论文检索

Summary A novel dynamic model for joint estimation of multiple quantiles of a time series conditionally on a set of covariates is presented. The model preserves quantile monotonicity and allows for a clear interpretation of covariate effects across quantiles. Model parameters are estimated using a two-step M-estimator. The resulting estimator is consistent, and its finite sample properties are analyzed through simulations. The new model is used to study the impact of…

未观测成分模型、近似滤波与动态自适应混合模型AI 解读

State estimation in unobserved component models with parameter uncertainty is traditionally performed through approximate filters, where Gaussian distributions with given moments are employed to replace otherwise intractable conditional densities. This paper re-examines signal-plus-noise models where parameter uncertainty is induced by a latent variable that may assume a fixed number of states. First, it is shown that, for these models, the approximate filters commonly…

状态切换整数值自回归模型AI 解读

Time series of counts often display complex dynamic and distributional characteristics. For this reason, we develop a flexible framework combining the integer-valued autoregressive (INAR) model with a latent Markov structure, leading to the hidden Markov model-INAR (HMM-INAR). First, we illustrate conditions for the existence of an ergodic and stationary solution and derive closed-form expressions for the autocorrelation function and its components. Second, we show…

多个分位数的半参数建模AI 解读

We develop a semiparametric model to track a large number of quantiles of a time series. The model satisfies the condition of non-crossing quantiles and the defining property of fixed quantiles. A key feature of the specification is that the updating scheme for time-varying quantiles at each probability level is based on the gradient of the check loss function. Theoretical properties of the proposed model are derived such as weak stationarity of the quantile process and…

具有一般杠杆效应设定的随机波动率模型AI 解读

We introduce a new stochastic volatility model that postulates a general correlation structure between the shocks of the measurement and log volatility equations at different temporal lags. The resulting specification is able to better characterize the leverage effect and propagation in financial time series. Furthermore, it nests other asymmetric volatility models and can be used for testing and diagnostics. We derive the simulated maximum likelihood and quasi maximum…

高频价格的动态离散混合模型AI 解读

The tick structure of the financial markets entails discreteness of stock price changes. Based on this empirical evidence, we develop a multivariate model for discrete price changes featuring a mechanism to account for the large share of zero returns at high frequency. We assume that the observed price changes are independent conditional on the realization of two hidden Markov chains determining the dynamics and the distribution of the multivariate time series at hand. We…

多元整数值时间序列的层级马尔可夫转换模型AI 解读

We propose a new flexible dynamic model for multivariate nonnegative integer-valued time-series. Observations are assumed to depend on the realization of two unobserved integer-valued stochastic variables which control for the time- and cross-dependence of the data. We provide conditional and unconditional (cross)-moments implied by the model, as well as the limiting distribution of the series. An Expectation–Maximization algorithm for maximum likelihood estimation of the…

马尔可夫切换广义自回归得分 Copula 模型及其系统性风险应用AI 解读

Summary Recent financial disasters have emphasized the need to accurately predict extreme financial losses and their consequences for the institutions belonging to a given financial market. The ability of econometric models to predict extreme events strongly relies on their flexibility to account for the highly nonlinear and asymmetric dependence patterns observed in financial time series. In this paper, we develop a new class of flexible copula models where the dependence…

具有自回归与异方差扰动的动态空间自回归模型AI 解读

Summary We propose a new class of models specifically tailored for spatiotemporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, that is, SARAR(1, 1), by exploiting the recent advancements in score‐driven (SD) models typically used in time series econometrics. In particular, we allow for time‐varying spatial autoregressive coefficients as well as time‐varying regressor coefficients and…