arXiv subjects

Sicheng Fu

Publications and source records attributed to Sicheng Fu.

7 recordsLinked to original sources

arXiv2026

A dynamic factor semiparametric model for VaR and expected shortfall driven by realized measures

This paper proposes a semiparametric joint VaRES framework driven by realized information, mo tivated by the economic mechanisms underlying tail risk generation. Building on the CAViaR quantile recursion, the model introduces a dynamic ESVaR gap to capture time-varying tail sever ity, while measurement equations transform multiple realized measures into high-frequency risk innovations.These innovations are further aggregated through a dynamic factor model, extracting common high-frequency tail risk factors that affect the quantile level and tail thickness through dis tinct risk channels. This structure explicitly separates changes in risk levels from the intensification of tail risk.Empirical evidence shows that the proposed model consistently outperforms quantile regression, EVT-based, and GARCH-type benchmarks across multiple loss functions, highlighting the importance of embedding high-frequency information directly into the tail risk generation layer

econ.GN↗

arXiv2025

A Predictive Framework Integrating Multi-Scale Volatility Components and Time-Varying Quantile Spillovers: Evidence from the Cryptocurrency Market

This paper investigates the dynamics of risk transmission in cryptocurrency markets and proposes a novel framework for volatility forecasting. The framework uncovers two key empirical facts: the asymmetric amplification of volatility spillovers in both tails, and a structural decoupling between market size and systemic importance. Building on these insights, we develop a state-adaptive volatility forecasting model by extracting time-varying quantile spillover features across different volatility components. These features are embedded into an extended Log-HAR structure, resulting in the SA-Log-HAR model. Empirical results demonstrate that the proposed model outperforms benchmark alternatives in both in-sample fitting and out-of-sample forecasting, particularly in capturing extreme volatility and tail risks with greater robustness and explanatory power.

econ.GN↗

arXiv2025

Forecasting realized volatility in the stock market: a path-dependent perspective

Volatility forecasting in financial markets is a topic that has received more attention from scholars. In this paper, we propose a new volatility forecasting model that combines the heterogeneous autoregressive (HAR) model with a family of path-dependent volatility models (HAR-PD). The model utilizes the long- and short-term memory properties of price data to capture volatility features and trend features. By integrating the features of path-dependent volatility into the HAR model family framework, we develop a new set of volatility forecasting models. And, we propose a HAR-REQ model based on the empirical quartile as a threshold, which exhibits stronger forecasting ability compared to the HAR-REX model. Subsequently, the predictive performance of the HAR-PD model family is evaluated by statistical tests using data from the Chinese stock market and compared with the basic HAR model family. The empirical results show that the HAR-PD model family has higher forecasting accuracy compared to the underlying HAR model family. In addition, robustness tests confirm the significant predictive power of the HAR-PD model family.

q-fin.RM↗

arXiv2024

FollowGen: A Scaled Noise Conditional Diffusion Model for Car-Following Trajectory Prediction

Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those utilizing transformer-based and generative models - have markedly improved prediction accuracy by capturing complex, non-linear patterns in vehicle dynamics and traffic interactions, they frequently overlook detailed car-following behaviors and the inter-vehicle interactions critical for real-world driving applications, particularly in fully autonomous or mixed traffic scenarios. To address the issue, this study introduces a scaled noise conditional diffusion model for car-following trajectory prediction, which integrates detailed inter-vehicular interactions and car-following dynamics into a generative framework, improving both the accuracy and plausibility of predicted trajectories. The model utilizes a novel pipeline to capture historical vehicle dynamics by scaling noise with encoded historical features within the diffusion process. Particularly, it employs a cross-attention-based transformer architecture to model intricate inter-vehicle dependencies, effectively guiding the denoising process and enhancing prediction accuracy. Experimental results on diverse real-world driving scenarios demonstrate the state-of-the-art performance and robustness of the proposed method.

cs.CV↗

arXiv2024

Analytical Optimized Traffic Flow Recovery for Large-scale Urban Transportation Network

