Theory of Stochastic Processes
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS


Theory Stoch. Process.:
Year:
Volume:
Issue:
Page:
Find




Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register

Theory of Stochastic Processes, 2010, Volume 16(32), Issue 1, Pages 44–48 (Mi thsp59)  
Geometric Gaussian martingales with disorder

Omar Glonti, Zaza Khechinashvili

I. Javakhishvili Tbilisi State University, University str., 2
Full-text PDF (125 kB)
References:
PDF
HTML
Abstract: We propose the scheme of a geometric Gaussian martingale with "disorder" as a model of a stock price evolution and investigate the problem of finding a forecasting estimation optimal in mean square sense within this scheme.
Keywords: Geometric Gaussian martingale, disorder moment, optimal forecasting.
Bibliographic databases:
Document Type: Article
MSC: 60G35, 60G42
Language: English
Citation: Omar Glonti, Zaza Khechinashvili, “Geometric Gaussian martingales with disorder”, Theory Stoch. Process., 16(32):1 (2010), 44–48
Citation in format AMSBIB
\Bibitem{GloKhe10}
\by Omar~Glonti, Zaza~Khechinashvili
\paper Geometric Gaussian martingales with disorder
\jour Theory Stoch. Process.
\yr 2010
\vol 16(32)
\issue 1
\pages 44--48
\mathnet{http://mi.mathnet.ru/thsp59}
\mathscinet{http://mathscinet.ams.org/mathscinet-getitem?mr=2779841}
\zmath{https://zbmath.org/?q=an:1224.91187}
Linking options:
  • https://www.mathnet.ru/eng/thsp59
  • https://www.mathnet.ru/eng/thsp/v16/i1/p44
    • Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Theory of Stochastic Processes
    Statistics & downloads:
    Abstract page:151
    Full-text PDF :50
    References:29
     
      Contact us:
    		  Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2024