Browsing by Author "Lin, Edward M. H."

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A Three-Parameter Speeded Item Response Model: Estimation and Application
(2016-01-01)
Chang, Joyce
;
Tsai, Henghsiu
;
Su, Ya-Hui
;
Lin, Edward M. H.
;
資訊管理與財務金融系註：原資管所+財金所
When given time constraints, it is possible that examinees leave the harder items till later and are not able to finish answering every item in time. In this paper, this situation is modeled by incorporating a speeded-effect term into a three-parameter logistic item response model. Due to the complexity of the likelihood structure, a Bayesian estimation procedure with Markov chain Monte Carlo method is presented. The methodology is applied to physics examination data of the Department Required Test for college entrance in Taiwan for illustration.
DOUBLY CONSTRAINED FACTOR MODELS WITH APPLICATIONS
(2016-10)
Tsai, Henghsiu
;
Tsay, Ruey S.
;
Lin, Edward M. H.
;
Cheng, Ching-Wei
;
資訊管理與財務金融系註：原資管所+財金所
This paper focuses on factor analysis of multivariate time series. We propose statistical methods that enable analysts to leverage their prior knowledge or substantive information to sharpen the estimation of common factors. Specifically, we consider a doubly constrained factor model that enables analysts to specify both row and column constraints of the data matrix to improve the estimation of common factors. The row constraints may represent classifications of individual subjects whereas the column constraints may show the categories of variables. We derive both the maximum likelihood and least squares estimates of the proposed doubly constrained factor model and use simulations to study the performance of the analysis in finite samples. The Akaike information criterion is used for model selection. Monthly U.S. housing start data from nine geographical divisions are used to demonstrate the application of the proposed model.
DOUBLY CONSTRAINED FACTOR MODELS WITH APPLICATIONS
(2016-10-01)
Tsai, Henghsiu
;
Tsay, Ruey S.
;
Lin, Edward M. H.
;
Cheng, Ching-Wei
;
資訊管理與財務金融系註：原資管所+財金所
This paper focuses on factor analysis of multivariate time series. We propose statistical methods that enable analysts to leverage their prior knowledge or substantive information to sharpen the estimation of common factors. Specifically, we consider a doubly constrained factor model that enables analysts to specify both row and column constraints of the data matrix to improve the estimation of common factors. The row constraints may represent classifications of individual subjects whereas the column constraints may show the categories of variables. We derive both the maximum likelihood and least squares estimates of the proposed doubly constrained factor model and use simulations to study the performance of the analysis in finite samples. The Akaike information criterion is used for model selection. Monthly U.S. housing start data from nine geographical divisions are used to demonstrate the application of the proposed model.
Inference of Seasonal Long-memory Time Series with Measurement Error
(2015-03-01)
Tsai, Henghsiu
;
Rachinger, Heiko
;
Lin, Edward M. H.
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
We consider the Whittle likelihood estimation of seasonal autoregressive fractionally integrated moving-average models in the presence of an additional measurement error and show that the spectral maximum Whittle likelihood estimator is asymptotically normal. We illustrate by simulation that ignoring measurement errors may result in incorrect inference. Hence, it is pertinent to test for the presence of measurement errors, which we do by developing a likelihood ratio (LR) test within the framework of Whittle likelihood. We derive the non-standard asymptotic null distribution of this LR test and the limiting distribution of LR test under a sequence of local alternatives. Because in practice, we do not know the order of the seasonal autoregressive fractionally integrated moving-average model, we consider three modifications of the LR test that takes model uncertainty into account. We study the finite sample properties of the size and the power of the LR test and its modifications. The efficacy of the proposed approach is illustrated by a real-life example.
Systemic risk, financial markets, and performance of financial institutions
(2018-03-01)
Lin, Edward M. H.
;
Sun, Edward W.
;
Yu, Min-Teh
;
交大名義發表
;
National Chiao Tung University
This paper studies the exposure and contribution of financial institutions to systemic risks in financial markets. We employ three popular indicators of a financial institution's exposure to systemic risks: the systemic risk index (SRISK) and marginal expected shortfall (MES) of Brownlees and Engle (Volatility, correlation and tails for systemic risk measurement, Social Science Research Network, Rochester, NY, 2012) and the conditional Value-at-Risk (CoVaR) of Adrian and Brunnermeier (2011). We use a primary database of Taiwan financial institutions for our empirical study. A panel contains data of stock market returns and balance sheets of 31 Taiwan financial institutions for 2005-2014. We focus on systemic risk analysis so as to understand the dynamics of volatility, interdependency, and risk during the recent financial crisis. We then report the time series dynamics and cross sectional rankings of these systemic risk measures. The main results indicate that although these three measures differ in their definition of the contributions to systemic risk, all are quite similar in identifying systemically important financial institutions (SIFIs). Moreover, we find empirical evidence that systemic risk contributions are closely related to certain institution characteristic factors. The results of the Granger causality tests prove that a systemic risk measure is a great alternative tool for monitoring early warning signals of distress in the real economy.
Systemic risk, interconnectedness, and non-core activities in Taiwan insurance industry
(2018-05-01)
Chang, Carolyn W.
;
Li, Xiaodan
;
Lin, Edward M. H.
;
Yu, Min-Teh
;
交大名義發表
We employ three measures- marginal expected shortfall, SRISK index, and Conditional Value-at-Risk- to assess an insurer's exposure and contribution to systemic risk during the period 2005-2015. We then examine the primary factors posing the systemic risk and find that both non-core activities and interconnectedness of insurers are the significant and primary drivers. Our results differ from the findings in the U.S. insurance sector where firm size is the primary driver of insurers' exposure and contribution to systemic risk, as the findings in the global insurance sector where leverage is a significant driver of insurers' exposure, and both leverage and non-core activities are primary drivers of insurers' contribution to systemic risk. Our results further indicate that the size and significance of the interconnectedness effect on systemic risk increase as the share of insurance sector within the financial industry rises.