M.J. McAleer (Michael)
http://repub.eur.nl/ppl/6150/
List of Publicationsenhttp://repub.eur.nl/eur_signature.png
http://repub.eur.nl/
RePub, Erasmus University RepositoryResearch Ideas for the Journal of Health & Medical Economics: Opinion
http://repub.eur.nl/pub/78715/
Tue, 01 Sep 2015 00:00:01 GMT<div>C-L. Chang</div><div>M.J. McAleer</div>
The purpose of this Opinion article is to discuss some ideas that might lead to papers that are suitable for publication in the Journal of Health and Medical Economics. The suggestions include the affordability and sustainability of universal health care insurance, monitoring and managing costs associated with public and private health and medical care coverage, panel data models based on industrial organization and corporate finance, and health and medical investment finance.Research Ideas for the Journal of Informatics and Data Mining: Opinion
http://repub.eur.nl/pub/78716/
Tue, 01 Sep 2015 00:00:01 GMT<div>M.J. McAleer</div>
The purpose of this Opinion article is to discuss some ideas that might lead to papers that are suitable for publication in the Journal of Informatics and Data Mining. The suggestions include the analysis of citations databases, PI-BETA (Papers Ignored – By Even The Authors), model specification and testing, pre-test bias and data mining, international rankings of academic journals based on citations, international rankings of academic institutions based on citations and other factors, and case studies in numerous disciplines in the sciences and social sciences.Behavioural, Financial, and Health & Medical Economics: A Connection
http://repub.eur.nl/pub/78718/
Tue, 01 Sep 2015 00:00:01 GMT<div>C-L. Chang</div><div>M.J. McAleer</div><div>W-K. Wong</div>
This Opinion article briefly reviews some of the literature in behavioural and financial economics that are related to health & medical economics. We then discuss some of the research on behavioural and financial economics that could be extended to health & medical economics beyond the existing areas in theory, statistics and econometrics.Industrial Agglomeration and Use of the Internet
http://repub.eur.nl/pub/78714/
Sat, 01 Aug 2015 00:00:01 GMT<div>C-L. Chang</div><div>M.J. McAleer</div><div>Y-C. Wu</div>
Taiwan has been hailed as a world leader in the development of global innovation and industrial clusters for the past decade. This paper investigates the effects of industrial agglomeration on the use of the internet and internet intensity for Taiwan manufacturing firms, and analyses whether the relationships between industrial agglomeration and total expenditure on internet usage for industries are substitutes or complements. The sample observations are based on 153,081 manufacturing plants, and covers 26 2-digit industry categories and 358 geographical townships in Taiwan. The Heckman selection model is used to adjust for sample selectivity for unobservable data for firms that use the internet. The empirical results from two-stage estimation show that: (1) for the industry overall, a higher degree of industrial agglomeration will not affect the probability that firms will use the internet, but will affect the total expenditure on internet usage; and (2) for 2-digit industries, industrial agglomeration generally decreases the total expenditure on internet usage, which suggests that industrial agglomeration and total expenditure on internet usage are substitutes.Daily Market News Sentiment and Stock Prices
http://repub.eur.nl/pub/78713/
Tue, 28 Jul 2015 00:00:01 GMT<div>D.E. Allen</div><div>M.J. McAleer</div><div>A.K. Singh</div>
In recent years there has been a tremendous growth in the influx of news related to traded assets in international financial markets. This financial news is now available via print media but also through real-time online sources such as internet news and social media sources. The increase in the availability of financial news and investor’s ease of access to it has a potentially significant impact on market price formation as these news items are swiftly transformed into investors sentiment which in turn drives prices. Various commercial agencies have started developing their own financial news data sets which are used by investors and traders to support their algorithmic trading strategies. Thomson Reuters News Analytics (TRNA)1 is one such data set. In this study we use the TRNA data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index component companies. We use these daily DJIA market sentiment scores to study the influence of financial news sentiment scores on the stock prices of these companies using a multi-factor model. We use an augmented Fama French Three Factor Model to evaluate the additional effects of financial news sentiment on stock prices in the context of this model. Our results suggest that even when market factors are taken into account, sentiment scores have a significant effect on Dow Jones constituent company returns and that lagged daily sentiment scores are often significant, suggesting that information compounded in these scores is not immediately reflected in security prices and related return series.Multivariate Volatility Impulse Response Analysis of GFC News Events
