Risk Estimation via Regression

Published Online:https://doi.org/10.1287/opre.2015.1419

References

  • Britten-Jones M, Schaefer SM (1999) Non-linear value-at-risk. Eur. Finance Rev. 2(2):161–187.CrossrefGoogle Scholar
  • Broadie M, Du Y, Moallemi CC (2011) Efficient risk estimation via nested sequential simulation. Management Sci. 57(6):1172–1194.LinkGoogle Scholar
  • Duffie D, Pan J (2001) Analytical value-at-risk with jumps and credit risk. Finance Stochastics 5(2):155–180.CrossrefGoogle Scholar
  • Gharavi RK (2010) Himalayan option. Encyclopedia of Quantitative Finance (John Wiley & Sons, New York).CrossrefGoogle Scholar
  • Glasserman P (2004) Monte Carlo Methods in Financial Engineering (Springer, New York).CrossrefGoogle Scholar
  • Glasserman P, Heidelberger P, Shahabuddin P (2000) Variance reduction techniques for estimating value-at-risk. Management Sci. 46(10):1349–1364.LinkGoogle Scholar
  • Golub GH, Van Loan CF (2012) Matrix Computations, 3rd ed. (Johns Hopkins University Press, Baltimore).Google Scholar
  • Gordy MB, Juneja S (2008) Nested simulation in portfolio risk measurement. FEDS 2008-21, Federal Reserve Board, Washington, DC.Google Scholar
  • Gordy MB, Juneja S (2010) Nested simulation in portfolio risk management. Management Sci. 56(10):1833–1848.LinkGoogle Scholar
  • Haug E (2006) The Complete Guide to Option Pricing Formulas, 2nd ed. (McGraw-Hill, New York).Google Scholar
  • Hong LJ, Juneja S (2009) Estimating the mean of a non-linear function of conditional expectation. Proceedings of the 2009 Winter Simulation Conference (IEEE Press, Piscataway, NJ), 1223–1236.CrossrefGoogle Scholar
  • Hui CH (1997) Time-dependent barrier option values. J. Futures Markets 17(6):667–688.CrossrefGoogle Scholar
  • Lan H, Nelson BL, Staum J (2010) A confidence interval procedure for expected shortfall risk measurement via two-level simulation. Oper. Res. 58(5):1481–1490.LinkGoogle Scholar
  • Lee S-H (1998) Monte Carlo Computation of Conditional Expectation Quantiles. Unpublished doctoral dissertation, Stanford University, Stanford, CA.Google Scholar
  • Lee S-H, Glynn PW (2003) Computing the distribution function of a conditional expectation via Monte Carlo: Discrete conditioning spaces. ACM Trans. Modeling Comput. Simulation 13(3):238–258.CrossrefGoogle Scholar
  • Liu M, Staum J (2010) Stochastic kriging for efficient nested simulation of expected shortfall. J. Risk 12(3):3–27.CrossrefGoogle Scholar
  • Longstaff FA, Schwartz ES (2001) Valuing American options by simulation: A simple least-squares approach. Rev. Financial Stud. 14(1):113–147.CrossrefGoogle Scholar
  • Metwally SAK, Atiya AF (2002) Using Brownian bridge for fast simulation of jump-diffusion processes and barrier options. J. Derivatives 10(1):43–54.CrossrefGoogle Scholar
  • Rouvinez C (1997) Going Greek with VaR. Risk 10(2):57–63.Google Scholar
  • Shapiro A, Dentcheva D, Ruszczyński A (2009) Lectures on Stochastic Programming: Modeling and Theory (SIAM, Philadelphia).CrossrefGoogle Scholar
  • Sobol IM (1967) On the distribution of points in a cube and the approximate evaluation of integrals. USSR Comput. Math. Math. Physics 7(4):86–112.CrossrefGoogle Scholar
  • Sun Y, Apley DW, Staum J (2011) Efficient nested simulation for estimating the variance of a conditional expectation. Oper. Res. 59(4):998–1007.LinkGoogle Scholar
  • Tsitsiklis JN, Van Roy B (2001) Regression methods for pricing complex American-style options. IEEE Trans. Neural Networks 12(4):694–703.CrossrefGoogle Scholar
  • White H (2001) Asymptotic Theory for Econometricians (Academic Press, Orlando, FL).Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.