In this paper we discuss sensitivity of forecast with respect to the information set considered in prediction; we define a sensitivity measure called impact factor, IF. We calculate this measure in VAR processes integrated of order 0, 1 and 2. For VAR processes this measure is a simple function of the impulse response coefficients. For integrated VAR systems this measure is shown to have a dire...

When dealing with market risk under the Basel II Accord, variation pays in the form of lower capital requirements and higher profits. Typically, GARCH type models are chosen to forecast Value-at-Risk (VaR) using a single risk model. In this paper we illustrate two useful variations to the standard mechanism for choosing forecasts, namely: (i) combining different forecast models for each period,...

The traditional Value at Risk (VaR) is a very popular tool measuring market risk, but it does not incorporate liquidity risk. This paper proposes an extended VaR model to integrate liquidity risk for intraday trading strategies using high frequency order book data. We estimate the one step ahead liquidity adjusted intraday VaR called(LAIVaR) for both bid and ask positions, considering several t...

In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of sola...

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural...

The recent financial crisis has raised numerous questions about the accuracy of value at risk (VaR) as a tool to quantify extreme losses. In this paper we develop data driven VaR approaches that are based on the principle of optimal combination and that provide robust and precise VaR forecasts for periods when they are needed most, such as the recent financial crisis. Within a comprehensive com...

This paper focuses on forecasting quarterly nominal global energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes Raissi (2018). includes a number potentially informative macroeconomic variables for 33 largest economies, overall accounting more than 80% GDP. To deal with information this large database, we apply dynamic factor models based pen...

This paper reports the near term forecasting power of a large Global Vector Autoregressive (GVAR) model originally developed by Pesaran, Schuermann and Weiner, PSW, (2004) and subsequently fine-tuned and re-estimated over 1979Q1-1 The GVAR model explicitly specifies interdependencies between different countries and sub-regions in terms of three transparent channels: i) domestic variables are re...

Purpose – This article examines internet search query data provided by ‘Google Trends’, with respect to its ability to serve as a sentiment indicator and improve commercial real estate forecasting models for transactions and price indices. Methodology – The study uses data from CoStar the largest data providers of US commercial real estate repeat sales indices. We design three groups of models:...