论文检索

无需VaR预测的期望损失E-回测：时间序列模型下的方法AI 解读

Expected Shortfall (ES) measures the average size of tail losses. Its sequential backtests typically also require a Value-at-Risk (VaR) forecast. We develop two e-backtesting methods using only observed losses and ES forecasts: one for iid losses only and the other for time series under an AR-GARCH conditional VaR structure. Under the stated assumptions, both control false rejections at any stopping time. For the time-series method, validity requires a fixed…

短程与长程相依下在线期望分位数估计的极限理论AI 解读

Expectiles are tail-sensitive distributional summaries with important applications in financial risk measurement and forecasting, yet statistical theory for their online estimation from dependent data remains limited. We study a one-pass, stochastic-gradient algorithm with constant learning rate $γ$ for estimating the marginal expectile of a stationary causal process. The recursion admits a unique stationary causal solution and geometrically forgets its initialization. Under…

循环重心编码器：预测、模拟及其在时间序列分析中的应用AI 解读

This dissertation has two parts. In the first part we theoretically develop a framework that extends the optimal transport barycenter problem from independent data with observed covariates to processes whose relevant covariate is their own history. Given a stationary stochastic process, our framework shows there exists an invertible transport map that extracts from it an iid component whose distribution is the barycenter of the conditional distributions of the input process…

已实现尾部风险条件自回归期望分位模型：精准稳定的预测表现AI 解读

A realized tail-risk conditional autoregressive expectile (RTCARE) model is proposed for joint forecasting of value-at-risk (VaR) and expected shortfall (ES). Realized tail measures such as the realized quantile and the realized ES, constructed from intraday high-frequency returns, are incorporated into the dynamics and measurement equations of the conditional expectile, allowing information about lower-tail risk to enter the forecasts. The parameters are estimated by Markov…

重叠多步预测的贝叶斯协调：跨预测起点一致性的后处理框架AI 解读

Multi-step forecasting systems, including modern artificial intelligence (AI) models, often issue overlapping predictions for the same future time from different forecast origins. These predictions can disagree even when produced by the same model, a phenomenon we call cross-origin incoherence. We propose a Bayesian reconciliation framework that treats overlapping forecasts as noisy and potentially biased measurements of a common latent trajectory. The method operates as a…

We tackle the issue of producing point, sign, and density forecasts for the monthly real price of carbon in the European Union Emissions Trading System (EU ETS). We show that Bayesian Vector Autoregressive (BVAR) models augmented with factors based on macroeconomic and financial variables yield improvements in point and density forecasting performance, with the largest point-forecast gains emerging at intermediate and longer horizons. In particular, the one-factor BVAR model…

共享横截面预测目标时的目标对齐、稀释与预测者选择AI 解读

Forecasters often score the same units per date against one standardized realized outcome. We show that every standardized forecast splits exactly into a component aligned with this common target and a component uncorrelated with it. Three consequences follow: forecast-error correlation largely mirrors forecast correlation and is therefore a poor measure of diversity; an equally weighted combination beats a no-information forecast only when average alignment is large…

预测评分分解的统计推断AI 解读

We introduce inference methods for score decompositions, which partition scoring functions for predictive assessment into three interpretable components: miscalibration, discrimination, and uncertainty. Our estimation and inference relies on a linear recalibration of the forecasts and is applicable to general point forecasts such as means and quantiles due to its validity for non-smooth scoring functions. This approach ensures non-negative decomposition terms in finite…

利用市场信息网络建模并评估金融极端事件预测AI 解读

Modeling the joint distribution of extreme values in high-dimensional financial time series is challenging because extremes are sparse and locally extreme observations are not necessarily extreme relative to their full marginal distribution. To address this, we introduce a time-dependent network Hüsler-Reiss model in which market-informed adjacency matrices determine how strongly observations contribute to the estimation. We propose binary and weighted specifications,…

分位数回归的固定平滑一致推断AI 解读

This paper develops fixed-smoothing (fixed-b, fixed-K) inference methods for time-series quantile regression that are robust to heteroskedasticity and autocorrelation. Our approach is uniformly valid over quantile levels and accounts for dependence both over time and across quantiles. It enables the construction of uniform confidence bands, Wald, and Sup-t tests for joint hypotheses, and tests of shape restrictions, providing a unified framework for assessing heterogeneity…

金融中的数量方法AI 解读

These lecture notes provide a comprehensive introduction to Quantitative Methods in Finance (QMF), designed for graduate students in finance and economics with heterogeneous programming backgrounds. The material develops a unified toolkit combining probability theory, statistics, numerical methods, and empirical modeling, with a strong emphasis on implementation in Python. Core topics include random variables and distributions, moments and dependence, simulation and Monte…

用于企业生存预测的高维删失 MIDAS 逻辑回归AI 解读

This paper addresses the challenge of forecasting corporate distress, a problem marked by three key statistical hurdles: (i) right censoring, (ii) high-dimensional predictors, and (iii) mixed-frequency data. To overcome these complexities, we introduce a novel high-dimensional censored MIDAS (Mixed Data Sampling) logistic regression. Our approach handles censoring through inverse probability weighting and achieves accurate estimation with numerous mixed-frequency predictors…

