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当摩擦具有分数特征：高频数据中的粗糙噪声AI 解读

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…

矩阵 GARCH 模型：推断与应用AI 解读

Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroskedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use…

时变因子模型的区分检验AI 解读

Time-varying factor models have been widely used to model changing relationships among economic and financial variables. The existing literature usually specifies the time-varying factor loadings as deterministic functions of time or unit root processes. This article proposes two consistent tests to distinguish between these two specifications based on a randomization approach. By setting the null hypothesis as either specification, we show that the proposed test statistics…

概率型分位数因子分析AI 解读

This article extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. We establish through synthetic and real data experiments that the proposed estimator can, in many cases, achieve better accuracy than a recently proposed loss-based estimator. We contribute to the factor analysis literature by extracting new indexes of low, medium, and high economic policy uncertainty, as well as…

基于日内蜡烛图影线的已实现方差估计AI 解读

We develop a novel nonparametric estimator of integrated variance by summing up the squared wick lengths of intraday candlesticks over a fixed time interval. The proposed wick-based estimator is robust to short-lived extreme price movements, such as gradual jumps and flash crashes. We investigate the asymptotic properties of the proposed estimator, and show that its asymptotic variance is about four times smaller than the state-of-the-art differenced-return volatility (DV)…

波动率周期建模：MF2-GARCH 模型AI 解读

ABSTRACT We propose a novel multiplicative factor multi‐frequency GARCH (MF2‐GARCH) model, which exploits the empirical fact that the daily standardized forecast errors of one‐component GARCH models are predictable by a moving average of past standardized forecast errors. In contrast to other multiplicative component GARCH models, the MF2‐GARCH features stationary returns, and long‐term volatility forecasts are mean‐reverting. When applied to the S&P 500, the new component…

特质波动率的横截面依赖AI 解读

This paper introduces an econometric framework for analyzing cross-sectional dependence in the idiosyncratic volatilities of assets using high frequency data. We first consider the estimation of standard measures of dependence in the idiosyncratic volatilities such as covariances and correlations. Naive estimators of these measures are biased due to the use of the error-laden estimates of idiosyncratic volatilities. We provide bias-corrected estimators and the relevant…

跨时区股票收益的大型验证性动态因子模型AI 解读

We propose a confirmatory dynamic factor model for a large number of stocks whose returns are observed daily across multiple time zones. The model has a global factor and a continental factor that both drive the individual stock return series. We propose two estimators of the model: a quasi-maximum likelihood estimator (QML-just-identified), and an improved estimator based on an Expectation Maximization (EM) algorithm (QML-all-res). Our estimators are consistent and…

通过张量因子建模进行张量时间序列缺失值填补AI 解读

We propose tensor time series imputation when the missing pattern in the tensor data can be general, as long as any two data positions along a tensor fibre are both observed for enough time points. The method is based on a tensor time series factor model with Tucker decomposition of the common component. One distinguished feature of the tensor time series factor model used is that there can be weak factors in the factor loading matrix for each mode. This reflects reality…

波动率的乘法因子模型AI 解读

Facilitated with high-frequency observations, we introduce a remarkably parsimonious one-factor volatility model that offers a novel perspective for comprehending daily volatilities of a large number of stocks. Specifically, we propose a multiplicative volatility factor (MVF) model, where stock daily variance is represented by a common variance factor and a multiplicative idiosyncratic component. We demonstrate compelling empirical evidence supporting our model and provide…

注意你的语言：市场对央行讲话的反应AI 解读

Central bank communication between meetings often moves markets, but researchers have traditionally paid less attention to it. Using a dataset of U.S. Federal Reserve speeches, we develop supervised multimodal natural language processing methods to identify how monetary policy news affect bond and stock market volatility and tail risk through implied changes in forecasts of GDP, inflation, and unemployment. We find that forecast revisions derived from FOMC-member speech can…

约束多项式似然AI 解读

We develop a non-negative polynomial minimum-norm likelihood ratio (PLR) of two distributions of which only moments are known. The sample PLR converges to the unknown population PLR under mild conditions. The methodology allows for additional shape restrictions, as we illustrate with two empirical applications. The first develops a PLR for the unknown transition density of a jump-diffusion process, while the second extracts a positive density directly from option prices. In…

系统性传染AI 解读

This article unifies existing systemic risk measures to present a novel intersecting notion of systemic contagion which captures institutional risk transmission amplified by a distressed financial system. The article constructs a test statistic for systemic contagion which demonstrates its systemic capacity in testing contagion under a series of numerical experiments. Using the new test the article constructs a forward-looking directed network to empirically measure the…

