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Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing

Awasthi, Achal
http://rave.ohiolink.edu/etdc/view?acc_num=osu1534335592622713

Abstract Details

2018, Master of Science, Ohio State University, Statistics.
In this thesis, we propose a generalized Heston model as a tool to estimate volatility. We have used Approximate Bayesian Computing to estimate the parameters of the generalized Heston model. This model was used to examine the daily closing prices of the Shanghai Stock Exchange and the NIKKEI 225 indices. We found that this model was a good fi t for shorter time periods around financial crisis. For longer time periods, this model failed to capture the volatility in detail.
Radu Herbei (Advisor)
Laura Kubatko (Committee Member)
176 p.
Statistics
Stochastic Volatility, Emerging Markets, Approximate Bayesian Computing

Recommended Citations

Citations

  • Awasthi, A. (2018). Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534335592622713

    APA Style (7th edition)

  • Awasthi, Achal. Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing. 2018. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1534335592622713.

    MLA Style (8th edition)

  • Awasthi, Achal. "Parameter Estimation in Stochastic Volatility Models Via Approximate Bayesian Computing." Master's thesis, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1534335592622713

    Chicago Manual of Style (17th edition)

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