The implementation of intelligent transportation systems (ITS) has enhanced data collection in urban transportation through advanced traffic sensing devices. However, the high costs associated with installation and maintenance result in sparse traffic data coverage. To obtain complete, accurate, and high-resolution network-wide traffic flow data, this study introduces the Analytical Optimized Recovery (AOR) approach that leverages abundant GPS speed data alongside sparse flow data to estimate traffic flow in large-scale urban networks. The method formulates a constrained optimization framework that utilizes a quadratic objective function with l2 norm regularization terms to address the traffic flow recovery problem effectively and incorporates a Lagrangian relaxation technique to maintain non-negativity constraints. The effectiveness of this approach was validated in a large urban network in Shenzhen's Futian District using the Simulation of Urban MObility (SUMO) platform. Analytical results indicate that the method achieves low estimation errors, affirming its suitability for comprehensive traffic analysis in urban settings with limited sensor deployment.

eess.SY↗

arXiv2024

Crossfusor: A Cross-Attention Transformer Enhanced Conditional Diffusion Model for Car-Following Trajectory Prediction

Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS), enhancing road safety and traffic efficiency. While traditional methods have laid foundational work, modern deep learning techniques, particularly transformer-based models and generative approaches, have significantly improved prediction accuracy by capturing complex and non-linear patterns in vehicle motion and traffic interactions. However, these models often overlook the detailed car-following behaviors and inter-vehicle interactions essential for real-world driving scenarios. This study introduces a Cross-Attention Transformer Enhanced Conditional Diffusion Model (Crossfusor) specifically designed for car-following trajectory prediction. Crossfusor integrates detailed inter-vehicular interactions and car-following dynamics into a robust diffusion framework, improving both the accuracy and realism of predicted trajectories. The model leverages a novel temporal feature encoding framework combining GRU, location-based attention mechanisms, and Fourier embedding to capture historical vehicle dynamics. It employs noise scaled by these encoded historical features in the forward diffusion process, and uses a cross-attention transformer to model intricate inter-vehicle dependencies in the reverse denoising process. Experimental results on the NGSIM dataset demonstrate that Crossfusor outperforms state-of-the-art models, particularly in long-term predictions, showcasing its potential for enhancing the predictive capabilities of autonomous driving systems.

cs.LG↗

arXiv2023

Optimizing Bus Travel: A Novel Approach to Feature Mining with P-KMEANS and P-LDA Algorithms

Customizing services for bus travel can bolster its attractiveness, optimize usage, alleviate traffic congestion, and diminish carbon emissions. This potential is realized by harnessing recent advancements in positioning communication facilities, the Internet of Things, and artificial intelligence for feature mining in public transportation. However, the inherent complexities of disorganized and unstructured public transportation data introduce substantial challenges to travel feature extraction. This study presents a bus travel feature extraction method rooted in Point of Interest (POI) data, employing enhanced P-KMENAS and P-LDA algorithms to overcome these limitations. While the KMEANS algorithm adeptly segments passenger travel paths into distinct clusters, its outcomes can be influenced by the initial K value. On the other hand, Latent Dirichlet Allocation (LDA) excels at feature identification and probabilistic interpretations yet encounters difficulties with feature intermingling and nuanced sub-feature interactions. Incorporating the POI dimension enhances our understanding of travel behavior, aligning it more closely with passenger attributes and facilitating easier data analysis. By incorporating POI data, our refined P-KMENAS and P-LDA algorithms grant a holistic insight into travel behaviors and attributes, effectively mitigating the limitations above. Consequently, this POI-centric algorithm effectively amalgamates diverse POI attributes, delineates varied travel contexts, and imparts probabilistic metrics to feature properties. Our method successfully mines the diverse aspects of bus travel, such as age, occupation, gender, sports, cost, safety, and personality traits. It effectively calculates relationships between individual travel behaviors and assigns explanatory and evaluative probabilities to POI labels, thereby enhancing bus travel optimization.

cs.LG↗