Skip to main content
  • 370 Accesses

Abstract

In the first 5 min (9:30–9:35 AM EST) after the market opened on Friday, June 24, 2016, the trading volume of the Dow Jones Industrial Average reached 5.71 million shares; by the closing minute (4:00 PM), the volume was over 63 million shares (Table 2.1). Over the course of the day, a total of 5.2 million trades were processed by the New York Stock Exchange (NYSE), and over five million of these were small trades of 1–2000 shares.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Data were gathered using http://finance.yahoo.com/echarts?s=%5EDJI+Interactive#{“range”:“1d”,“allowChartStacking”:true}

  2. 2.

    Data were taken directly from the NYSE website on June 24th, 2016. Data can be retrieved from: http://www.nyxdata.com/Data-Products/NYSE-Volume-Summary#summaries

  3. 3.

    For example, if the current interest rate is 2.5% and interest rate changes by.25% at a time, one step up would be 2.75%, and one step down would be 2.25%.

  4. 4.

    This is referred to as “equifinality” (Bertalanffy 1969)

References

  • C. Alexander, Volatility and correlation: Measurement, models and applications. Risk. Manag. Anal. 1, 125–171 (1998)

    Google Scholar 

  • E. Angel, E. Bampis, F. Pascual, Truthful algorithms for scheduling selfish tasks on parallel machines. Theor. Comput. Sci. 369(1), 157–168 (2006)

    Article  MathSciNet  Google Scholar 

  • M.J. Bach, The design of the UNIX operating system (Prentice-Hall, Inc, New Delhi, 1986a)

    Google Scholar 

  • M.J. Bach, The design of the UNIX operating system, vol 5 (Prentice-Hall, Englewood Cliffs, 1986b)

    Google Scholar 

  • K.R. Baker, D. Trietsch, Principles of sequencing and scheduling (Wiley, New Jersey, 2013)

    MATH  Google Scholar 

  • J. Bentham, An introduction to the principles of morals and legislation (Clarendon Press, Oxford, 1879)

    Google Scholar 

  • L.v. Bertalanffy, General system theory; foundations, development, applications (G. Braziller, New York, 1969)

    Google Scholar 

  • J. Chong, K. Keutzer, M.F. Dixon, Acceleration of market value-at-risk estimation. Available at SSRN 1576402 (2009)

    Google Scholar 

  • R.W. Conway, W.L. Maxwell, L.W. Miller, Theory of scheduling (Courier Corporation, NewYork, 2012)

    MATH  Google Scholar 

  • P. Glasserman, P. Heidelberger, P. Shahabuddin, Efficient monte carlo methods for value-at-risk, (2010)

    Google Scholar 

  • P. Jorion, Value at risk (McGraw-Hill, New York, 1997)

    Google Scholar 

  • J. Kay, P. Lauder, A fair share scheduler. Commun. ACM. 31(1), 44–55 (1988)

    Article  Google Scholar 

  • R. B. Krishnamurthy, I. Chin, A. Chinnapatlolla, Exploration of parallelization frameworks for computational finance. Paper presented at the Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA) (2012)

    Google Scholar 

  • R. D. Luce, H. Raiffa, Games and decisions: Introduction and critical survey. (Courier Corporation 2012)

    Google Scholar 

  • V. Pareto, Manuale di economia politica con una introduzione alla scienza sociale (manual of political economy) (Societa Editrice Libraria, Milano, 1919)

    Google Scholar 

  • R. G. Parker, Deterministic scheduling theory. (CRC Press, 1996)

    Google Scholar 

  • C. Reyes, K. Walters, W. Yang, Monte carlo within a day. Paper presented at the quantitative analysis in financial markets: Collected papers of the New York university mathematical finance seminar (2001)

    Google Scholar 

  • A. Sedighi, Y. Deng, P. Zhang, Fariness of task scheduling in high performance computing environments. Scalable Computing: Pract. Experience 15(3), 273–285 (2014). https://doi.org/10.12694/scpe.v15i3.1020

    Article  Google Scholar 

  • A. Sedighi, M. Smith, Y, Deng, (2017a, November 3rd–5th 2017). An evaluation of optimizing for FUD in scheduling for shared computing environments. Paper presented at the 2nd IEEE International Conference on Smart Cloud (SmartCloud 2017), New York

    Google Scholar 

  • A. Sedighi, M. Smith, Y. Deng, FUD – Balancing scheduling parameters in shared computing environments. Paper presented at the 4th IEEE International Conference on Cyber Security and Cloud Computing (IEEE CSCloud 2017) (New York 2017b)

    Google Scholar 

  • S. Soltesz, H. Pötzl, M. E. Fiuczynski, A. Bavier, L. Peterson, Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors. Paper presented at the ACM SIGOPS operating systems review (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sedighi, A., Smith, M. (2019). Financial Market Risk. In: Fair Scheduling in High Performance Computing Environments. Springer, Cham. https://doi.org/10.1007/978-3-030-14568-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-14568-2_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14567-5

  • Online ISBN: 978-3-030-14568-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics