
    B.B. Mandelbrot. The variation of certain speculative prices. Journal of Business, 36:394–419, 1963.CrossRefGoogle Scholar
  66. 66.
    B.B. Mandelbrot. Fractal, Form Dimension and Chance. W.H. Freeman & Co., San Francisco, CA, 1977.Google Scholar
  67. 67.
    K.V. Mardia, J.T. Kent, and J.M. Bibby. Multivariate Analysis. Academic Press, New York, N.Y., 1979.MATHGoogle Scholar
  68. 68.
    D. Drouet Mari and S. Kotz. Correlation and Dependence. Imperial College Press, London, 2001.CrossRefMATHGoogle Scholar
  69. 69.
    D.C. Montgomery and E.A. Peck. Introduction to Linear Regression Analysis. John Wiley & Sons, New York, N.Y., 2nd edition, 1992.MATHGoogle Scholar
  70. 70.
    S. Menendez N. Perez and L. Seco. New families of distributions fitting l-moments for modelling financial data, 2004.Google Scholar
  71. 71.
    G.P. Nason. Wavelet Methods in Statistics with R. Springer Verlag, New York, NY, 2008.CrossRefMATHGoogle Scholar
  72. 72.
    A. Mc Neil, R. Frey, and P. Embrechts. Quantitative Risk Management: Concepts, Techniques and Tools. Princeton University Press, Princeton, NJ, 2005.Google Scholar
  73. 73.
    R. B. Nelsen. An Introduction to Copulas. Springer-Verlag, New York, 1999.CrossRefMATHGoogle Scholar
  74. 74.
    C.R. Nelson and A.F. Siegel. Parsimonious modeling of yield curves.Google Scholar
  75. 75.
    J. Pickands. Multivariate extreme value distributions. pages 229–231, 1981.Google Scholar
  76. 76.
    M.B. Priestley. Nonlinear and Non-stationary Time Series Analysis. Academic Press, New York, N.Y., 1988.Google Scholar
  77. 77.
    R. Rebonato. Interest-Rate Option Models: Understanding, Analyzing and Using Models for Exotic Interest-Rate Options. Wiley, New York, N.Y., 1996.Google Scholar
  78. 78.
    E. Renault and N. Touzi. Option hedging and implied volatility in a stochastic volatility model. Mathematical Finance, 6:215–236, 1996.CrossRefGoogle Scholar
  79. 79.
    S.I. Resnick. Extreme Values, Regular Variation and Point Processes. Springer Verlag, New York, 1987.MATHGoogle Scholar
  80. 80.
    R. Roll. A critique of the asset pricint theory’s test, part one: Past and potential testability of the theory. Journal of Financial Economics, 4, 1977.Google Scholar
  81. 81.
    M. Rosenblatt. Gaussian and Non-Gaussian Linear Time Series and Random Fields. Springer Verlag, New York N.Y., 2000.CrossRefMATHGoogle Scholar
  82. 82.
    P.J. Rousseeuw and A.M. Leroy. Robust Regression and Outlier Detection. New York, N.Y., 1984.Google Scholar
  83. 83.
    B. Van Roy and J. N. Tsitsiklis. Regression methods for pricing complex American-style options. IEEE Trans. on Neural Networks, 2000.Google Scholar
  84. 84.
    P.A. Ruud. An Introduction to Classical Econometric Theory. Oxford University Press, New York, N.Y., 2000.Google Scholar
  85. 85.
    Y. Ait Sahalia and A.W. Lo. Nonparametric estimation of state price densities implicit in financial asset prices. The Journal of Finance, 53:499–547, 1998.CrossRefGoogle Scholar
  86. 86.
    P. Schönbucher. Credit Dderivatives Pricing Models: Model, Pricing and Implementation. John Wiley & Sons, New York, N.Y., 2003.Google Scholar
  87. 87.
    R.H. Shumway and D.S. Stoffer. Time Series Analysis and Its Applications. Springer Verlag, Newy York, N.Y., 2000.CrossRefMATHGoogle Scholar
  88. 88.
    B.W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman & Hall, London, 1986.CrossRefMATHGoogle Scholar
  89. 89.
    R. L. Smith. Threshold methods in statistics. In J. Tiago de Olivera, editor, Statistical Extremes and Applications, volume 131 of NATO ASI Series, pages 621–638. Reidel, 1984.Google Scholar
  90. 90.
    J.M. Steele. Stochastic Calculus and Financial Applications. Springer Verlag, New York N.Y., 2000.Google Scholar
  91. 91.
    L.E.O. Svensson. Estimating and interpreting forward interest rates: Sweden 1992–94. 4871, 1994.Google Scholar
  92. 92.
    R.S. Tsay. Analysis of Financial Time Series. John Wiley & Sons, New York, N.Y., 2002.CrossRefMATHGoogle Scholar
  93. 93.
    O. Vasicek and G. Fong. Term structure estimation using exponential splines. Journal of Finance, 38:339–348, 1982.CrossRefGoogle Scholar
  94. 94.
    W. N. Venables and B. D. Ripley, Modern Applied Statistics with S-PLUS. Springer-Verlag, New York, 1997.CrossRefMATHGoogle Scholar
  95. 95.
    H. von Storch and F.W. Zwiers. Statistical Analysis in Climate Research. Cambridge University Press, New York, N.Y., 1999.Google Scholar
  96. 96.
    L. De Vroye. Non-uniform Variates. Springer Verlag, 1984.Google Scholar
  97. 97.
    M.V. Wickerhauser. Adapted Wavelet Analysis: from Theory to Software. A.K.Peters LTd, Wellesley, MA, 1994.MATHGoogle Scholar
  98. 98.
    I.H. Witten and E. Frank. Data Mining. Academic Press, San Diego, CA, 2000.Google Scholar
  99. 99.
    E. Zivot and J. Wang. Modeling Financial Time Series with S-PLUS. Springer Verlag, New York, N.Y., 2003.CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • René Carmona
    • 1
  1. 1.Department of Operations Research and Financial EngineeringPrinceton UniversityPrincetonUSA

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