Purpose of the paper The Basel Committee regulations require the estimation of Value-at-Risk at 99% confidence level for a 10-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically distributed real-estate portfolio returns. The purpose of the paper is to estimate accurate 10-day-ahead 99% VaR...

in this paper, we investigate the performance of parametric arch class models to forecast out-of-sample var for two portfolios of tehran stock exchange (tse) companies (market portfolio and a portfolio of 50 liquid companies), using a number of distributional assumptions and sample sizes at low and high confidence levels. we find, first, that leptokurtic distributions are able to produce better...

We evaluate the relative accuracy of real GDP forecasts made by 25 Japanese economists over the past 15 years. The encompassing test reveals the following results. (a) All of these forecasts outperform naïve forecasts. (b) Their current-year forecasts are inferior to the corresponding forecasts of VAR, VECM, or the Japanese government. (c) Their year-ahead forecasts are inferior to the correspo...

This paper compares the out-of-sample forecasting accuracy of five classes of time series models for market shares of the six most important Portuguese car market competitors over different horizons. As representative time series models I employ a random walk with drift (Naive), a univariate ARIMA, a near-VAR and a general BVAR. The out-of-sample forecasts are also compared against forecasts ge...

Short-term wind and wind power forecasts are required for the reliable and economic operation of power systems with significant wind power penetration. This thesis presents new statistical techniques for producing forecasts at multiple locations using spatiotemporal information. Forecast horizons of up to 6 hours are considered for which statistical methods outperform physical models in general...

In this paper, we investigate whether incorporating common factors of CPI sub-aggregates into forecasting models increases the accuracy of forecasts of inflation. We extract factors by both static and dynamic factor models and then embed them in ARMA and VAR models. Using quarterly data of Iran’s CPI and its sub-aggregates, the models are estimated over 1990:2 to 2008:2 and out of sample ...

This paper analyzes whether a wealth of information contained in 126 monthly series used by large-scale Bayesian Vector Autoregressive (LBVAR) models, as well as Factor Augmented Vector Autoregressive (FAVAR) models, either Bayesian or classical, can prove to be more useful in forecasting real house price growth rate of the nine census divisions of the US, compared to the small-scale VAR models...

In this paper the value at risk (VaR) forecasts are compared using three different GARCH models; ARCH(1), GARCH(1,1) and EGARCH(1,1). The implemented method is a one-day ahead out of sample forecast of the VaR. The forecasts are evaluated using the Kupiec test with a five percent significance level. The focus is on three different markets; commodities, equities and exchange rates. The goal of t...

This paper proposes a robust semiparametric bootstrap method to estimate predictive distributions of GARCH-type models. The method is based on a robust estimation of parametric GARCH models and a robustified resampling scheme for GARCH residuals that controls bootstrap instability due to outlying observations. A Monte Carlo simulation shows that our robust method provides more accurate VaR fore...

We compare the performance of a subset of CBO’s economic forecasts against that of an unrestricted vector autoregression (VAR) model. We evaluate forecasts of real economic indicators as well as budget-related nominal statistics. We find that under most specifications, the VAR performs competitively with, if slightly worse than, the corresponding CBO forecasts at up to 20 quarters. Therefore, a...