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高维矩阵值时间序列的时空自回归模型AI 解读

Motivated by predicting intraday trading volume curves, we consider two spatio-temporal autoregressive models for matrix time series, in which each column may represent daily trading volume curve of one asset, and each row captures synchronized 5-minute volume intervals acrossmultiple assets. While traditional matrix time series focus mainly on temporal evolution, our approach incorporates both spatial and temporal dynamics, enabling simultaneous analysis of interactions…

具有社群网络结构的高维多变量已实现波动率预测AI 解读

We introduce the Community Network Heterogeneous Autoregressive model with Quarticity (CNHARQ), a novel framework that integrates network-based information to enhance the forecasting of multivariate realized volatilities. To address the curse of dimensionality, we propose a Correlation-Based Stochastic Block Model (CBSBM) to uncover latent community structures from the correlation network of realized volatilities of N assets. This approach reduces the number of unknown…

即期相关过程日内变动的非参数检验AI 解读

The association between log-price increments of exchange-traded equities, as measured by their spot correlation estimated from high-frequency data, exhibits a pronounced upwardsloping and almost piecewise linear relationship at the intraday horizon. There is notably lower—on average less positive—correlation in the morning than in the afternoon. We develop a nonparametric testing procedure to detect such variation in a correlation process. The test statistic has a known…

超越均值：无限均值自回归条件持续时间的极限理论与检验AI 解读

Abstract Integrated autoregressive conditional duration (ACD) models serve as counterparts to integrated generalized autoregressive conditional heteroskedastic models used for financial returns. However, despite their resemblance, asymptotic theory for ACD is still incomplete. Central challenges arise from the facts that (i) integrated ACD processes imply durations with infinite expectation and (ii) conventional asymptotic approaches break down due to the randomness in the…

Lévy过程的快速无似然参数估计AI 解读

ABSTRACT Lévy processes are widely used in financial modeling due to their ability to capture discontinuities and heavy tails, which are common in high‐frequency asset return data. However, parameter estimation remains a challenge when associated likelihoods are unavailable or costly to compute. We propose a fast and accurate method for Lévy parameter estimation using the neural Bayes estimation (NBE) framework—a simulation‐based, likelihood‐free approach that leverages…

高频信息组合优化中重审 EWMA：比较评估AI 解读

ABSTRACT This paper compares the statistical and economic performance of state‐of‐the‐art high‐frequency (HF) based multivariate volatility models with a simpler, widely used alternative, the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S. stock returns (2002–2023), we assess model performance through a Global Minimum Variance portfolio optimization exercise, with and without short‐selling restrictions across multiple forecast…

广义自回归得分树与森林AI 解读

We propose methods to improve the forecasts from generalized autoregressive score (GAS) models (Creal, et al., 2013; Harvey, 2013) by localizing their parameters using decision trees and random forests. These methods avoid the curse of dimensionality faced by kernel-based approaches, and allow one to draw on information from multiple state variables simultaneously. We apply the new models to four distinct empirical analyses, and in all applications the proposed new methods…

基于方差分解检验高频网络连通性的差异AI 解读

We develop a Bayesian Wald-type test for detecting temporal differences in connectedness from variance decompositions, and establish the asymptotic normality of impulse response functions and variance decomposition matrices at fixed local time points. The resulting test statistic is asymptotically χ 2 distributed under the null hypothesis and performs well in Monte Carlo simulations. Applying our methodology to high-frequency options data on sectoral exchange-traded funds…

动态状态空间 HAR 模型AI 解读

The Heterogeneous AutoRegressive model for the logs of Realised Volatility (HARL) has established itself as the benchmark specification for modelling and forecasting return volatility, owing to its parsimony and ability to capture the strong persistence typically observed in RV. To address potential concerns such as measurement errors, nonlinearities, and non-spherical residuals, numerous variants of the baseline HARL model have been developed in the literature. This paper…

股票指数间的跳跃传染：来自期权市场的证据AI 解读

We analyze the contagious propagation of jumps among international stock market indices, using a rich panel of high-frequency stock and options data ( 692,892 option contracts) over the period 2006–2015. We propose a bivariate option pricing model designed to allow for time and space amplification of jumps in option markets. We develop a semi-parametric estimation procedure, which employs a continuum of moment conditions in GMM with implied states and non-parametric…

高频价格停滞因子与波动率估计AI 解读

In this article, we propose a price staleness factor model that accounts for pervasive market friction across assets and incorporates relevant covariates. Using large-panel high-frequency data, we derive the maximum likelihood estimators of the regression coefficients, the nonstationary factors, and their loading parameters. These estimators recover the time-varying price staleness probabilities. We develop asymptotic theory in which both the dimension d and the sampling…

利用混频数据改进密度预测：贝叶斯方法AI 解读

We construct a modeling framework that allows for the flexible contribution of intraday information to predict the conditional distribution of asset returns. A flexible spline specification is proposed to capture the contribution of intraday returns inside GARCH and stochastic volatility specifications for daily returns. The “focused Bayesian” prediction framework is adopted to allow the intraday contribution to the simpler GARCH specification to be trained according to the…

