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Modeling Intra-day Markets with an a...

Chai, Yikang.

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Modeling Intra-day Markets with an application of Risk Management and Optimal Order Execution.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling Intra-day Markets with an application of Risk Management and Optimal Order Execution./
作者:	Chai, Yikang.
面頁冊數:	82 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
Contained By:	Dissertation Abstracts International75-11B(E).
標題:	Applied Mathematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3629447
ISBN:	9781321063943

Modeling Intra-day Markets with an application of Risk Management and Optimal Order Execution.
Chai, Yikang.

Modeling Intra-day Markets with an application of Risk Management and Optimal Order Execution. - 82 p.

Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2014.

This item must not be sold to any third party vendors.

Financial time series data exhibits heavy tailed, volatility clustering and long range dependence style facts. Traditional Gaussian distribution assumption based model failed to explain these phenomena. A unified framework model proposed in this thesis, fractionally integrated autoregressive moving average (FARIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) with multivariate generalized hyperbolic distribution (MGHD), trying to capture all these phenomena together. We also examined this model by using intra-day market dataset to backtest of various risk measure. With rise of high frequency trading and algorithm trading in recent years, trading volume hugely increased and markets became more volatile. Order execution is the main concern for traders, especially in the case of liquidation of big orders. We illustrate how the optimal order execution strategy behaves under the assumption that market price dynamics follows high volatile (non-Gaussian) markets with volatility clustering and log-range dependence characteristics.

ISBN: 9781321063943Subjects--Topical Terms:

1669109
Applied Mathematics.

Modeling Intra-day Markets with an application of Risk Management and Optimal Order Execution.
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500 $a Advisers: Svetlozar Rachev; James Glimm.
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506 $a This item must not be added to any third party search indexes.
520 $a Financial time series data exhibits heavy tailed, volatility clustering and long range dependence style facts. Traditional Gaussian distribution assumption based model failed to explain these phenomena. A unified framework model proposed in this thesis, fractionally integrated autoregressive moving average (FARIMA) and fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) with multivariate generalized hyperbolic distribution (MGHD), trying to capture all these phenomena together. We also examined this model by using intra-day market dataset to backtest of various risk measure. With rise of high frequency trading and algorithm trading in recent years, trading volume hugely increased and markets became more volatile. Order execution is the main concern for traders, especially in the case of liquidation of big orders. We illustrate how the optimal order execution strategy behaves under the assumption that market price dynamics follows high volatile (non-Gaussian) markets with volatility clustering and log-range dependence characteristics.
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