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部分信息汇集的动态条件相关模型AI 解读

We propose a novel Dynamic Conditional Correlation model with Conditional Linear Information Pooling (CLIP-DCC) which endogenously determines an optimal degree of commonality in the correlation innovations. Effectively, this allows a part of the update of each individual correlation to parsimoniously depend on the information contained in all asset return pairs. In contrast to existing approaches, such as the Dynamic EquiCOrrelation (DECO) model, the CLIP-DCC model does not…

低噪声动态因子模型中 EM 算法的缓慢收敛AI 解读

ABSTRACT This paper addresses the slow convergence of the expectation–maximization (EM) algorithm in the estimation of low‐noise dynamic factor models, commonly used in macroeconomic nowcasting and forecasting. We show analytically and in Monte Carlo simulations how the EM algorithm stagnates in a low‐noise environment, leading to inaccurate estimates of factor loadings and factor realizations. An adaptive version of EM considerably speeds up convergence, producing…

马尔可夫转换 VAR 模型中的矩、冲击与溢出AI 解读

To investigate how economies, financial markets or institutions can deal with stress, we often analyze the effects of shocks conditional on being in a recession or a bear market. MSVAR models are perfectly suited for such analyses because they combine gradual movements with sudden regime switches. In this paper, we develop a comprehensive methodology to conduct these analyses. We derive first and second moments conditional only on the regime distribution and propose impulse…

具有观测驱动动态因子载荷的闭式多因子 Copula 模型AI 解读

We develop new multi-factor dynamic copula models with time-varying factor loadings and observation-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation matrices. A closed-form likelihood expression allows for straightforward parameter estimation and likelihood inference. We apply the new model to a large panel of 100 U.S. stocks over the period 2001–2014. The proposed multi-factor structure is…

适用于厚尾已实现协方差与收益的新 HEAVY 模型AI 解读

We develop a new score-driven model for the joint dynamics of fat-tailed realized covariance matrix observations and daily returns. The score dynamics for the unobserved true covariance matrix are robust to outliers and incidental large observations in both types of data by assuming a matrix-F distribution for the realized covariance measures and a multivariate Student's t distribution for the daily returns. The filter for the unknown covariance matrix has a computationally…

利用聚焦评分规则组合密度预测AI 解读

Summary We investigate the added value of combining density forecasts focused on a specific region of support. We develop forecast combination schemes that assign weights to individual predictive densities based on the censored likelihood scoring rule and the continuous ranked probability scoring rule (CRPS) and compare these to weighting schemes based on the log score and the equally weighted scheme. We apply this approach in the context of measuring downside risk in equity…

如何识别与预测牛市和熊市？AI 解读

Summary Because the state of the equity market is latent, several methods have been proposed to identify past and current states of the market and forecast future ones. These methods encompass semi‐parametric rule‐based methods and parametric Markov switching models. We compare the mean‐variance utilities that result when a risk‐averse agent uses the predictions of the different methods in an investment decision. Our application of this framework to the S&P 500 shows that…

SUMMARY We consider forecasting the term structure of interest rates with the assumption that factors driving the yield curve are stationary around a slowly time‐varying mean or ‘shifting endpoint’. The shifting endpoints are captured using either (i) time series methods (exponential smoothing) or (ii) long‐range survey forecasts of either interest rates or inflation and output growth, or (iii) exponentially smoothed realizations of these macro variables. Allowing for…

比较尾部密度预测的似然评分规则AI 解读

We propose new scoring rules based on conditional and censored likelihood for assessing the predictive accuracy of competing density forecasts over a specific region of interest, such as the left tail in financial risk management. These scoring rules can be interpreted in terms of Kullback-Leibler divergence between weighted versions of the density forecast and the true density. Existing scoring rules based on weighted likelihood favor density forecasts with more probability…

历史视角下的协整AI 解读

We analyse the impact of the Engle and Granger (1987) article by means of its citations over time, and find evidence of a second life starting in the new millennium. Next, we propose a possible explanation of the success of this citation classic. We argue that the conditions for its success were just right at the time of its appearance, because of the growing emphasis on time series properties in econometric modelling, the empirical importance of stochastic trends, the…

领先指标对经济周期峰值的领先时间是否长于谷值？AI 解读

We develop a novel Markov switching vector autoregressive model to investigate the possibility that leading indicators have different lead times at business cycle peaks and at troughs. In this model, coincident and leading indicators share a common Markov state process, but their cycles are nonsynchronous, with the nonsynchronicity varying across regimes. An application shows that on average the Conference Board’s Composite Leading Index leads the Composite Coincident Index…

利用已实现极差测量波动率AI 解读

Realized variance, being the summation of squared intra-day returns, has quickly gained popularity as a measure of daily volatility. Following Parkinson (1980) we replace each squared intra-day return by the high-low range for that period to create a novel and more efficient estimator called the realized range. In addition we suggest a bias-correction procedure to account for the effects of microstructure frictions based upon scaling the realized range with the average level…

摘要暂缺

统计发布机构做对了吗？数据理性与经济周期不对称AI 解读

This article provides new evidence on the rationality of early releases of industrial production (IP) and producer price index (PPI) data. Rather than following the usual practice of examining only first available and fully revised data, we examine the entire revision history for each variable. Thus we are able to assess, for example, whether earlier releases of data are in any sense “less” rational than later releases, and when data become rational. Our findings suggest…

美国制造业部门的多层面板平滑转换自回归模型AI 解读

Abstract We introduce a multi‐level smooth transition model for a panel of time series, which can be used to examine the presence of common nonlinear business cycle features across many variables. The model is positioned in between a fully pooled model, which imposes such common features, and a fully heterogeneous model, which allows for unrestricted nonlinearity. We introduce a second‐stage model linking the parameters that determine the timing of the switches between…

时变平滑转换自回归模型AI 解读

perceived as competing alternatives to linearity. In this article we study the so-called time-varying smooth transition autoregressive (TV-STAR) model, which can be used both for describing simultaneous nonlinearity and structural change and for distinguishing between these features. Two modeling strategies for empirical specification of TV-STAR models are developed. Monte Carlo simulations show that neither of the two strategies dominates the other. A…

摘要暂缺

存在加性异常值时的 ARCH 检验AI 解读

In this paper we investigate the properties of the Lagrange Multiplier (LM) test for autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) in the presence of additive outliers (AOs). We show analytically that both the asymptotic size and power are adversely affected if AOs are neglected: the test rejects the null hypothesis of homoscedasticity too often when it is in fact true, while the test has difficulty detecting genuine GARCH effects. Several…

存在加性异常值时的 ARCH 检验AI 解读

In this paper we investigate the properties of the Lagrange Multiplier (LM) test for autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) in the presence of additive outliers (AOs). We show analytically that both the asymptotic size and power are adversely affected if AOs are neglected: the test rejects the null hypothesis of homoscedasticity too often when it is in fact true, while the test has difficulty detecting genuine GARCH effects. Several…

存在异常值时的平滑转换非线性检验AI 解读

Regime-switching models, like the smooth transition autoregressive (STAR) model, are typically applied to time series of moderate length. Hence, the nonlinear features that these models intend to describe may be reflected in only a few observations. Conversely, neglected outliers in a linear time series of moderate length may incorrectly suggest STAR (or other) type(s of) nonlinearity. In this article, the authors propose outlier robust tests for STAR-type nonlinearity.…