多交易场所资产的积分方差估计AI 解读

In this paper, we show that the availability of multiple price series for the same asset can be exploited to estimate its integrated variance. We use a vector error correction model for those prices and its common trend representation to estimate the efficient price of the asset. Because the estimated efficient price satisfies the martingale property, we can then compute the sum of its squared intra-day returns to obtain an unbiased and efficient realized variance estimator.…

用于跳跃检测的随机化检验AI 解读

This article proposes sequential randomized tests to locate the presence of jumps on the paths of efficient asset prices in a continuous-time model. The randomized statistics are generated by artificially adding randomness to the robust approximations of the locally averaged returns of the efficient price. In the case of finite activity jumps, we derive the asymptotic distribution of the maximum of all the local statistics unaffected by jumps, which makes it feasible to…

对跳跃、微观结构噪声与异步观测稳健的高频方差分析AI 解读

This paper develops the necessary methodology for high frequency ANOVA, focusing on the estimations of idiosyncratic volatility and averaged R-Squared. As a nonlinear and complicated functional of the spot covariance matrix, idiosyncratic volatility estimation poses significant challenges, particularly with jumps, microstructure noise, and asynchronous observation times. Averaged R-Squared, a newly introduced quantity in high frequency econometrics, provides a measure of…

有效尾部假说：市场效率的极值视角AI 解读

In finance, the Efficient Market Hypothesis posits that asset prices reflect all available information in the market. Several empirical investigations show that market efficiency drops when it undergoes extreme events. Many models for multivariate extremes focus on positive dependence, making them unsuitable for studying extremal dependence in financial markets where data often exhibit both positive and negative extremal dependence. To this end, we construct regular…

可解释的市场微观结构噪声AI 解读

High-frequency financial data are often contaminated by market microstructure effects. In this study, we consider a setting where a portion of the microstructure noise can be explained by observable trading information, referred to as the explicative noise component. To formally analyze this component, we first develop a model-free variable importance measure in the high-frequency setting that quantifies the price impact of subsets of trading variables. Based on the…

波动率究竟是粗糙性还是长记忆？AI 解读

We develop a framework for composite likelihood estimation of parametric continuous-time stationary Gaussian processes. We derive the asymptotic theory of the associated maximum composite likelihood estimator. We implement our approach on a pair of models that have been proposed to describe the random log-spot variance of financial asset returns. A simulation study shows that it delivers good performance in these settings and improves upon a method-of-moments estimation. In…

重印：融合交易信息的高频数据非参数估计AI 解读

We propose nonparametric estimators for the explicative part of the noise in a model where the market microstructure noise is an unknown function of the trading information while allowing for the presence of an additional residual noise component. Our method allows for dependence in the observable trading information and accommodates the presence of infinite variation jumps in the efficient price process. We establish the convergence and asymptotic normality of the proposed…

时间变换扩散模型中已实现方差估计的高效抽样AI 解读

This paper analyzes the benefits of sampling intraday returns in intrinsic time for the realized variance (RV) estimator. We theoretically show in finite samples that depending on the permitted sampling information, the RV estimator is most efficient under either hitting time sampling that samples whenever the price changes by a pre-determined threshold, or under the new concept of realized business time that samples according to a combination of observed trades and…

内生抽样时点下离散观测价格过程的跳跃检验AI 解读

This paper introduces a novel nonparametric high-frequency jump test for discretely observed Itô semimartingales. Based on observations sampled recursively at first exit times from a symmetric double barrier, our method distinguishes between threshold exceedances caused by the Brownian component and jumps, which enables the construction of a feasible, noise-robust statistical test. Simulation results demonstrate superior finite-sample performance of our test compared to…

定制已实现波动率：面向波动率预测的风险度量AI 解读

Standard realized volatility (RV) measures estimate the latent volatility of an asset price using high frequency data with no reference to how or where the estimate will subsequently be used. This paper presents methods for “tailoring” the estimate of volatility to the application in which it will be used. For example, if the volatility measure will be used in a specific parametric forecasting model, it may be possible to exploit that knowledge to construct a better measure…

已实现协方差度量的概率分布AI 解读

Realized covariance measures (RCs) are an essential input for assessing the risks of different investment allocations. Thus, it is useful to model and forecast them. To this end, a realistic distributional assumption is needed. In this paper, we compare all probability distributions that have so far in the literature been applied to time-series of RCs. We derive them in a unified framework based on their stochastic representations using random lower and upper triangular…

一致最小方差无偏分块估计量AI 解读

We provide necessary and sufficient conditions for an (Unbiased) Block estimator to have Uniformly Minimum Variance. Our theory parallels the theory of UMVU estimation, the main novel insight being the focus on the covariance among blocks. We use this theory to derive lower variance bounds for block estimators of functionals of high-frequency volatility when the block size is fixed. We further show the relevance of the new theory for the classical problem of estimation of…