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Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging
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Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging

by Yves Hilpisch
August 2015
Beginner to intermediate
376 pages
8h 27m
English
Wiley
Content preview from Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging

Bibliography

  1. Andersen, Leif (2008) Simple and Efficient Simulation of the Heston Stochastic Volatility Model, Journal of Computational Finance, 11(3), 1–42.
  2. Andersen, Leif, Peter Jäckel and Christian Kahl (2010) Simulation of Square-Root Processes, in Encyclopedia of Quantitative Finance, Rama Cont (ed. in chief), John Wiley & Sons, Hoboken, New Jersey.
  3. Andersen, Torben and Luca Benzoni (2009) Realized Volatility, in Handbook of Financial Time Series, Torben Andersen, Richard Davis, Jens-Peter Krieß, Thomas Mikosch (eds), Springer Verlag, Berlin, 555–575.
  4. Bakshi, Gurdip, Charles Cao and Zhiwu Chen (1997) Empirical Performance of Alternative Option Pricing Models, Journal of Finance, 52(5), 2003–2049.
  5. Bali, Turan and Liuren Wu (2006) A Comprehensive Analysis of the Short-Term Interest-Rate Dynamics, Journal of Banking & Finance, 30, 1269–1290.
  6. Bates, David (1996) Jumps and Stochastic Volatility: Exchange Rates Processes Implicit in Deutsche Mark Options, Review of Financial Studies, 9(1), 69–107.
  7. Baxter, Martin and Andrew Rennie (1996) Financial Calculus—An Introduction to Derivative Pricing, Cambridge University Press, Cambridge.
  8. Bhattacharya, Rabi and Edward Waymire (2007) A Basic Course in Probability Theory, Springer Verlag, New York.
  9. Bittman, James (2009) Trading Options as a Professional, McGraw-Hill, New York.
  10. Björk, Tomas (2004) Arbitrage Theory in Continuous Time, 2nd ed., Oxford University Press, Oxford.
  11. Björk, Tomas (2009) An Overview of Interest Rate Theory, in ...
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Publisher Resources

ISBN: 9781119037996Purchase book