Search results for "Autoregressive conditional heteroskedasticity"
showing 10 items of 24 documents
Forecasting the volatility of biofuel feedstock prices: the US evidence
2019
Given that, nowadays, 40% of the US corn crop is used for biofuel production, there is a growing concern that the rise in biofuel production might lead to an increase in food prices. However, it is also obvious that significant growth in biofuel use has minimized the demand for fossil fuel and has hence reduced the volume of carbon emissions. It is therefore crucial to model corn market volatility precisely because such an estimate could play a vital role in stabilizing food and biofuel market prices. For this purpose, we consider using the information content of the corn implied volatility (CIV) index to predict the corn futures market return volatility. Using symmetric and asymmetric GARC…
Modeling Conditional Skewness in Stock Returns
2007
Abstract In this paper, we propose a new GARCH-in-Mean (GARCH-M) model allowing for conditional skewness. The model is based on the so-called z distribution capable of modeling skewness and kurtosis of the size typically encountered in stock return series. The need to allow for skewness can also be readily tested. The model is consistent with the volatility feedback effect in that conditional skewness is dependent on conditional variance. Compared to previously presented GARCH models allowing for conditional skewness, the model is analytically tractable, parsimonious and facilitates straightforward interpretation.Our empirical results indicate the presence of conditional skewness in the mon…
Dosage individualization of erythropoietin using a profile-dependent support vector regression
2003
The external administration of recombinant human erythropoietin is the chosen treatment for those patients with secondary anemia due to chronic renal failure in periodic hemodialysis. The objective of this paper is to carry out an individualized prediction of the EPO dosage to be administered to those patients. The high cost of this medication, its side-effects and the phenomenon of potential resistance which some individuals suffer all justify the need for a model which is capable of optimizing dosage individualization. A group of 110 patients and several patient factors were used to develop the models. The support vector regressor (SVR) is benchmarked with the classical multilayer percept…
Asymmetric covariance in spot-futures markets
2003
This article studies how the spot-futures conditional covariance matrix responds to positive and negative innovations. The main results of the article are achieved by obtaining the Volatility Impulse Response Function (VIRF) for asymmetric multivariate GARCH structures, extending Lin (1997) findings for symmetric GARCH models. This theoretical result is general and can be applied to analyze covariance dynamics in any financial system. After testing how multivariate GARCH models clean up volatility asymmetries, the Asymmetric VIRF is computed for the Spanish stock index IBEX-35 and its futures contract. The empirical results indicate that the spot-futures variance system is more sensitive to…
Price and volatility dynamics between electricity and fuel costs: Some evidence for Spain
2012
Abstract The purpose of this study is to investigate the causal linkages between the Spanish electricity, Brent crude oil and Zeebrugge (Belgium) natural gas 1-month-ahead forward prices. Following Lutkepohl et al. (2004), we control for the presence of a structural change in the series and then we use the Johansen cointegration test and a vector error correction model (VECM) to embrace the analysis. Additionally, a multivariate generalized autoregressive conditional heteroskedastic (GARCH) model is applied to explore volatility interactions between the three markets involved in the study. Our findings reveal that Brent crude oil and Zeebrugge natural gas forward prices play a prominent rol…
A critical view on temperature modelling for application in weather derivatives markets
2012
In this paper we present a stochastic model for daily average temperature. The model contains seasonality, a low-order autoregressive component and a variance describing the heteroskedastic residuals. The model is estimated on daily average temperature records from Stockholm (Sweden). By comparing the proposed model with the popular model of Campbell and Diebold (2005), we point out some important issues to be addressed when modelling the temperature for application in weather derivatives market.
The Economic Value of Volatility Transmission Between the Stock and Bond Markets
2008
This study has two main objectives. Firstly, volatility transmission between stocks and bonds in European markets is studied using the two most important financial assets in these fields: the DJ Euro Stoxx 50 index futures contract and the Euro Bund futures contract. Secondly, a trading rule for the major European futures contracts is designed. This rule can be applied to different markets and assets to analyze the economic significance of volatility spillovers observed between them. The results indicate that volatility spillovers take place in both directions and that the stock-bond trading rule offers very profitable returns after transaction costs. These results have important implicatio…
A Test of Covariance-Matrix Forecasting Methods
2015
Providing a more accurate covariance matrix forecast can substantially improve the performance of optimized portfolios. Using out-of-sample tests, in this article the author evaluates alternative covariance matrix-forecasting methods by looking at: (1) their forecast accuracy, (2) their ability to track the volatility of a minimum-variance portfolio, and (3) their ability to keep the volatility of a minimum-variance portfolio at a target level. The author finds large differences between the methods. The results suggest that shrinking the sample covariance matrix improves neither the forecast accuracy nor the performance of minimum-variance portfolios. In contrast, switching from the sample …
A Mixture Multiplicative Error Model for Realized Volatility
2006
A multiplicative error model with time-varying parameters and an error term following a mixture of gamma distributions is introduced. The model is fitted to the daily realized volatility series of deutschemark/dollar and yen/dollar returns and is shown to capture the conditional distribution of these variables better than the commonly used autoregressive fractionally integrated moving average model. The forecasting performance of the new model is found to be, in general, superior to that of the set of volatility models recently considered by Andersen et al. (2003, Econometrica 71, 579--625) for the same data. Copyright 2006, Oxford University Press.
Nonlinear GARCH models for highly persistent volatility
2005
In this paper we study new nonlinear GARCH models mainly designed for time series with highly persistent volatility. For such series, conventional GARCH models have often proved unsatisfactory because they tend to exaggerate volatility persistence and exhibit poor forecasting ability. Our main emphasis is on models that are similar to previously introduced smooth transition GARCH models except for the novel feature that a lagged value of conditional variance is used as the transition variable. This choice of the transition variable corresponds to the idea that high persistence in conditional variance is related to relatively infrequent changes in regime. U sing the theory of Markov chains w…