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Physical Sciences and Mathematics Commons™
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- Bi-power Variation (1)
- Early Warning (1)
- Financial Market Risk Management (1)
- GARCH Family Models (1)
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- Jump Detection (1)
- Kurtosis and Volatility Estimation (1)
- Leverage Effect (1)
- Limit Order Book (1)
- Loan performance (1)
- Markov chain (1)
- Microstructure Noise (1)
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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Estimation Of A Noisy Subordinated Brownian Motion Via Two-Scales Power Variations, José E. Figueroa-López, Kiseop Lee
Estimation Of A Noisy Subordinated Brownian Motion Via Two-Scales Power Variations, José E. Figueroa-López, Kiseop Lee
Mathematics Faculty Publications
High frequency based estimation methods for a semiparametric pure-jump subordinated Brownian motion exposed to a small additive microstructure noise are developed building on the two-scales realized variations approach originally developed by Zhang et al. (2005) for the estimation of the integrated variance of a continuous Itô process. The proposed estimators are shown to be robust against the noise and, surprisingly, to attain better rates of convergence than their precursors, method of moment estimators, even in the absence of microstructure noise. Our main results give approximate optimal values for the number K of regular sparse subsamples to be used, which is …
Mortgage Transition Model Based On Loanperformance Data, Shuyao Yang
Mortgage Transition Model Based On Loanperformance Data, Shuyao Yang
Arts & Sciences Electronic Theses and Dissertations
The unexpected increase in loan default on the mortgage market is widely considered to be one of the main cause behind the economic crisis. To provide some insight on loan delinquency and default, I analyze the mortgage performance data from Fannie Mae website and investigate how economic factors and individual loan and borrower information affect the events of default and prepaid. Various delinquency status including default and prepaid are treated as discrete states of a Markov chain. One-step transition probabilities are estimated via multinomial logistic models. We find that in general current loan-to-value ratio, credit score, unemployment rate, and interest …
Statistical Analysis Of Markovian Queueing Models Of Limit Order Books, Yiyao Luo
Statistical Analysis Of Markovian Queueing Models Of Limit Order Books, Yiyao Luo
Arts & Sciences Electronic Theses and Dissertations
The objective of this thesis is to investigate the suitability of some Markovian queueing models in being able to effectively describe the dynamical properties of a limit order book more specifically. We review and compare the assumptions proposed by Huang et al.[Quantitative Finance,12,547-557(2012)] and Cont et al.[SIAM Journal for Financial Mathematics,4,1- 25(2013)], and estimate the intensity parameters in both ways, based on real data of a stock on the Nasdaq Stock Market. Trough comparing by cumulative distribution functions of first-passage time to state 0, we will hsow that the estimators of Cont’s model fit our data better and we put …
On Post-Selection Confidence Intervals In Linear Regression, Xinwei Zhang
On Post-Selection Confidence Intervals In Linear Regression, Xinwei Zhang
Arts & Sciences Electronic Theses and Dissertations
The general goal of this thesis is to investigate and examine some issues about post-selection inference which arises from the setting where statistical inference is carried out after a datadriven model selection step. In this setting, the classical inference theory which requires a fixed priori model becomes invalid since the selected model is a result of random event. Hence, a common practice in applied research which ignores the model selection and builds up confidence interval will result in misleading or even false conclusion. In this thesis, specifically, we first discusses some examples to show how the classical inference theory loses …
Market Risk Management For Financial Institutions Based On Garch Family Models, Qiandi Chen
Market Risk Management For Financial Institutions Based On Garch Family Models, Qiandi Chen
Arts & Sciences Electronic Theses and Dissertations
The financial stock market turned out to rise and fall suddenly and sharply in recent years, which means that volatility and uncertainty is very significant in market and measuring the market risk accurately is of great importance. I collect the historical close price of S&P 500 Financials Sector Index from January 19th 2011 to January 31st 2017, and use the daily logarithm yield as time series data to build 2 ARMA models and 5 GARCH family models using t-distribution. Then I calculate future 10 days’ relative VAR in 1-day horizon under 99\% confidence level based on the selected model. E-GARCH …
Statistical Models To Predict Popularity Of News Articles On Social Networks, Ziyi Liu
Statistical Models To Predict Popularity Of News Articles On Social Networks, Ziyi Liu
Arts & Sciences Electronic Theses and Dissertations
Social networks have changed the way that we obtain information. Content creators and, specifically news article authors, have in interest in predicting the popularity of content, in terms of the number of shares, likes, and comments across various social media platforms. In this thesis, I employ several statistical learning methods for prediction. Both regression-based and classification-based methods are compared according to their predictive ability, using a database from the UCI Machine Learning Repository.
Statistical Analysis Of The Price Jumps Of Financial Assets Based On Lob Data, Ying Zhuang
Statistical Analysis Of The Price Jumps Of Financial Assets Based On Lob Data, Ying Zhuang
Arts & Sciences Electronic Theses and Dissertations
The price process in electronic markets is one prototypical example of a stochastic process, and it has historically be fitted and analyzed using different stochastic models such as Levy processes, diffusions, and SDEs (stochastic differential equations). In this thesis, we analyze Microsoft stock data in 2014-11-03 with the goal of studying the presence of jumps based on Limit Order Book (LOB) data. To this end, we divide the whole day’s data into many consecutive intervals and proceed to apply a jump detection method to identify the intervals that could potentially have jumps. After obtaining the intervals with potential jumps, we …