高维波动率模型的稳健且可扩展估计AI 解读

This paper introduces a robust and computationally efficient estimation framework for high-dimensional volatility models in the BEKK-ARCH class. The proposed approach employs data truncation to ensure robustness against heavy-tailed distributions and utilizes a regularized least squares method for efficient optimization in high-dimensional settings. Non-asymptotic error bounds are established for the resulting estimators under heavy-tailed regimes, and the minimax optimal…

非流动性风险值AI 解读

Market efficiency relies fundamentally on stable liquidity. Consequently, forecasting liquidity dynamics is a priority for both investors and regulators. We introduce a new tail-risk metric, Illiquidity-at-Risk (IlliQaR), designed to quantify the magnitude of extreme liquidity dry-ups. Relying upon the realized Amihud (a precise illiquidity measurement derived from high-frequency data as the ratio of realized volatility to trading volume) we assess the predictive power of…

随机波动率管理组合的灵活变分近似AI 解读

This paper investigates volatility-managed portfolios through a variational Bayes approach that smooths stochastic volatility forecasts. Our algorithm exhibits competitive performance relative to established methods both in terms of inferential accuracy and computational efficiency. Analyzing equity factors and characteristic-based portfolios, we show that smoothing reduces excess leverage and turnover, significantly improving risk-adjusted returns after transaction costs.…

偏斜 t 分布实现随机波动率模型：波动率与尾部风险预测AI 解读

Accurate forecasting of volatility is essential for financial risk management and for the evaluation of tail risk measures such as value-at-risk (VaR) and expected shortfall (ES). This study proposes the realized stochastic volatility (RSV) model, an extension of the traditional stochastic volatility (SV) model that incorporates realized volatility as an efficient proxy for latent volatility. To better capture the stylized features of financial return distributions,…

网络实现 GARCH–Itô 模型：基于高频识别的波动率溢出AI 解读

We introduce a network realized GARCH-Itô model in which volatility transmission is a dynamic relation among the latent daily integrated volatilities of multiple assets. An unknown directed and signed network is embedded in a continuous-time variance process and appears in the resulting exponential daily recursion. Intraday returns identify integrated volatility and hence the network that governs its propagation. For a fixed network template, feasible estimation based on…

路径签名学习有多快？路径回归的统计理论与应用AI 解读

Many prediction and decision-making problems in operations research involve path-valued covariates -- data that evolve over time -- for which path signatures have become a canonical feature representation. Their use is justified by a universal approximation theorem, but this is an existence result: it guarantees that a finite-level signature can approximate any continuous path functional, without quantifying how fast the approximation error decreases as the truncation level…

用于分布时间序列预测的 Wasserstein 指数平滑AI 解读

Distributional time series arise when each temporal observation is a probability distribution rather than a scalar. We propose Wasserstein exponential smoothing (WES), a one-parameter recursive forecasting method for distributional time series on $\mathbb{R}$. The method adapts the practical logic of classical exponential smoothing to probability distributions by updating forecast distributions along Wasserstein geodesics. This yields a simple filter that can be applied…

分位数估计的补偿模型及其在 VaR 中的应用AI 解读

Unlike the standard two-step workflow of estimating a time series distribution and extracting quantiles from it, this paper proposes a compensatory model to refine quantile estimates based on an existing fitted distribution. We embed a new penalty term in the model and theoretically characterize its ability to bound realized coverage errors, yielding an adaptive quantile estimator. Backtests on the S&P 500 and NASDAQ Composite show that the compensatory model substantially…

融合多种已实现测度的金融波动率与风险预测AI 解读

Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volatility estimators, each with unique advantages and limitations, selecting an optimal estimator may introduce challenges. In this thesis, aiming to synthesise the impact of various realised volatility measures on volatility forecasting, we propose an extension of the Realised GARCH model that…

通过二维马尔可夫链构建长记忆 GARCH 模型AI 解读

This paper proposes a GARCH-type volatility model in which level-and-slope updates of a latent power-law kernel generate state-dependent decay of past shocks within a two-dimensional Markov state. We derive a joint Foster--Lyapunov condition and establish positive Harris recurrence and uniqueness of the invariant distribution. Simulations show substantial low-frequency persistence in log-squared innovations, especially near the diagnostic stability boundary. Empirically, the…

基于市场状态的社交情绪与技术特征融合：比特币价格方向预测AI 解读

Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance. Bitcoin exhibits fat-tailed returns, non-stationary dynamics, and a price discovery process influenced by social discourse on Reddit and Twitter. Conventional approaches fuse OHLCV technical features with sentiment via static concatenation, applying identical fusion weights regardless of market state. This is inconsistent with the behavioural finance literature, which shows that…

多元非平稳时间序列的自适应多尺度预测与门控局部共形预测AI 解读

We propose ABF-T-GLCP, a model-agnostic framework for forecasting and uncertainty quantification in nonstationary multivariate time series. The central idea is to learn an adaptive predictive state representation for point forecasting and reuse it for conformal calibration. The forecasting module combines horizon-specific temporal experts through a learned gate and refines predictions using sparse predictive transfer across related series. The uncertainty module,…