Abstract
Securities trading is one of the few business activities where a few seconds processing delay can cost a company big fortune. The growing competition in the market exacerbates the situation and pushes further towards instantaneous trading even in split second. The key lies on the performance of the underlying information system. Following the computing evolution in financial services, it was a centralized process to begin with and gradually decentralized into a distribution of actual application logic across service networks. Financial services have tradition of doing most of its heavy-duty financial analysis in overnight batch cycles. However, in securities trading it cannot satisfy the need due to its ad hoc nature and requirement of fast response. New computing paradigms, such grid and cloud computing, aiming at scalable and virtually standardized distributed computing resources, are well suited to the challenge posed by the capital market practices. Both consolidate computing resources by introducing a layer of middleware to orchestrate the use of geographically distributed powerful computers and large storages via fast networks. It is nontrivial to harvest the most of the resources from this kind of architecture. Wiener process plays a central role in modern financial modeling. Its scaled random walk feature, in essence, allows millions of financial simulation to be conducted simultaneously. The sheer scale can only be tackled via grid or cloud computing. In this study the core computing competence for financial services is examined. Grid and cloud computing will be briefly described. How the underlying algorithm for financial analysis can take advantage of grid environment is chosen and presented. One of the most popular practiced algorithms Monte Carlo simulation is used in our case study for option pricing and risk management. The various distributed computational platforms are carefully chosen to demonstrate the performance issue for financial services.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abramson, D., et al. (2006). Deploying scientific applications to the PRAGMA grid testbed: strategies and lessons. In Sixth IEEE international symposium on cluster computing and the grid, Singapore, pp. 241–248.
Ahmad, K., Gillam, L., & Cheng, D. (2005). Textual and quantitative analysis: Towards a new, e-mediated social science. In Proceedings of the 1st international conference on e-Social science, Manchester, pp. 22–24.
Almond, J., & Snelling, D. (1999). UNICORE: Uniform access to supercomputing as an element of electronic commerce. Future Generation Computer Systems, 15(5), 539–548.
Amad, K., et al. (2004). Financial information grid –an ESRC e-Social social science pilot. In Proceedings of the third UK e-Science programme all-hands meeting, Nottingham.
Antonov, I. A., & Saleev, V.M. (1979). An economic method of computing LP[tau]-sequences. USSR Computational Mathematics and Mathematical Physics, 19(1), 252–256.
Atkins, D.E., et al. (2003). Revolutionizing science and engineering through cyberinfrastructure. Report of the National Science Foundation Blue-Ribbon Advisory Panel on Cyberinfrastructure. National Science Foundation.
Bell, D., & Ludwig, A. S. (2005). Grid service discovery in the financial markets sector. In 7th international conference on information technology interfaces, Cavtat, Croatia.
Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81, 637–654.
Bratley, P., & Fox, B. L. (1988). Algorithm 659: Implementing Sobol’s quasirandom sequence generator. ACM Transactions on Mathematical Software, 14, 88–100.
Bughin, J., Chui, M., & Manyika, J. (2010). Cloud, big data and smart assets: Ten tech-enabled business trends to watch. McKinsey Quarterly August, 7–8.
Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, M., Fikes, A., Gruber, R.E. (2006). Big table: A distributed storage system for structured data. In OSDI 06: Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation, Seattle, WA, p. 15-15.
Cheng, J., & Druzdzel, M. J. (2000). AIS-BN: An Adaptive importance sampling algorithm for evidential reasoning in large bayesian networks. Journal of Artificial Intelligence Research, 13, 155–188.
Chen, G., Thulasiraman, P., & Thulasiram, R.K. (2006). Distributed quasi-monte carlo algorithm for option pricing on HNOWs using mpC. In Proceedings of the 39th annual simulation symposium (ANSS’06), Huntsville.
Cizek, P., Härdle, W., & Weron, R. (2011). Statistical tools for finance and insurance. Berlin: Springer.
Dean, J., & Ghemawat, S. (2004). Map reduce: Simpli_ed data processing on large clusters. In OSDI 04, San Francisco, CA, pp. 137–150.
Dowd, K. (2002). Measuring market risk. Chichester/England/New York: Wiley.
Efon, B. (1981). Censored data and the bootstrap. Journal of the American Statistical Association, 76(374), 312–319.
Efron, B., & Tibshirani, R. (1986). Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistical Science, 1(1), 54–75.
Faure, H. (1982). Discrepance de Suites Associees a un Systeme de Numeration (en Dimension s). Acta Arithmetica, 41, 337–351.
Foster, I., & Kessleman, C. (2004). The grid: Blueprint for a new computing infrastructure (2nd ed.). Amsterdam: Morgan Kaufmann.
Foster, I., Zhao, Y., Raicu, I., & Lu, S. (2008). Cloud computing and grid computing 360-degree compared. In Grid computing environments workshop, Austin, TX, pp. 1–10.
Galanti, S., & Jung, A. (1997). Low-discrepancy sequences: Monte Carlo simulation of option prices. Journal of Derivatives, 5(1), 63–83.
Ghoshal, D., Canon, R. S., & Ramakrishnan, L. (2011). I/O performance of virtualized cloud environments. In The second international workshop on data intensive computing in the clouds, Seattle, WA, pp 71–80.
Gillam, L., Ahmad, K., & Dear, G. (2005). Grid-enabling social scientists: The Fingrid infrastructure. In Proceedings of the 1st international conference on e-social science, Manchester, pp. 22–24.
