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Full-Text Articles in Econometrics
Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang
Testing For Structural Changes In Factor Models Via A Nonparametric Regression, Liangjun Su, Xia Wang
Research Collection School Of Economics
We propose a model-free test for structural changes in factor models. The basic idea is to regress the data on commonly estimated factors by local smoothing and compare the fitted values of time-varying factor loadings with those of time-invariant factor loadings estimated via principal component analysis. By construction, the test is designed to be powerful against both smooth structural changes and sudden structural breaks with a possibly unknown number of breaks and unknown break dates in the factor loadings. No restrictions on the form of alternatives or trimming of boundary regions near the beginning or end of the sample period …
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Approach For The Correlation Matrix, Yingjie Dong, Yiu Kuen Tse
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Approach For The Correlation Matrix, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We apply the factor approach to the correlation matrix to forecast large covariance matrix of asset returns using high-frequency data, using the principal component method to model the underlying latent factors of the correlation matrix. The realized variances are separately forecasted using the Heterogeneous Autoregressive model. The forecasted variances and correlations are then combined to forecast large covariance matrix. Our proposed method is found to perform better in reporting smaller forecast errors than some selected competitors. Empirical application to a portfolio of 100 NYSE and NASDAQ stocks shows that our method provides lower out-of-sample realized variance in selecting global minimum …
Jump Factor Models In Large Cross-Sections, Jia Li, Viktor Todorov, George. Tauchen
Jump Factor Models In Large Cross-Sections, Jia Li, Viktor Todorov, George. Tauchen
Research Collection School Of Economics
We develop tests for deciding whether a large cross-section of asset prices obey an exact factor structure at the times of factor jumps. Such jump dependence is implied by standard linear factor models. Our inference is based on a panel of asset returns with asymptotically increasing cross-sectional dimension and sampling frequency, and essentially no restriction on the relative magnitude of these two dimensions of the panel. The test is formed from the high-frequency returns at the times when the risk factors are detected to have a jump. The test statistic is a cross-sectional average of a measure of discrepancy in …
Rank Tests At Jump Events, Jia Li, Viktor Todorov, George Tauchen, Huidi. Lin
Rank Tests At Jump Events, Jia Li, Viktor Todorov, George Tauchen, Huidi. Lin
Research Collection School Of Economics
We propose a test for the rank of a cross-section of processes at a set of jump events. The jump events are either specific known times or are random and associated with jumps of some process. The test is formed from discretely sampled data on a fixed time interval with asymptotically shrinking mesh. In the first step, we form nonparametric estimates of the jump events via thresholding techniques. We then compute the eigenvalues of the outer product of the cross-section of increments at the identified jump events. The test for rank r is based on the asymptotic behavior of the …
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Correlation Matrix Approach, Yingjie Dong, Yiu Kuen Tse
Forecasting Large Covariance Matrix With High-Frequency Data: A Factor Correlation Matrix Approach, Yingjie Dong, Yiu Kuen Tse
Research Collection School Of Economics
We propose a factor correlation matrix approach to forecast large covariance matrix of asset returns using high-frequency data. We apply shrinkage method to estimate large correlation matrix and adopt principal component method to model the underlying latent factors. A vector autoregressive model is used to forecast the latent factors and hence the large correlation matrix. The realized variances are separately forecasted using the Heterogeneous Autoregressive model. The forecasted variances and correlations are then combined to forecast large covariance matrix. We conduct Monte Carlo studies to compare the finite sample performance of several methods of forecasting large covariance matrix. Our proposed …
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Research Collection School Of Economics
In this paper we study the estimation of a large dimensional factor model when the factor loadingsexhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the third step, we devise a grid search method to estimate the location of the break on each identified interval. …
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
Research Collection School Of Economics
Conventional factor models assume that factor loadings are fixed over a long horizon of time, which appears overly restrictive and unrealistic in applications. In this paper, we introduce a time-varying factor model where factor loadings are allowed to change smoothly over time. We propose a local version of the principal component method to estimate the latent factors and time-varying factor loadings simultaneously. We establish the limiting distributions of the estimated factors and factor loadings in the standard large N and large T framework. We also propose a BIC-type information criterion to determine the number of factors, which can be used …
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Estimation Of Large Dimensional Factor Models With An Unknown Number Of Breaks, Shujie Ma, Liangjun Su
Research Collection School Of Economics
In this paper we study the estimation of a large dimensional factor model when the factor loadings exhibit an unknown number of changes over time. We propose a novel three-step procedure to detect the breaks if any and then identify their locations. In the first step, we divide the whole time span into subintervals and fit a conventional factor model on each interval. In the second step, we apply the adaptive fused group Lasso to identify intervals containing a break. In the third step, we devise a grid search method to estimate the location of the break on each identified …
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang
Research Collection School Of Economics
Conventional factor models assume that factor loadings are fixed over a long horizon of time, which appears overly restrictive and unrealistic in applications. In this paper, we introduce a time-varying factor model where factor loadings are allowed to change smoothly over time. We propose a local version of the principal component method to estimate the latent factors and time-varying factor loadings simultaneously. We establish the limiting distributions of the estimated factors and factor loadings in the standard large N and large T framework. We also propose a BIC-type information criterion to determine the number of factors, which can be used …