跳到正文
简报说明

来源与收录范围

汇总期刊目录中的 16 个期刊来源,以及 arXiv 计量经济学与相关统计、金融分类和主题检索中的预印本。期刊题录主要来自 Crossref,可靠摘要保留补充来源;期刊官网链接可在论文检索的说明中核对。

保留计量方法研究、相关统计基础及实质涉及计量方法的应用、教程、比较和综述。待核验、范围外及撤回记录不进入公开简报。基础文献按学习与研究需要单独选编。

此前日简报及周、月简报按可核实的原站发表、首次提交和修订日期归档;历史补录只留在文献库。期刊仅纳入收录当月新发表或提前取得题录的论文;只有月份的记录仅进入对应月简报。arXiv 同期只计一次,首次发布优先;未保存的中间版本无法补算。撤回内容会同步移除;所有历史补录仍可在论文检索中查找。

当前结果:已发表 0 篇 · 待刊 0 篇 · 预印本 4 篇

关键词匹配标题、摘要、作者或 DOI;支持部分字母,多词同时匹配,双引号匹配短语。

本期动态按期刊、arXiv 首次发布、arXiv 修订分组,组内默认本文引用优先。也可选择作者引用优先或最新收录。

期刊论文在前,预印本在后。组内先看本文引用,再看已有明确关联作者的最高累计引用,最后按当前日期顺序排序。

“作者引用”指已有明确关联作者的最高累计引用,不合计共同作者。目前作者指标只来自有身份关联快照的预印本,普通期刊条目可能没有该项数据;本页 0/4 篇有可核验作者指标。

本文引用采用 OpenAlex 单条关联记录,不合计版本;缺失不记作零。累计引用会随时间积累,可结合近年范围比较;引用数和作者发表经历是阅读线索,不代表论文质量或同行评审状态。

“作者发表记录”展示作者已有论文的可核验发表记录,不证明该预印本已接收或通过同行评审。

正常题录、可靠摘要和引用同步继续;AI 解读依据可靠材料,材料不足时保留待解读状态。

本期 4 篇
导出

arXiv · 本期首次发布

Beyond the Traditional VIX: A Novel Approach to Identifying Uncertainty Shocks in Financial Markets

超越传统 VIX:金融市场不确定性冲击的新识别方法

We introduce a new identification strategy for uncertainty shocks to explain macroeconomic volatility in financial markets. The Chicago Board Options Exchange Volatility Index (VIX) measures market expectations of future volatility, but traditional methods based on second-moment shocks and time-varying volatility of the VIX often fail to capture the non-Gaussian, heavy-tailed nature of asset returns. To address this, we construct a revised VIX by fitting a…

AI 解读阅读原文 ↗BibTeX ↓

Return and Volatility Forecasting Using On-Chain Flows in Cryptocurrency Markets

利用链上资金流预测加密货币收益与波动率

We empirically examine the intraday return- and volatility-forecasting power of on-chain flow data for Bitcoin(BTC), Ethereum(ETH), and Tether(USDT). We find ETH net inflows to strongly predict ETH returns and volatility in the 2017-2023 period. Our intraday frequencies are 1-6 hours. We find that differing significantly from forecasting patterns for BTC, ETH net inflows negatively predict ETH returns and volatility. First, we find that USDT flowing out of investors wallets…

AI 解读阅读原文 ↗BibTeX ↓

arXiv · 本期修订

When Frictions are Fractional: Rough Noise in High-Frequency Data

当市场摩擦具有分数性:高频数据中的粗糙噪声

The analysis of high-frequency financial data is often impeded by the presence of noise. This article is motivated by intraday return data in which market microstructure noise appears to be rough, that is, best captured by a continuous-time stochastic process that locally behaves as fractional Brownian motion. Assuming that the underlying efficient price process follows a continuous Itô semimartingale, we derive consistent estimators and asymptotic confidence intervals for…

AI 解读阅读原文 ↗BibTeX ↓

Short-time expansion of characteristic functions in a rough volatility setting with applications

粗糙波动率下特征函数的短时展开及其应用

We derive a higher-order asymptotic expansion of the conditional characteristic function of the increment of an Itô semimartingale over a shrinking time interval. The spot characteristics of the Itô semimartingale are allowed to have dynamics of general form. In particular, their paths can be rough, that is, exhibit local behavior like that of a fractional Brownian motion, while at the same time have jumps with arbitrary degree of activity. The expansion result shows the…

AI 解读阅读原文 ↗BibTeX ↓