Abstract Details
Activity Number:
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112
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 4, 2014 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Risk Analysis
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Abstract #313052
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Title:
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Statistical Methods for Large Portfolio Risk Management
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Author(s):
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Jian Zou*+
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Companies:
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Indiana University-Purdue University Indianapolis
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Keywords:
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High-frequency ;
volatility modeling ;
portfolio allocation ;
risk management
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Abstract:
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The field of high-frequency finance has experienced a rapid evolvement over the past few decades. One focus point is volatility modeling and analysis for high-frequency financial data. Volatility analysis plays a central role in modern finance. The abundance of high-frequency financial data stimulates the study of price and volatility movements over a relatively short period of time to reflect the current market dynamics more accurately. It allows us to employ flexible nonparametric methodologies for the volatility study based on high-frequency returns directly. It makes possible to estimate the vast volatility matrix of a large number of assets. On the other hand, high-frequency financial data pose a set of new challenges for researchers and practitioners. Observed high-frequency financial data exhibit complex features and structures, such as micro-structure noise, non-sychronization, irregularly spaced times between observations, non-stationarity and jumps. In this talk, we introduce a new methodology to carry out efficient asset allocations using regularization on estimated integrated volatility via intra-day high-frequency data. We illustrate the methodology by comparing the re
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Authors who are presenting talks have a * after their name.
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