Griffiths, P., & Remenyi, D. (2003). Information technology in financial services: A model for value creation. Electronic Journal of Information Systems Evaluation, 6(2), 107–116.
Grimshaw, A. S., & Wulf, W. A. (1997). The legion vision of a worldwide virtual computer. Communications of the ACM, 40(1), 39–45.
Halton, J. H. (1960). On the efficiency of certain quasirandom sequences of points in evaluating multidimensional integrals. Numerische Mathematik, 2, 84–90.
Hauswald, R., & Marquez, R. (2003). Information technology and financial services competition. The Review of Financial Studies, 16(3), 921–948.
Hull, J. C. (2003). Options, futures and other derivatives (5th ed.). Upper Saddle River: Prentice Hall.
Joe, S., & Kuo, F. Y. (2003). Remark on algorithm 659: Implementing Sobol’s quasirandom sequence generator. ACM Transactions on Mathematical Software, 29(1), 49–57.
Korpela, E., et al. (2001). SETI@home-massively distributed computing for SETI. Computing in Science and Engineering, 3(1), 78–83.
Koschnick, T. (2008). Cluster & security: Separating application environments. In Int’l Supercomputing conference and exhibition, Dresden, pp. 17–20.
Kuipers, L., & Niederreiter, H. (1974). Uniform distribution of sequence. New York: Wiley.
Lauret, J., Walker, M., Goasguen, M., & Hajdu, L. (2010). From grid to cloud, the STAR experience. In SciDAC 2010 proceedings, Chattanooga, TN.
Leto, A., et al. (2005). EGRID Project: Experience report on the implementation of a grid infrastructure for the analysis of financial data. In Proceedings of the international conference on information technology: Coding and computing, Las Vegas, NV.
Litzkow, M. J., Livny, M., & Mutka, M. W. (1988). Condor-a hunter of idle workstations. In 8th international conference on distributed computing systems, proceeding, San Jose, CA, pp. 104–111.
Mell, P., Grance, T. (2011). The NIST definition of cloud computing (pp. 800-145). Special Publication.
Milojicic, S. D., et al. (2002). Peer-to-peer computing (HL laboratories research report).
Niederreiter, H. (1992). Random number generation and Quasi-Monte Carlo methods. In CBMS-NSF regional conference series in appl. math. Philadelphia, PA.
Paskov, S. H., & Traub, J. F. (1995). Faster valuation of financial derivatives. Journal of Portfolio Management, 22(1), 113–120.
Phillips, A., et al. (1984). Effects of information technology on financial services systems. Washington, D. C.: U.S. Congress, Office of Technology Assessment, OTA-CIT-202.
Ramakrishnan, L., Canon, R. S., Muriki, K., Sakrejda, I., & Wright, N. J. (2011). Evaluating interconnect and virtualization performance for high performance computing. In Proceedings of 2nd international workshop on performance modeling, benchmarking and simulation of high performance computing systems, Seattle, WA, pp. 1–2.
Schmidt, A. B. (2005). Quantitative finance for physicists: An introduction. Academic Press Advanced Finance Series, Elsevier, Oxford.
Smarr, L., & Catlett, C. (1992). Metacomputing. Communications of the ACM archive, 35(6), 44–52.
Snir, M., et al. (1996). MPI: The complete reference. The MIT Press, Cambridge, Massachusetts.
Sobol, I. M. (1967). On the distribution of points in a cube and the approximate evaluation of integers. U.S.S.R. Computational Mathematics and Mathematical Physics, 7(4), 86–112.
Sobol, I. M., & Levitan, Y. L. (1976). The production of points uniformly distributed in a multidimensional cube. Tech. Rep. 40, Institute of Applied Mathematics, USSR Academy of Sciences (in Russian).
Stokes-Rees, I., et al. (2007). Multi-cluster parallel job submission: Experiences with Monte Carlo simulations for computational finance on Grid5000 (IRIS PicsouGrid Project Report).
Strandell, E., Tilak, S., Chou, H. M., Wang, Y. T., Lin, F. P., Arzberger, P. Fountain, T. (2007). Data management at kenting's underwater ecological observatory. In Proceedings of the third International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia.
Tanaka, S. (2003). Joint development project applies grid computing technology to financial risk management. Nikkei Computer, special section on grid computing.
Tilak, S., Hubbard, P. Miller, M., & Fountain, T. (2007). The ring buffer network bus (RBNB) data turbine streaming data middleware for environmental observing systems. In Proceedings of third IEEE international conference on e-science and grid computing, Bangalore, India, pp. 125–133.
Weron, R. (2004). Handbook of computational statistics. Berlin: Springer.
Yelick, K., Coghlan, S., Draney, B., Canon, R. S. (2011). The magellan report on cloud computing for science. ASCR, DOE. Washington, DC.
Yoon, J. (2008) Korea@Home (PC Grid project in Korea). GridAsia 2008: Singapore.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer Science+Business Media New York
About this entry
Cite this entry
Lin, FP., Lee, CF., Chung, H. (2015). Computer Technology for Financial Service. In: Lee, CF., Lee, J. (eds) Handbook of Financial Econometrics and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7750-1_49
Download citation
DOI: https://doi.org/10.1007/978-1-4614-7750-1_49
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-7749-5
Online ISBN: 978-1-4614-7750-1
eBook Packages: Business and Economics