Computational Finance

From Ioannis Kourouklides
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This page contains resources about Computational Finance, including Financial Engineering, Mathematical Finance, Quantitative Finance and Financial Econometrics.

Subfields and Concepts[edit]

  • Binomial Options Pricing Model
  • Black–Scholes Model
  • Capital Asset Pricing Model (CAPM)
    • Modern Portfolio Theory (MPT)
    • Markowitz Model / Mean-Variance Model
  • Markov property
  • Martingale property
  • Efficient Market Hypothesis (EMH)
  • Capital Market Line
  • Financial Signal Processing
  • Financial Portfolio Management / Asset Allocation / Non-speculative Trading
    • Security Selection
    • Portfolio Construction
  • Speculative Trading
    • Intra-day Trading / Day Trading
  • Algorithmic Trading
  • Single-asset Trading
    • Multi-asset Trading
  • Inter-day trading
  • Swing trading
  • Financial Risk Management
  • Financial Risk Metrics
    • Value at Risk (VaR)
    • Conditional VaR (CVaR)
    • Sharpe ratio
    • Dispersion
    • Drawdown
    • Maximum Drawdown (MDD)
    • Alpha
    • Beta
    • Sortino ratio
    • Calmar ratio / Drawdown ratio

Online courses[edit]

Video Lectures[edit]

Lectures Notes[edit]

Books and Book Chapters[edit]

See also Reading List.

