The Economics of CSI300 Stock Index Futures in China

Chinese financial markets play an ever more pertinent role within the global economic. In this thesis, we investigate empirically the efficiency and functioning of the Chinese Security Index 300 (CSI300) index future. While CSI300 index futures market is a relatively new market, it has attracted huge trading volume and liquidity as there is no other financial derivatives markets in China and the short-selling in the stock market is difficult. Therefore, it is important and informative to examine both the hedging effectiveness and price discovery ability of CSI300 stock index futures. This thesis presents one of the first attempts in empirically investigate the market efficiency and hedging effective of the Chinese stock index futures from 2012 to 2018. In particular, chapter 2 studies the hedging effectiveness of CSI300 index futures with both static and dynamic hedging methods. The results show that CSI300 stock index futures is an effective hedging instrument, and in general the performance of dynamic models are better than static models. In chapter 3, we analyze the price discovery contribution of CSI300 index futures market in the context of six relevant hypothesis and three empirical measures (PT/GG, IS, and MIS methods). The price discovery performance of Chinese stock index futures is found to be consistent with the other mature markets, indicating that new information that affects the fundamental value is reflected more quickly in the CSI300 index futures markets. Finally, using the efficient market hypothesis and unbiasedness hypothesis, CSI300 index futures is also found to be informational efficient in chapter 4. The market is partially efficient and the futures price is a constant risk unbiased predictor for the subsequent spot price in the long run. Different from previous literature which focus on the CSI300 futures and spot market, this thesis utilizes various data frequency and futures with different maturity to address the empirical issues regarding the functioning of CSI300 futures market. In addition, this thesis is the first study to the impact of regulation reforms in 2015 (when Chinese regulators strictly tightened the rules on trading stock index futures) on CSI300 index futures market. Finally, the performance of the CSI300 index futures market has been compared and evaluated with other more mature index futures markets around the globe. The findings of this thesis have important implications to market regulators and participants in developing more effective investment and regulatory strategies

Pix2Vox: Context-aware 3D Reconstruction from Single and Multi-view Images

Recovering the 3D representation of an object from single-view or multi-view RGB images by deep neural networks has attracted increasing attention in the past few years. Several mainstream works (e.g., 3D-R2N2) use recurrent neural networks (RNNs) to fuse multiple feature maps extracted from input images sequentially. However, when given the same set of input images with different orders, RNN-based approaches are unable to produce consistent reconstruction results. Moreover, due to long-term memory loss, RNNs cannot fully exploit input images to refine reconstruction results. To solve these problems, we propose a novel framework for single-view and multi-view 3D reconstruction, named Pix2Vox. By using a well-designed encoder-decoder, it generates a coarse 3D volume from each input image. Then, a context-aware fusion module is introduced to adaptively select high-quality reconstructions for each part (e.g., table legs) from different coarse 3D volumes to obtain a fused 3D volume. Finally, a refiner further refines the fused 3D volume to generate the final output. Experimental results on the ShapeNet and Pix3D benchmarks indicate that the proposed Pix2Vox outperforms state-of-the-arts by a large margin. Furthermore, the proposed method is 24 times faster than 3D-R2N2 in terms of backward inference time. The experiments on ShapeNet unseen 3D categories have shown the superior generalization abilities of our method.Comment: ICCV 201

PGDiff: Guiding Diffusion Models for Versatile Face Restoration via Partial Guidance

Exploiting pre-trained diffusion models for restoration has recently become a favored alternative to the traditional task-specific training approach. Previous works have achieved noteworthy success by limiting the solution space using explicit degradation models. However, these methods often fall short when faced with complex degradations as they generally cannot be precisely modeled. In this paper, we propose PGDiff by introducing partial guidance, a fresh perspective that is more adaptable to real-world degradations compared to existing works. Rather than specifically defining the degradation process, our approach models the desired properties, such as image structure and color statistics of high-quality images, and applies this guidance during the reverse diffusion process. These properties are readily available and make no assumptions about the degradation process. When combined with a diffusion prior, this partial guidance can deliver appealing results across a range of restoration tasks. Additionally, PGDiff can be extended to handle composite tasks by consolidating multiple high-quality image properties, achieved by integrating the guidance from respective tasks. Experimental results demonstrate that our method not only outperforms existing diffusion-prior-based approaches but also competes favorably with task-specific models.Comment: GitHub: https://github.com/pq-yang/PGDif

Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior

Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a "ghosting effect" in photos, which can impact their quality. Eliminating reflective flare is highly desirable but challenging. Many existing methods rely on manually designed features to detect these bright spots, but they often fail to identify reflective flares created by various types of light and may even mistakenly remove the light sources in scenarios with multiple light sources. To address these challenges, we propose an optical center symmetry prior, which suggests that the reflective flare and light source are always symmetrical around the lens's optical center. This prior helps to locate the reflective flare's proposal region more accurately and can be applied to most smartphone cameras. Building on this prior, we create the first reflective flare removal dataset called BracketFlare, which contains diverse and realistic reflective flare patterns. We use continuous bracketing to capture the reflective flare pattern in the underexposed image and combine it with a normally exposed image to synthesize a pair of flare-corrupted and flare-free images. With the dataset, neural networks can be trained to remove the reflective flares effectively. Extensive experiments demonstrate the effectiveness of our method on both synthetic and real-world datasets.Comment: CVPR2023 (Highlight

Towards Robust Blind Face Restoration with Codebook Lookup Transformer

Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to 1) improve the mapping from degraded inputs to desired outputs, or 2) complement high-quality details lost in the inputs. In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces. Under this paradigm, we propose a Transformer-based prediction network, named CodeFormer, to model the global composition and context of the low-quality faces for code prediction, enabling the discovery of natural faces that closely approximate the target faces even when the inputs are severely degraded. To enhance the adaptiveness for different degradation, we also propose a controllable feature transformation module that allows a flexible trade-off between fidelity and quality. Thanks to the expressive codebook prior and global modeling, CodeFormer outperforms the state of the arts in both quality and fidelity, showing superior robustness to degradation. Extensive experimental results on synthetic and real-world datasets verify the effectiveness of our method.Comment: Accepted by NeurIPS 2022. Code: https://github.com/sczhou/CodeForme