Value at risk (VaR) is the standard measure of market risk used by financial institutions. Interpreting the VaR as the quantile of future portfolio values conditional on current information, the conditional autoregressive value at risk (CAViaR) model specifies the evolution of the quantile over time using an autoregressive process and estimates the parameters with regression quantiles. Utilizin...

The purpose of this paper is to analyze in bivariate vector autoregression the relationship between feedback in stochastic systems, Granger causality and a measure of dissimilarity between ARMA models. In particular, we consider a bivariate vector autoregressive processes of order p (a bivariate VAR(p) process) and we prove if the distance between the univariate ARMA models implied by the VAR r...

In this paper, we take a step forward from the existing monetary literature, which is largely dominated by vector autoregressive (VAR) and structural vector autoregressive (SVAR) models, and apply a vector autoregressive moving average (VARMA) methodology for modelling and analysing Malaysian monetary policy. The Malaysian economy is an interesting case study of a small open economy with capita...

A comparative assessment of two time–domain output–only vector structural identification methods, namely a Vector AutoRegressive (VAR) and a stochastic subspace method, is presented via their application to a laboratory cable–stayed bridge structure. A brief overview of the estimation methods is provided, while model order selection and validation are discussed. The modal frequencies and dampin...

Empirical time series are subject to observational noise. Naïve approaches that estimate parameters in stochastic models for such time series are likely to fail due to the error-in-variables challenge. State space models (SSM) explicitly include observational noise. Applying the expectation maximization (EM) algorithm together with the Kalman filter constitute a robust iterative procedure to es...

We investigated the connectedness of returns and volatility clean energy stock, technology crude oil, natural gas, investor sentiment based on time-varying parameter vector autoregressive (TVP-VAR) approach. The empirical results indicate that average total is higher in system than return system. has a weak impact stock. Our show dynamic across assets varies with time. Furthermore, increases si...

In this paper, the spatio-temporal (multi-channel) linear models, which use temporal and the neighbouring wind speed measurements around the target location, for the best short-term wind speed forecasting are investigated. Multi-channel autoregressive moving average (MARMA) models are formulated in matrix form and efficient linear prediction coefficient estimation techniques are first used and ...

Previously, quality control and improvement researchers discussed multivariate control charts for independent processes and univariate control charts for autocorrelated processes separately. We combine the two topics and propose vector autoregressive (VAR) control charts for multivariate autocorrelated processes. In addition, we estimateAR(p) models instead ofARMAmodels for the systematic cause...

Traditional literature on statistical quality control discusses separately multivariate control charts for independent processes and univariate control charts for autocorrelated processes. We extend univariate residual monitoring to the multivariate environment, and propose using vector autoregressive residuals (VAR) to monitor multivariate processes in the presence of serial correlation. We ma...

This paper investigates the linear and nonlinear effects of financial regulation policy uncertainty shocks on US macroeconomic aggregates within a Vector Autoregressive (VAR) framework. Financial regulation policy uncertainty (FRPU) is quantified with a news-based index developed by Baker et al. (2013). Particular attention is paid to the reaction of corporate credit spreads to FRPU shocks. The...