高维矩阵因子模型的准最大似然估计AI 解读

In this study, we introduce a novel approach, called the quasi maximum likelihood estimation (Q-MLE), for estimating large-dimensional matrix factor models. In contrast to the principal component analysis based approach, Q-MLE considers the heteroscedasticity of the idiosyncratic error term, the heteroscedasticity of which is simultaneously estimated with other parameters. Interestingly, under the homoscedasticity assumption of the idiosyncratic error, the Q-MLE estimator…

融合不同信息来源的贝叶斯最优投资组合选择AI 解读

This article introduces Bayesian inference procedures for tangency portfolios, with a primary focus on deriving a new conjugate prior for portfolio weights. This approach not only enables direct inference about the weights but also seamlessly integrates additional information into the prior specification. Specifically, it automatically incorporates high-frequency returns and a market condition metric (MCM), exemplified by the CBOE Volatility Index (VIX) and Economic Policy…

多期动态投资组合选择：高维资产与收益可预测性AI 解读

Multiperiod portfolio choice is the central problem in active asset management. Multiperiod dynamic portfolios are notoriously difficult to solve, especially when there are hundreds of tradable assets as well as a large number of state variables. In this article, we develop a novel two-step methodology to solve the multiperiod dynamic portfolio choice problem with high dimensional assets in the presence of return predictability conditional on a large number of state…

零利率下限下的平滑影子利率动态 Nelson–Siegel 模型AI 解读

We propose a smooth shadow-rate version of the dynamic Nelson-Siegel (DNS) model to analyze the term structure of interest rates during a zero lower bound (ZLB) period.By relaxing the no-arbitrage restriction, our shadow-rate model becomes highly tractable with a closed-form yield curve expression.This permits the implementation of readily available DNS extensions such as allowing for time-varying parameters and the integration of macroeconomic variables.Using U.S. Treasury…

大型偏斜 t Copula 模型与日内股票收益的非对称依赖AI 解读

Skew-t copula models are attractive for the modeling of financial data because they allow for asymmetric and extreme tail dependence.We show that the copula implicit in the skew-t distribution of Azzalini and Capitanio allows for a higher level of pairwise asymmetric dependence than two popular alternative skew-t copulas.Estimation of this copula in high dimensions is challenging, and we propose a fast and accurate Bayesian variational inference (VI) approach to do so.The…

极端系统性风险预测中的估计风险AI 解读

Systemic risk measures have been shown to be predictive of financial crises and declines in real activity. Thus, forecasting them is of major importance in finance and economics. In this paper, we propose a new forecasting method for systemic risk as measured by the marginal expected shortfall (MES). It is based on first de-volatilizing the observations and, then, calculating systemic risk for the residuals using an estimator based on extreme value theory. We show the…

汇率、无抛补利率平价与时变 Fama 回归AI 解读

ABSTRACT This paper studies the forward premium puzzle, which signals a violation of the uncovered interest parity (UIP) hypothesis. We test this hypothesis with Fama‐style regressions with time‐varying parameters (TVPs) and stochastic volatility (SV) on six major currencies relative to the US dollar on monthly samples from 1993 to 2018. TVP‐SV regressions are also employed to examine the opposing predictions of the forward premium and excess volatility puzzles often found…

通过杠杆效应识别波动率风险价格AI 解读

In asset pricing models with stochastic volatility, uncertainty about volatility affects risk premia through two channels: aversion to decreasing returns and aversion to increasing volatility. We analyze the identification of and robust inference for structural parameters measuring investors’ aversions to these risks: the return risk price and the volatility risk price. In the presence of a leverage effect (instantaneous causality between the asset return and its…

多因子资产定价模型中识别稳健的同时推断AI 解读

This paper proposes exact identification-robust confidence sets for the zero-beta rate and ex-post factor prices in asset pricing models. Exploiting the information from the cross-sectional intercept allows us to impose or formally test model-consistent restrictions, including those resulting from traded factors in excess of the zero beta-rate or from return spreads. Analytical projection-based solutions for confidence set outcomes are developed. The proposed procedures are…

条件谱方法AI 解读

We model predictive scale-specific cycles. By employing suitable matrix representations, we express the forecast errors of covariance-stationary multivariate time series in terms of conditionally orthonormal scale-specific bases. The representations yield conditionally orthogonal decompositions of these forecast errors. They also provide decompositions of their variances and betas in terms of scale-specific variances and betas capturing predictive variability and…

潜在因子与可观测因子的空间张成检验AI 解读

Factor analysis is a widely used tool to summarize high dimensional panel data via a small dimensional set of latent factors. Many applications in finance and macroeconomics, are often focused on observable factors with an economic interpretation. The objective of this paper is to provide a test to answer a question which naturally comes up in discussions regarding latent versus observable factors: do latent and observable factors span the same space? We derive asymptotic…