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高维夏普比率分析：因子模型中基于残差的逐节点回归AI 解读

We provide a new theory for nodewise regression when the residuals from a fitted factor model are used. We apply our results to the analysis of the consistency of Sharpe Ratio estimators when there are many assets in a portfolio. We allow for an increasing number of assets as well as time observations of the portfolio. Since the nodewise regression is not feasible due to the unknown nature of idiosyncratic errors, we provide a feasible-residual-based nodewise regression to…

时齐扩散过程的扩散系数跳跃有多大？AI 解读

We consider high-frequency observations from a one-dimensional time-homogeneous diffusion process Y . We assume that the diffusion coefficient $\sigma $ is continuously differentiable in y , but with a jump discontinuity at some level y , say $y=0$ . We first study sign-constrained kernel estimators of functions of the left and right limits of $\sigma $ at $0$ . These functions intricately depend on both limits. We propose a method to extricate these functions by searching…

多元极值的稀疏聚类AI 解读

Identifying directions where extreme events occur is a significant challenge in multivariate extreme value analysis. In this article, we use the concept of sparse regular variation introduced by Meyer and Wintenberger to infer the tail dependence of a random vector X. This approach relies on the Euclidean projection onto the simplex which better exhibits the sparsity structure of the tail of X than the standard methods. Our procedure based on a rigorous methodology aims at…

杠杆效应估计：核函数与效率AI 解读

This article proposes more efficient estimators for the leverage effect than the existing ones. The idea is to allow for nonuniform kernel functions in the spot volatility estimates or the aggregated returns. This finding highlights a critical difference between the leverage effect and integrated volatility functionals, where the uniform kernel is optimal. Another distinction between these two cases is that the overlapping estimators of the leverage effect are more efficient…

机器学习能否修正期权定价模型？AI 解读

We introduce a novel two-step approach to predict implied volatility surfaces. Given any fitted parametric option pricing model, we train a feedforward neural network on the model-implied pricing errors to correct for mispricing and boost performance. Using a large dataset of S&P 500 options, we test our nonparametric correction on several parametric models ranging from ad-hoc Black–Scholes to structural stochastic volatility models and demonstrate the boosted performance…

重尾时间序列中基于期望分位数的尾部风险推断AI 解读

Expectiles define the only law-invariant, coherent and elicitable risk measure apart from the expectation. The popularity of expectile-based risk measures is steadily growing and their properties have been studied for independent data, but further results are needed to establish that extreme expectiles can be applied with the kind of dependent time series models relevant to finance. In this article we provide a basis for inference on extreme expectiles and expectile-based…

已实现协方差矩阵的奇异条件自回归 Wishart 模型AI 解读

Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in nonsingular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices may obtain as singular by…

时间序列模型两阶段准极大似然估计的 Bootstrap 推断AI 解读

This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing the substitution of capital…

共同基金绩效的自助法分析AI 解读

We study bootstrap methods for fund performance evaluation. We first show that two prominent bootstrap tests have biased test sizes in a large cross-section with short time series and lack test power to detect skilled funds when a substantial number of unskilled funds are present. We then develop the theory for a valid bootstrap Hotelling’s T-squared test for zero alpha. We apply the proposed bootstrap test in a practical two-step procedure to identify skilled funds. Our…

含结构突变线性回归中贝叶斯推断的渐近性质AI 解读

This paper studies large sample properties of a Bayesian approach to inference about slope parameters γ in linear regression models with a structural break. In contrast to the conventional approach to inference about γ that does not take into account the uncertainty of the unknown break date, the Bayesian approach that we consider incorporates such uncertainty. Our main theoretical contribution is a Bernstein–von Mises type theorem (Bayesian asymptotic normality) for γ under…

异质流动性下的已实现协方差建模：广义矩阵 Wishart 状态空间模型AI 解读

We propose to generalize the Wishart state-space model for realized covariance matrices of asset returns in order to capture complex measurement error structures induced by modern robust and data efficient realized covariance estimators and heterogeneous liquidity across assets. Our model assumes that the latent covariance matrix of the assets is observed through their realized covariance matrix with a Riesz measurement density, which generalizes the Wishart to monotone…

异质幂律数据的极值推断AI 解读

We extend extreme value statistics to independent data with possibly very different distributions. In particular, we present novel asymptotic normality results for the Hill estimator, which now estimates the extreme value index of the average distribution. Due to the heterogeneity, the asymptotic variance can be substantially smaller than that in the i.i.d. case. As a special case, we consider a heterogeneous scales model where the asymptotic variance can be calculated…

分布中的多个结构突变：经验特征函数方法AI 解读

We estimate and test for multiple structural breaks in distribution via an empirical characteristic function approach. By minimizing the sum of squared generalized residuals, we can consistently estimate the break fractions. We propose a sup- F type test for structural breaks in distribution as well as an information criterion and a sequential testing procedure to determine the number of breaks. We further construct a class of derivative tests to gauge possible sources of…

