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ϊ | 2019N426ϊiΰ Fridayj@16:50-18:35 |
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κ | εwεw@oΟw€Θ wpπ¬ i¬z[j1K ζ1Z~i[Ί [n}] |
ρ | Mike So (Hong Kong University of Science and Technology) "Efficient Estimation of High-Dimensional Dynamic Covariance by Risk Factor Mapping: Applications for Financial Risk Management" |
v|(Abstract) | @This paper aims to explore a modified method of high-dimensional dynamic variance-covariance matrix estimation via risk factor mapping, which can yield a dependence estimation of asset returns within a large portfolio with high computational efficiency. The essence of our methodology is to express the time-varying dependence of high-dimensional return variables using the co-movement concept of returns with respect to risk factors. A novelty of the proposed methodology is to allow mapping matrices, which govern the co-movement of returns, to be time-varying. We also consider the exible modeling of risk factors by a copula multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model. Through the proposed risk factor mapping model, the number of parameters and the time complexity are functions of a small number of risk factors instead of the number of stocks in the portfolio, making our proposed methodology highly scalable. We adopt Bayesian methods to estimate unknown parameters and various risk measures in the proposed model. The proposed risk mapping method and financial applications are demonstrated by an empirical study of the Hong Kong stock market. The assessment of the effectiveness of the mapping via risk measure estimation is also discussed. |
ϊ | 2019N524ϊiΰ Fridayj@16:50-18:35 |
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κ | εwεw@oΟw€Θ wpπ¬ i¬z[j1K ζ1Z~i[Ί [n}] |
ρ | ―μGicεw) TBA |
v|(Abstract) | @ |
ϊ | 2019N67ϊiΰ Fridayj@16:50-18:35 |
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κ | εwεw@oΟw€Θ wpπ¬ i¬z[j1K ζ1Z~i[Ί [n}] |
ρ | kΊSκ (Yale University) TBA |
v|(Abstract) | @ |
ϊ | 2019N75ϊiΰ Fridayj@16:50-18:35 |
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κ | εwεw@oΟw€Θ wpπ¬ i¬z[j1K ζ1Z~i[Ί [n}] |
ρ | ΰ_iΦΌw@εwj TBA |
v|(Abstract) | @ |
ϊ | 2019N719ϊiΰ Fridayj@16:50-18:35 |
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κ | εwεw@oΟw€Θ wpπ¬ i¬z[j1K ζ1Z~i[Ί [n}] |
ρ | ιcTκYi»w€j TBA |
v|(Abstract) | @ |
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ϊ | 2019N419ϊiΰ Fridayj@16:50-18:35 |
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κ | εwεw@oΟw€Θ wpπ¬ i¬z[j1K ζ1Z~i[Ί [n}] |
ρ | ³ΎYiwK@εwj "Non-Ignorable Attrition in Pairwise Randomized Experiments" |
v|(Abstract) | @The pairwise randomized experiments enable robust and efficient causal inference. What if outcomes of some units are missing? One way is to delete missing units and calculate difference-in-means (unitwise deletion estimator, UDE). Another method is to delete the other unit in the pair as well (pairwise deletion estimator, PDE). UDE is biased. Some scholars argue that PDE is unbiased, while opponents criticize that PDE is also biased if attrition is non-ignorable and PDE is less efficient than UDE. By using the potential outcome framework, this study formally shows that PDE can be biased but more efficient than UDE; the pairwise variance estimator of PDE is unbiased in the super-population. I argue that it is easier to interpret PDE as a causal effect than UDE. I also propose a new variance estimator. Finally, in order to show how PDE and UDE work, an application is demonstrated. This paper recommends PDE. |