Discovering macroeconomic phases using statistical methods.

Economies and financial markets are complex system which can exist in different macroeconomic phases. To build predictive models, we need to know how many such phases there are, and what their statistical properties are. In this report, a recursive segmentation scheme based on the Jensen-Shannon div...

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Main Author: Wong, Jian Cheng.
Other Authors: Lian Heng
Format: Final Year Project
Language:English
Published: 2009
Subjects:
Online Access:http://hdl.handle.net/10356/14733
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-147332023-02-28T23:18:01Z Discovering macroeconomic phases using statistical methods. Wong, Jian Cheng. Lian Heng School of Physical and Mathematical Sciences DRNTU::Science::Mathematics::Statistics Economies and financial markets are complex system which can exist in different macroeconomic phases. To build predictive models, we need to know how many such phases there are, and what their statistical properties are. In this report, a recursive segmentation scheme based on the Jensen-Shannon divergence is employed to extract statistical signatures of different market phases from the Dow Jones Industrial Average half-hourly time series from January 1997 to March 2007. Each stationary segment of the segmented time series is described by a different Gaussian model. We find a total of 102 segments, and these segments can be grouped into five clusters by hierarchical clustering. There is a low volatility phase that can be associated with expansion, a high volatility phase within which economic contraction occurs, a moderate volatility correction phase, an extremely high volatility crash phase, and also an extremely-low-volatility phase whose macroeconomic nature is not yet understood. The market is predominantly found in the low and high volatility phases. These are interrupted by the correction phase. Moreover, the crash phase is mostly found within the high volatility phase. Transition from low to high and high to low occurs over a period of roughly one year. Bachelor of Science in Mathematical Sciences 2009-01-21T08:31:22Z 2009-01-21T08:31:22Z 2008 2008 Final Year Project (FYP) http://hdl.handle.net/10356/14733 en 38 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Science::Mathematics::Statistics
spellingShingle DRNTU::Science::Mathematics::Statistics
Wong, Jian Cheng.
Discovering macroeconomic phases using statistical methods.
description Economies and financial markets are complex system which can exist in different macroeconomic phases. To build predictive models, we need to know how many such phases there are, and what their statistical properties are. In this report, a recursive segmentation scheme based on the Jensen-Shannon divergence is employed to extract statistical signatures of different market phases from the Dow Jones Industrial Average half-hourly time series from January 1997 to March 2007. Each stationary segment of the segmented time series is described by a different Gaussian model. We find a total of 102 segments, and these segments can be grouped into five clusters by hierarchical clustering. There is a low volatility phase that can be associated with expansion, a high volatility phase within which economic contraction occurs, a moderate volatility correction phase, an extremely high volatility crash phase, and also an extremely-low-volatility phase whose macroeconomic nature is not yet understood. The market is predominantly found in the low and high volatility phases. These are interrupted by the correction phase. Moreover, the crash phase is mostly found within the high volatility phase. Transition from low to high and high to low occurs over a period of roughly one year.
author2 Lian Heng
author_facet Lian Heng
Wong, Jian Cheng.
format Final Year Project
author Wong, Jian Cheng.
author_sort Wong, Jian Cheng.
title Discovering macroeconomic phases using statistical methods.
title_short Discovering macroeconomic phases using statistical methods.
title_full Discovering macroeconomic phases using statistical methods.
title_fullStr Discovering macroeconomic phases using statistical methods.
title_full_unstemmed Discovering macroeconomic phases using statistical methods.
title_sort discovering macroeconomic phases using statistical methods.
publishDate 2009
url http://hdl.handle.net/10356/14733
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