Derivative Exposure and the Interest Rate and Exchange Rate Risks of U.S. Banks

This paper estimates the interest rate and exchange rate risk betas of fifty-nine large U. S. commercial banks for the period of 1975-1992, as well as the bank-specific determinants of these betas. The estimation procedure uses a modified seemingly unrelated simultaneous method that recognizes cross-equation dependencies and adjusts for serial correlation and heteroskedasticity. Overall, the exchange rate risk betas are more significant than the interest rate risk betas. More importantly, we find a link between the scale of a bank's interest rate and currency derivative contracts and the bank's interest rate and exchange rate risks. Particularly noteworthy is the influence of currency derivatives on exchange rate betas.Off-balance sheet, Bank risk Derivatives, Interest rate risk, Exchange risk exposure JEL classification: G2, Gl, F3

Human Motion Prediction via Learning Local Structure Representations and Temporal Dependencies

Human motion prediction from motion capture data is a classical problem in the computer vision, and conventional methods take the holistic human body as input. These methods ignore the fact that, in various human activities, different body components (limbs and the torso) have distinctive characteristics in terms of the moving pattern. In this paper, we argue local representations on different body components should be learned separately and, based on such idea, propose a network, Skeleton Network (SkelNet), for long-term human motion prediction. Specifically, at each time-step, local structure representations of input (human body) are obtained via SkelNet's branches of component-specific layers, then the shared layer uses local spatial representations to predict the future human pose. Our SkelNet is the first to use local structure representations for predicting the human motion. Then, for short-term human motion prediction, we propose the second network, named as Skeleton Temporal Network (Skel-TNet). Skel-TNet consists of three components: SkelNet and a Recurrent Neural Network, they have advantages in learning spatial and temporal dependencies for predicting human motion, respectively; a feed-forward network that outputs the final estimation. Our methods achieve promising results on the Human3.6M dataset and the CMU motion capture dataset.Comment: Accepted by AAAI19; Updated with the open source lin

Financial Crisis and Risk Management: Reassessing the Asian Financial Crisis in Light of the American Financial Crisis

No abstract available

Pooling Faces: Template based Face Recognition with Pooled Face Images

We propose a novel approach to template based face recognition. Our dual goal is to both increase recognition accuracy and reduce the computational and storage costs of template matching. To do this, we leverage on an approach which was proven effective in many other domains, but, to our knowledge, never fully explored for face images: average pooling of face photos. We show how (and why!) the space of a template's images can be partitioned and then pooled based on image quality and head pose and the effect this has on accuracy and template size. We perform extensive tests on the IJB-A and Janus CS2 template based face identification and verification benchmarks. These show that not only does our approach outperform published state of the art despite requiring far fewer cross template comparisons, but also, surprisingly, that image pooling performs on par with deep feature pooling.Comment: Appeared in the IEEE Computer Society Workshop on Biometrics, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June, 201

CoDR: Correlation-based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data

A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily become more than tens of GB per day, forming a significant hurdle due to the storage and network cost. To address this problem, this paper proposes a novel scheme, called CoDR, which can reduce data volume by considering the correlations among heterogeneous driving data. Among heterogeneous datasets, CoDR first chooses one set as a pivot data. Then, according to the objective of data collection, it identifies data ranges relevant to the objective from the pivot dataset. Finally, it investigates correlations among sets, and reduces data volume by eliminating irrelevant data from not only the pivot set but also other remaining datasets. CoDR gathers four heterogeneous driving datasets: two videos for front view and driver behavior, OBD-II and GPS data. We show that CoDR decreases data volume by up to 91%. We also present diverse analytical results that reveal the correlations among the four datasets, which can be exploited usefully for edge computing to reduce data volume on the spot. Document type: Articl

Business Groups and Corporate Social Responsibility

© 2018, Springer Science+Business Media B.V., part of Springer Nature. There is a growing literature on corporate social responsibility (CSR), but few have focused on the implications of business groups for CSR. We examine the antecedents and outcomes of CSR behaviors of group firms in Korea. We find that group affiliation is associated with higher CSR overall and for its major societal and environmental components. However, the ownership disparity between cash flow and control by controlling inside shareholders is associated with lower CSR, consistent with opportunistic rent expropriation theory. We further find that CSR initiatives can impact group firms positively in the event of bad events, consistent with insurance theory. This motive for CSR as a means of enhancing reputation capital to buffer the bad events is pronounced for group firms because of group-wide dissemination of negative reputational externality