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Four essays on the equity market: D...
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Chen, Chen.
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Four essays on the equity market: Decimalization, volatility, indexation and cross-sectional returns.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Four essays on the equity market: Decimalization, volatility, indexation and cross-sectional returns./
作者:
Chen, Chen.
面頁冊數:
157 p.
附註:
Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6706.
Contained By:
Dissertation Abstracts International66-12B.
標題:
Statistics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3199850
ISBN:
9780542465277
Four essays on the equity market: Decimalization, volatility, indexation and cross-sectional returns.
Chen, Chen.
Four essays on the equity market: Decimalization, volatility, indexation and cross-sectional returns.
- 157 p.
Source: Dissertation Abstracts International, Volume: 66-12, Section: B, page: 6706.
Thesis (Ph.D.)--University of Illinois at Chicago, 2005.
This dissertation examines four features of equity markets: indexation, volatility, cross-sectional returns, and decimalization. The first part of this research is motivated by the biased approximation of prices to fundamental values used in portfolio constructions. The empirical comparison in literature shows that estimated fundamentals-based portfolios outperform standard capitalization-weighted portfolios. Instead of using accounting data to estimate a stock's fundamental value, a simple and effective statistical method is proposed in this part to estimate a stock's fundamental value by smoothing the stock's noisy price time series. Expressions for the expected returns of standard market cap weighted and fundamentals-based portfolios are presented under various assumptions about the change in fundamentals. The empirical results show that statistical estimated fundamentals work as well as accounting estimated fundamentals in outperforming the standard indices. The second part considers volatility forecasting. The intraday and overnight volatility are viewed as two dependent states in a non-linear state space model. They are estimated and forecasted jointly in a bivariate stochastic volatility (SV) model framework using Markov Chain Monte Carlo (MCMC) method. A new measurement is proposed to assess volatility forecasting performance. It is based on the profitability of a trading strategy based on volatility forecast if there exists a market trading realized volatility. The time series analysis of the cross sectional returns is in the third part. The time-changing distribution of the cross sectional returns is estimated by a generalized skew t-distribution. In addition, the properties of the cross sectional returns are described in the form of quantile series. The fourth part, is about the effect of decimalization of US securities trading system. The dynamic structure of the intraday bid and ask prices is introduced as a framework to examine the effect of the reduction in the minimum tick size. The Sequential Monte Carlo (SMC) method is utilized to easily and efficiently obtain on-line estimations of market efficient price and market exposure costs. Before and after analysis demonstrates that when the stock market moves from a fractional to a decimal trading system, the variance components due to market-making exposure costs decline significantly, while the variance component due to public news remains unchanged. In addition, the market maker's profitable gap is narrowed.
ISBN: 9780542465277Subjects--Topical Terms:
517247
Statistics.
Four essays on the equity market: Decimalization, volatility, indexation and cross-sectional returns.
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This dissertation examines four features of equity markets: indexation, volatility, cross-sectional returns, and decimalization. The first part of this research is motivated by the biased approximation of prices to fundamental values used in portfolio constructions. The empirical comparison in literature shows that estimated fundamentals-based portfolios outperform standard capitalization-weighted portfolios. Instead of using accounting data to estimate a stock's fundamental value, a simple and effective statistical method is proposed in this part to estimate a stock's fundamental value by smoothing the stock's noisy price time series. Expressions for the expected returns of standard market cap weighted and fundamentals-based portfolios are presented under various assumptions about the change in fundamentals. The empirical results show that statistical estimated fundamentals work as well as accounting estimated fundamentals in outperforming the standard indices. The second part considers volatility forecasting. The intraday and overnight volatility are viewed as two dependent states in a non-linear state space model. They are estimated and forecasted jointly in a bivariate stochastic volatility (SV) model framework using Markov Chain Monte Carlo (MCMC) method. A new measurement is proposed to assess volatility forecasting performance. It is based on the profitability of a trading strategy based on volatility forecast if there exists a market trading realized volatility. The time series analysis of the cross sectional returns is in the third part. The time-changing distribution of the cross sectional returns is estimated by a generalized skew t-distribution. In addition, the properties of the cross sectional returns are described in the form of quantile series. The fourth part, is about the effect of decimalization of US securities trading system. The dynamic structure of the intraday bid and ask prices is introduced as a framework to examine the effect of the reduction in the minimum tick size. The Sequential Monte Carlo (SMC) method is utilized to easily and efficiently obtain on-line estimations of market efficient price and market exposure costs. Before and after analysis demonstrates that when the stock market moves from a fractional to a decimal trading system, the variance components due to market-making exposure costs decline significantly, while the variance component due to public news remains unchanged. In addition, the market maker's profitable gap is narrowed.
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