Andrew Sanford

Personal Details

This person is deceased (Date: Jun 2022)

First Name:	Andrew
Middle Name:
Last Name:	Sanford
Suffix:
RePEc Short-ID:	psa1281
Terminal Degree:	2004 Department of Banking and Finance; Monash Business School; Monash University (from RePEc Genealogy)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

1. Andrew Sanford & Imad Moosa, 2015. "Operational risk modelling and organizational learning in structured finance operations: a Bayesian network approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(1), pages 86-115, January.

   Cited by:

   1. Cornwell, Nikki & Bilson, Christopher & Gepp, Adrian & Stern, Steven & Vanstone, Bruce J., 2023. "Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
   2. Lu Wei & Jianping Li & Xiaoqian Zhu, 2018. "Operational Loss Data Collection: A Literature Review," Annals of Data Science, Springer, vol. 5(3), pages 313-337, September.
   3. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
   4. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
   5. Yuan Hong & Shaojian Qu, 2024. "Beyond Boundaries: The AHP-DEA Model for Holistic Cross-Banking Operational Risk Assessment," Mathematics, MDPI, vol. 12(7), pages 1-18, March.

2. Sanford, Andrew D. & Martin, Gael M., 2005. "Simulation-based Bayesian estimation of an affine term structure model," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 527-554, April.
   • Andrew D. Sanford & Gael M. Martin, 2003. "Simulation-Based Bayesian Estimation of Affine Term Structure Models," Monash Econometrics and Business Statistics Working Papers 15/03, Monash University, Department of Econometrics and Business Statistics.

   Cited by:

   1. Hautsch, Nikolaus & Yang, Fuyu, 2010. "Bayesian inference in a stochastic volatility Nelson-Siegel Model," SFB 649 Discussion Papers 2010-004, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
      • Hautsch, Nikolaus & Yang, Fuyu, 2012. "Bayesian inference in a Stochastic Volatility Nelson–Siegel model," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3774-3792.
   2. Andrew D. Sanford & Gael Martin, 2004. "Bayesian Analysis of Continuous Time Models of the Australian Short Rate," Monash Econometrics and Business Statistics Working Papers 11/04, Monash University, Department of Econometrics and Business Statistics.
   3. Yasuhiro Omori & Toshiaki Watanabe, 2007. "Block Sampler and Posterior Mode Estimation for A Nonlinear and Non-Gaussian State-Space Model with Correlated Errors," CARF F-Series CARF-F-104, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
      • Yasuhiro Omori & Toshiaki Watanabe, 2007. "Block Sampler and Posterior Mode Estimation for A Nonlinear and Non-Gaussian State-space Model with Correlated Errors," CIRJE F-Series CIRJE-F-508, CIRJE, Faculty of Economics, University of Tokyo.
      • Yasuhiro Omori & Toshiaki Watanabe, 2003. "Block Sampler and Posterior Mode Estimation for a Nonlinear and Non-Gaussian State-Space Model with Correlated Errors," CIRJE F-Series CIRJE-F-221, CIRJE, Faculty of Economics, University of Tokyo.
   4. Juneja, Januj, 2017. "Invariance, observational equivalence, and identification: Some implications for the empirical performance of affine term structure models," The Quarterly Review of Economics and Finance, Elsevier, vol. 64(C), pages 292-305.
   5. Jang, Bong-Gyu & Yoon, Ji Hee, 2010. "Analytic valuation formulas for range notes and an affine term structure model with jump risks," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2132-2145, September.
   6. Richard Finlay & Mark Chambers, 2009. "A Term Structure Decomposition of the Australian Yield Curve," The Economic Record, The Economic Society of Australia, vol. 85(271), pages 383-400, December.
      • Richard Finlay & Mark Chambers, 2008. "A Term Structure Decomposition of the Australian Yield Curve," RBA Research Discussion Papers rdp2008-09, Reserve Bank of Australia.
   7. Collin-Dufresne, Pierre & Goldstein, Robert S. & Jones, Christopher S., 2009. "Can interest rate volatility be extracted from the cross section of bond yields?," Journal of Financial Economics, Elsevier, vol. 94(1), pages 47-66, October.
   8. Peter Feldhütter, 2016. "Can Affine Models Match the Moments in Bond Yields?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 1-56, June.
   9. Andrew D. Sanford & Gael M. Martin, 2006. "Bayesian comparison of several continuous time models of the Australian short rate," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 46(2), pages 309-326, June.

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