arXiv subjects

Tuoyuan Cheng

Publications and source records attributed to Tuoyuan Cheng.

4 recordsLinked to original sources

arXiv2025

Design-Life Levels for Environmental Extremes: A Dependence-Aware Block-Maxima Workflow for Severity and Persistence

Environmental risk assessment often asks how large the maximum discharge, flood, or insured loss may become over a design life rather than in a single year. In environmental records, planning-horizon risk is complicated by limited record length, extremal clustering, and sub-asymptotic behavior, yet severity estimation, clustering assessment, and design-life levels are often handled separately. We develop a dependence-aware block-maxima workflow that links these tasks within a single inferential scheme. The severity branch estimates the extreme value index from sliding block-maximum quantile scaling using data-adaptive plateau selection and covariance-aware feasible generalized least squares. The persistence branch pools native block-maxima extremal-index paths over a stable block-size window to characterize extremal clustering. Design-life levels are then derived on the chosen observation clock, with the extremal index retained as a complementary descriptor of persistence. In synthetic short-record benchmarks, the main gain is improved interval calibration under overlap dependence, especially within block-maxima comparisons. Applications to Texas and Florida streamflow and National Flood Insurance Program building-payout claims show persistent hydrologic extremes but much faster escalation of insured losses across adjacent parts of the flood-risk chain. The workflow provides calibrated severity, persistence, and design-life levels for environmental design and flood-risk assessment under dependent records.

stat.ME↗

arXiv2025

Vine Copulas as Differentiable Computational Graphs

Vine copulas are sophisticated models for multivariate distributions and are increasingly used in machine learning. To facilitate their integration into modern ML pipelines, we introduce the vine computational graph, a DAG that abstracts the multilevel vine structure and associated computations. On this foundation, we devise new algorithms for conditional sampling, efficient sampling-order scheduling, and constructing vine structures for customized conditioning variables. We implement these ideas in torchvinecopulib, a GPU-accelerated Python library built upon PyTorch, delivering improved scalability for fitting, sampling, and density evaluation. Our experiments illustrate how gradient flowing through the vine can improve Vine Copula Autoencoders and that incorporating vines for uncertainty quantification in deep learning can outperform MC-dropout, deep ensembles, and Bayesian Neural Networks in sharpness, calibration, and runtime. By recasting vine copula models as computational graphs, our work connects classical dependence modeling with modern deep-learning toolchains and facilitates the integration of state-of-the-art copula methods in modern machine learning pipelines.

cs.LG↗

arXiv2023

A General Framework for Portfolio Construction Based on Generative Models of Asset Returns

In this paper, we present an integrated approach to portfolio construction and optimization, leveraging high-performance computing capabilities. We first explore diverse pairings of generative model forecasts and objective functions used for portfolio optimization, which are evaluated using performance-attribution models based on LASSO. We illustrate our approach using extensive simulations of crypto-currency portfolios, and we show that the portfolios constructed using the vine-copula generative model and the Sharpe-ratio objective function consistently outperform. To accommodate a wide array of investment strategies, we further investigate portfolio blending and propose a general framework for evaluating and combining investment strategies. We employ an extension of the multi-armed bandit framework and use value models and policy models to construct eclectic blended portfolios based on past performance. We consider similarity and optimality measures for value models and employ probability-matching ("blending") and a greedy algorithm ("switching") for policy models. The eclectic portfolios are also evaluated using LASSO models. We show that the value model utilizing cosine similarity and logit optimality consistently delivers robust superior performances. The extent of outperformance by eclectic portfolios over their benchmarks significantly surpasses that achieved by individual generative model-based portfolios over their respective benchmarks.

q-fin.PM↗

arXiv2022

Measuring Tail Risks

Value at risk (VaR) and expected shortfall (ES) are common high quantile-based risk measures adopted in financial regulations and risk management. In this paper, we propose a tail risk measure based on the most probable maximum size of risk events (MPMR) that can occur over a length of time. MPMR underscores the dependence of the tail risk on the risk management time frame. Unlike VaR and ES, MPMR does not require specifying a confidence level. We derive the risk measure analytically for several well-known distributions. In particular, for the case where the size of the risk event follows a power law or Pareto distribution, we show that MPMR also scales with the number of observations $n$ (or equivalently the length of the time interval) by a power law, $\text{MPMR}(n) \propto n^{\eta}$, where $\eta$ is the scaling exponent. The scale invariance allows for reasonable estimations of long-term risks based on the extrapolation of more reliable estimations of short-term risks. The scaling relationship also gives rise to a robust and low-bias estimator of the tail index (TI) $\xi$ of the size distribution, $\xi = 1/\eta$. We demonstrate the use of this risk measure for describing the tail risks in financial markets as well as the risks associated with natural hazards (earthquakes, tsunamis, and excessive rainfall).

q-fin.RM↗