  • Lachowicz, P. (TBA). Python for Quants. Volume II. QuantAtRisk eBooks.
  • Dixon, M. F. (2020). Machine Learning in Finance: From Theory to Practice. Springer Nature.
  • Sargent, T., & Stachurski, J. (2015). Quantitative Economics with Python. (link)
  • de Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.
  • Yan, Y. (2017). Python for Finance. 2nd Ed. Packt Publishing Ltd.
  • Akansu, A. N., Kulkarni, S. R., & Malioutov, D. M. (Eds.). (2016). Financial Signal Processing and Machine Learning. John Wiley & Sons.
  • Akansu, A. N., & Torun, M. U. (2015). A primer for financial engineering: financial signal processing and electronic trading. Academic Press.
  • Lachowicz, P. (2015). Python for Quants. Volume I. QuantAtRisk eBooks.
  • Hilpisch, Y. (2015). Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging. John Wiley & Sons.
  • Skoglund, J., & Chen, W. (2015). Financial risk management: Applications in market, credit, asset and liability management and firmwide risk. John Wiley & Sons.
  • Hull, J. (2015). Risk management and financial institutions. 4th Ed. John Wiley & Sons.
  • John, C. (2014). Options, Futures and other Derivative Securities. 9th Ed. Prentice HaII.
  • Hilpisch, Y. (2014). Python for Finance: Analyze Big Financial Data. O'Reilly Media.
  • Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern portfolio theory and investment analysis. 9th Ed. John Wiley & Sons.
  • Benninga, S. (2014). Financial modeling. MIT Press.
  • Crack, T. F. (2014). Heard on the Street: Quantitative Questions from Wall Street Job Interviews. 15th Ed. Timothy Crack.
  • Crouhy, M., Galai, D., & Mark, R. (2014). The essentials of risk management. 2nd Ed. McGraw-Hill.
  • Chatterjee, R. (2014). Practical methods of financial engineering and risk management: tools for modern financial professionals. Apress.
  • Blyth, S. (2013). An introduction to quantitative finance. Oxford University Press.
  • Joshi, M. S., & Paterson, J. M. (2013). Introduction to Mathematical Portfolio Theory. Cambridge University Press.
  • Joshi, M. S., Denson, N., & Downes, A. (2013). Quant Job Interview: Questions and Answers. 2nd Ed. Pilot Whale Press.
  • Verbeek, M. (2012). A guide to modern econometrics. 4th Ed. John Wiley & Sons.
  • McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media.
  • Steland, A. (2012). Financial statistics and mathematical finance: methods, models and applications. John Wiley & Sons.
  • Hirsa, A. (2012). Computational methods in finance. CRC Press.
  • Alhabeeb, M. J. (2012). Mathematical finance. John Wiley & Sons.
  • Boyarshinov, V. (2012). Machine learning in computational finance: Practical algorithms for building artificial intelligence applications. LAP LAMBERT Academic Publishing.
  • Allen, S. (2012). Financial Risk Management: A Practitioner's Guide to Managing Market and Credit Risk. 2nd Ed. John Wiley & Sons.
  • Joshi, M. S. (2011). More Mathematical Finance. Pilot Whale.
  • Stefanica, D. (2011). A primer for the Mathematics of Financial Engineering. Fe Press.
  • Duffie, D. (2010). Dynamic asset pricing theory. Princeton University Press.
  • Tsay, R. S. (2010). Analysis of Financial Time Series. 3rd Ed. John Wiley & Sons.
  • Kennedy, D. (2010). Stochastic financial models. Chapman and Hall/CRC.
  • Jeanblanc, M., Yor, M., & Chesney, M. (2009). Mathematical methods for financial markets. Springer Science & Business Media.
  • Meucci, A. (2009). Risk and asset allocation. Springer Science & Business Media.
  • Wang, P. (2008). Financial econometrics. Routledge.
  • Zhou, X. (2008). A Practical Guide to Quantitative Finance Interviews. 14th Ed. CreateSpace.
  • Joshi, M. S. (2008). The concepts and practice of mathematical finance. 2nd Ed. Cambridge University Press.
  • Brooks, C. (2008). Introductory econometrics for finance. 2nd Ed. Cambridge University Press.
  • Bacon, C. R. (2008). Practical Portfolio Performance Measurement and Attribution. 2nd Ed. John Wiley & Sons.
  • Fusai, G., & Roncoroni, A. (2008). Implementing models in quantitative finance: methods and cases. Springer Science & Business Media.
  • Wilmott, P. (2007). Paul Wilmott introduces quantitative finance. John Wiley & Sons.
  • Estrada, J. (2007). Finance in a Nutshell: A No-nonsense Companion to the Tools and Techniques of Finance. Pearson Education.
  • Brabazon, A., & O'Neill, M. (2006). Biologically inspired algorithms for financial modelling. Springer Science & Business Media.
  • Seydel, R., & Seydel, R. (2006). Tools for computational finance. Springer.
  • Brandimarte, P. (2006). Numerical methods in finance and economics: a MATLAB-based introduction. 2nd Ed. John Wiley & Sons.
  • Higham, D. (2004). An introduction to financial option valuation: mathematics, stochastics and computation. Cambridge University Press.
  • Joshi, M. S. (2004). More Mathematical Finance. Cambridge University Press.
  • Joshi, M. S. (2004). The concepts and practice of mathematical finance. Cambridge University Press.
  • Cuthbertson, K., & Nitzsche, D. (2004). Quantitative financial economics: stocks, bonds and foreign exchange. 2nd Ed. John Wiley & Sons.
  • Glasserman, P. (2003). Monte Carlo methods in financial engineering. Springer Science & Business Media.
  • Feibel, B. J. (2003). Investment performance measurement. John Wiley & Sons.
  • Jackel, P. (2002). Monte Carlo methods in finance. John Wiley & Sons.
  • Cuthbertson, K., & Nitzsche, D. (2001). Financial engineering: derivatives and risk management. John Wiley & Sons.
  • Grinold, R. C., & Kahn, R. N. (2000). Active portfolio management. 2nd Edition. McGraw-Hill Education.
  • Karatzas, I., & Shreve, S. E. (1998). Methods of mathematical finance. Springer Science & Business Media.
  • Luenberger, D. G. (1997). Investment science. Oxford University Press.
  • Campbell, J. Y., Lo, A. W. C., & MacKinlay, A. C. (1997). The econometrics of financial markets. 2nd Ed. Princeton University Press.
  • Baxter, M., & Rennie, A. (1996). Financial calculus: an introduction to derivative pricing. Cambridge University Press.
  • Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton University Press.

Scholarly Articles[edit]

  • Boyd, S., Busseti, E., Diamond, S., Kahn, R. N., Koh, K., Nystrup, P., & Speth, J. (2017). Multi-period trading via convex optimization. Foundations and Trends® in Optimization, 3(1), 1-76.
  • Feng, Y., & Palomar, D. P. (2016). A signal processing perspective on financial engineering. Foundations and Trends® in Signal Processing, 9(1–2), 1-231.
  • Bonanno, G., Caldarelli, G., Lillo, F., Micciche, S., Vandewalle, N., & Mantegna, R. N. (2004). Networks of equities in financial markets. The European Physical Journal B, 38(2), 363-371.


See also[edit]

Other Resources[edit]