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UBC Theses and Dissertations
A flexible inference method for an autoregressive stochastic volatility model with an application to risk management Xie, Yijun
Abstract
The Autoregressive Stochastic Volatility (ARSV) model is a discrete-time stochastic volatility model that can model the financial returns time series and volatilities. This model is relevant for risk management. However, existing inference methods have various limitations on model assumptions. In this report we discuss a new inference method that allows flexible model assumption for innovation of the ARSV model. We also present the application of ARSV model to risk management, and compare the ARSV model with another commonly used model for financial time series, namely the GARCH model.
Item Metadata
| Title |
A flexible inference method for an autoregressive stochastic volatility model with an application to risk management
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| Creator | |
| Publisher |
University of British Columbia
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| Date Issued |
2017
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| Description |
The Autoregressive Stochastic Volatility (ARSV) model is a discrete-time stochastic volatility model that can model the financial returns time series and volatilities. This model is relevant for risk management. However, existing inference methods have various limitations on model assumptions. In this report we discuss a new inference method that allows flexible model assumption for innovation of the ARSV
model. We also present the application of ARSV model to risk management, and
compare the ARSV model with another commonly used model for financial time
series, namely the GARCH model.
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| Genre | |
| Type | |
| Language |
eng
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| Date Available |
2017-04-20
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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| DOI |
10.14288/1.0344013
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| URI | |
| Degree | |
| Program | |
| Affiliation | |
| Degree Grantor |
University of British Columbia
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| Graduation Date |
2017-05
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| Campus | |
| Scholarly Level |
Graduate
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International