Modelling FX smile : from stochastic volatility to skewness

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Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks

Taking a photo outside, can we predict the immediate future, e.g., how would the cloud move in the sky? We address this problem by presenting a generative adversarial network (GAN) based two-stage approach to generating realistic time-lapse videos of high resolution. Given the first frame, our model learns to generate long-term future frames. The first stage generates videos of realistic contents for each frame. The second stage refines the generated video from the first stage by enforcing it to be closer to real videos with regard to motion dynamics. To further encourage vivid motion in the final generated video, Gram matrix is employed to model the motion more precisely. We build a large scale time-lapse dataset, and test our approach on this new dataset. Using our model, we are able to generate realistic videos of up to $128\times 128$ resolution for 32 frames. Quantitative and qualitative experiment results have demonstrated the superiority of our model over the state-of-the-art models.Comment: To appear in Proceedings of CVPR 201

Forecasting Australian GDP Growth Using Coefficients Constrained by A Term Structure Model

Yield spread between long and short bonds has been used to forecast economic activity for a long time and has yielded some positive results, particularly for the U.S. data. Recently it has been shown that the forecast can be improved by incorporating the economic activity variable into a term structure model with observable factors. The idea is to constrain the parameters of the system by the term structure model and see whether the constrained model produces better forecasts for the economic activity. This has been done for the U.S. We test this model on Australian GDP growth. We find the forecast results using constrained parameters are quite poor compared to those for the unconstrained model. The reason is that in the traditional affine yield model, researchers normally assume a mean reverting process for factors such as short rate. When this is not supported by the data, the forecast results could be quite poor. To overcome this problem, one might want to twist the affine factor model so that it can accommodate non-mean reverting processes for factors such as the sTerm Structure, VAR and GDP Growth

HySIM: A Hybrid Spectrum and Information Market for TV White Space Networks

We propose a hybrid spectrum and information market for a database-assisted TV white space network, where the geo-location database serves as both a spectrum market platform and an information market platform. We study the inter- actions among the database operator, the spectrum licensee, and unlicensed users systematically, using a three-layer hierarchical model. In Layer I, the database and the licensee negotiate the commission fee that the licensee pays for using the spectrum market platform. In Layer II, the database and the licensee compete for selling information or channels to unlicensed users. In Layer III, unlicensed users determine whether they should buy the exclusive usage right of licensed channels from the licensee, or the information regarding unlicensed channels from the database. Analyzing such a three-layer model is challenging due to the co-existence of both positive and negative network externalities in the information market. We characterize how the network externalities affect the equilibrium behaviours of all parties involved. Our numerical results show that the proposed hybrid market can improve the network profit up to 87%, compared with a pure information market. Meanwhile, the achieved network profit is very close to the coordinated benchmark solution (the gap is less than 4% in our simulation).Comment: This manuscript serves as the online technical report of the article published in IEEE International Conference on Computer Communications (INFOCOM), 201

Clothing Co-Parsing by Joint Image Segmentation and Labeling

This paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. The first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (E-SVM) technique [23]. In the second phase (i.e. "region co-labeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89% recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods.Comment: 8 pages, 5 figures, CVPR 201