半参数因子模型的可行加权投影主成分分析AI 解读

Summary Various factor estimation procedures have been developed, based on the latent factor model. They often consider general conditions that allow for correlations and heteroscedasticity. However, the conventional principal components method does not efficiently estimate the parameters. It also does not accommodate additional covariates, which explain the unknown factors, even if they are available. In particular, a few aggregated macroeconomic variables can be used as…

PELVE：VaR 与 ES 的概率等价水平AI 解读

In the recent Fundamental Review of the Trading Book (FRTB), the Basel Committee on Banking Supervision proposed the shift from the 99% Value-at-Risk (VaR) to the 97.5% Expected Shortfall (ES) for internal models in market risk assessment. Inspired by the above transition, we introduce a new distributional index, the probability equivalence level of VaR and ES (PELVE), which identifies the balancing point for the equivalence between VaR and ES. PELVE enjoys many desirable…

准得分驱动模型AI 解读

This paper introduces the class of quasi score-driven (QSD) models. This new class inherits and extends the basic ideas behind the development of score-driven (SD) models and addresses a number of unsolved issues in the score literature. In particular, the new class of models (i) generalizes many existing models, including SD models, (ii) disconnects the updating equation from the log-likelihood implied by the conditional density of the observations, (iii) allows testing of…

预测分位数回归的新稳健推断方法AI 解读

This paper proposes a novel approach to offer a robust inferential theory across all types of persistent regressors in a predictive quantile regression model. We first estimate a quantile regression with an auxiliary regressor, which is generated as a weighted combination of an exogenous random walk process and a bounded transformation of the original regressor. With a similar spirit of rotation in factor analysis, one can then construct a weighted estimator using the…

幂 ARCH(∞) 模型中的条件非对称性AI 解读

We consider an extension of ARCH(∞) models to account for conditional asymmetry in the presence of high persistence. After stating existence and stationarity conditions, this paper develops the statistical inference of such models and proves the consistency and asymptotic distribution of a Quasi Maximum Likelihood estimator. Some particular specifications are studied and we introduce a Portmanteau goodness-of-fit test. Additionally, test procedures for asymmetry and GARCH…

向量 CopulaAI 解读

This paper introduces vector copulas associated with multivariate distributions with given multivariate marginals, based on the theory of measure transportation, and establishes a vector version of Sklar’s theorem. The latter provides a theoretical justification for the use of vector copulas to characterize nonlinear or rank dependence between a finite number of random vectors (robust to within vector dependence), and to construct multivariate distributions with any given…

基于期权的即时波动率估计之偏差缩减AI 解读

We consider the problem of nonparametric spot volatility estimation from options that is robust to time-variation in volatility and presence of jumps in the underlying asset price. Using a higher-order expansion of the characteristic function of the underlying price increment over shrinking time intervals and option-based estimates of the latter over two distinct horizons, we achieve asymptotic bias-reduction in spot volatility estimation, relative to existing methods, that…

具有持仓数量约束的高维投资组合选择AI 解读

The expanding number of assets offers more opportunities for investors but poses new challenges for modern portfolio management (PM). As a central plank of PM, portfolio selection by expected utility maximization (EUM) faces uncontrollable estimation and optimization errors in ultrahigh-dimensional scenarios. Past strategies for high-dimensional PM mainly concern only large-cap companies and select many stocks, making PM impractical. We propose a…

大规模面板高频数据中全局与局部主成分分析的差异AI 解读

In this article, we study the discrepancy between the global principal component analysis (GPCA) and local principal component analysis (LPCA) in recovering the common components of a large-panel high-frequency data. We measure the discrepancy by the total sum of squared differences between common components reconstructed from GPCA and LPCA. The asymptotic distribution of the discrepancy measure is provided when the factor space is time invariant as the dimension p and…

非平稳时间序列的泛函估计与变化检测AI 解读

Tests for structural breaks in time series should ideally be sensitive to breaks in the parameter of interest, while being robust to nuisance changes. Statistical analysis thus needs to allow for some form of nonstationarity under the null hypothesis of no change. In this paper, estimators for integrated parameters of locally stationary time series are constructed and a corresponding functional central limit theorem is established, enabling change-point inference for a broad…

非参数高频估计量的推断及金融贝塔时变性的应用AI 解读

We consider the problem of conducting inference on nonparametric high-frequency estimators without knowing their asymptotic variances. We prove that a multivariate subsampling method achieves this goal under general conditions that were not previously available in the literature. By construction, the subsampling method delivers estimates of the variance-covariance matrices that are always positive semidefinite. Our simulation study indicates that the subsampling method is…