This paper addresses the problem of nonparametric estimation of the conditional expected shortfall (CES) which has gained popularity in financial risk management. We propose a new nonparametric estimator of the CES. The proposed estimator is defined as a conditional counterpart of the sample average estimator of the unconditional expected shortfall, where the empirical distribution function is ...

Multivariate ordinal categorical data is encountered in many fields of research. For analysis and data reduction the conditional independence properties of these data are studied in graphical models. However, to simulate multivariate ordinal data with a specific conditional independence structure, for use in simulation studies or computer intensive methods of inference, is non-trivial. We prese...

To capture the missed information in the standardized errors by parametric multivariate generalized autoregressive conditional heteroskedasticity (MV-GARCH) model, we propose a new semiparametric MV-GARCH (SM-GARCH) model. This SM-GARCH model is a twostep model: firstly estimating parametric MV-GARCH model, then using nonparametric skills to model the conditional covariance matrix of the standa...

ARCH and GARCH models are widely used to model financial market volatilities in risk management applications. Considering a GARCH model with heavy-tailed innovations, we characterize the limiting distribution of an estimator of the conditional Value-at-Risk (VaR), which corresponds to the extremal quantile of the conditional distribution of the GARCH process. We propose two methods, the normal ...

We propose a class of specification tests for Autoregressive Conditional Duration (ACD) models that are robust to time-varying conditional dispersion and higher order conditional moments of unknown form. Both linear and nonlinear ACD models are covered. No specific estimation method is required, and the tests have a convenient null asymptotic N(0,1) distribution. To reduce the impact of paramet...

We derive a local linear estimator of generalized impulse response (GIR) functions for nonlinear conditional heteroskedastic autoregressive processes and show its asymptotic normality. We suggest a plug-in bandwidth based on the derived asymptotically optimal bandwidth. A local linear estimator for the conditional variance function is proposed which has simpler bias than the standard estimator....

In order to describe the comovements in both conditional mean and conditional variance of high dimensional nonstationary time series by dimension reduction, we introduce the conditional heteroscedasticity with factor structure to the error correction model. The new model is called the error correction–volatility factor model. Some specification and estimation approaches are developed. In partic...

deviding both the subjects and the predicates of the categorical propositions into actuality and verity, afzal al-din khunaji states the actuality-parts as adjectives, but verity-parts in three forms: as simple, as relational and as conditional. since the adjectives are formulated in modern logic by conjunction, we can formulate actuality-parts as conjunctions. verity-parts can be formulated at...

A considerable amount of studies have been established on conditional reasoning supporting mental model theory of propositional reasoning. Mental model theory proposed by Johnson- larid and Byrne is an explanation of someone's thought process about how something occurs in the real world. Conditional reasoning as a kind of reasoning is the way to speak about possibilities or probabilities. The a...

When we use simulation to estimate the performance of a stochastic system, the simulation often contains input models that were estimated from real-world data; therefore, there is both simulation and input uncertainty in the performance estimates. In this paper, we provide a method to measure the overall uncertainty while simultaneously reducing the influence of simulation estimation error due ...