http://repub.eur.nl/pub/78463/
Wed, 01 Jul 2015 00:00:01 GMT<div>D.E. Allen</div><div>M.J. McAleer</div><div>R.J. Powell</div>
__Abstract__
This paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index from the London Stock Exchange, from 3 January 2005 to 31 January 2015. This period captures both the Global Financial Crisis (GFC) and the subsequent European Sovereign Debt Crisis (ESDC). The attraction of the HH approach is that it involves a novel application of the concept of impulse response functions, tracing the effects of independent shocks on volatility through time, while avoiding typical orthogonalization and ordering problems. Volatility impulse response functions (VIRF) provide information about the impact of independent shocks on volatility. HH’s VIRF extends a framework provided by Koop et al. (1996) for the analysis of impulse responses. This approach is novel because it explores the effects of shocks to the conditional variance, as opposed to the conditional mean. HH use the fact that GARCH models can be viewed as being linear in the squares, and that multivariate GARCH models are known to have a VARMA representation with non-Gaussian errors. They use this particular structure to calculate conditional expectations of volatility analytically in their VIRF analysis. A Jordan decomposition of Σt is used to obtain independent and identically distributed innovations. A general issue in the approach is the choice of baseline volatilities. VIRF is defined as the expectation of volatility conditional on an initial shock and on history, minus the baseline expectation that conditions on history. This makes the process endogenous, but the choice of the baseline shock within the data set makes a difference. We explore the impact of three different shocks, the first marking the onset of the GFC, which we date as 9 August 2007 (GFC1). This began with the seizure in the banking system precipitated by BNP Paribas announcing that it was ceasing activity in three hedge funds that specialised in US mortgage debt. It took a year for the financial crisis to come to a head, but it did so on 15 September 2008, when the US government allowed the investment bank Lehman Brothers to go bankrupt (GFC2). The third shock is 9 May 2010, which marked the point at which the focus of concern switched from the private sector to the public sector. A further contribution of this paper is the inclusion of leverage, or asymmetric effects. Our modelling is undertaken in the context of a multivariate GARCH model featuring pre-whitened return series, which are then analysed using both BEKK and diagonal BEKK models with the t-distribution. A key result is that the impact of negative shocks is larger, in terms of the effects on variances and covariances, but shorter in duration, in this case a difference between three and six months, in the context of the return series.Multivariate Volatility Impulse Response Analysis of GFC News Events
http://repub.eur.nl/pub/78711/
Wed, 01 Jul 2015 00:00:01 GMT<div>D.E. Allen</div><div>M.J. McAleer</div><div>R.J. Powell</div><div>A.K. Singh</div>
This paper applies the Hafner and Herwartz (2006) (hereafter HH) approach to the analysis of multivariate GARCH models using volatility impulse response analysis. The data set features ten years of daily returns series for the New York Stock Exchange Index and the FTSE 100 index from the London Stock Exchange, from 3 January 2005 to 31 January 2015. This period captures both the Global Financial Crisis (GFC) and the subsequent European Sovereign Debt Crisis (ESDC). The attraction of the HH approach is that it involves a novel application of the concept of impulse response functions, tracing the effects of independent shocks on volatility through time, while avoiding typical orthogonalization and ordering problems. Volatility impulse response functions (VIRF) provide information about the impact of independent shocks on volatility. HH’s VIRF extends a framework provided by Koop et al. (1996) for the analysis of impulse responses. This approach is novel because it explores the effects of shocks to the conditional variance, as opposed to the conditional mean. HH use the fact that GARCH models can be viewed as being linear in the squares, and that multivariate GARCH models are known to have a VARMA representation with non-Gaussian errors. They use this particular structure to calculate conditional expectations of volatility analytically in their VIRF analysis. A Jordan decomposition of Σt is used to obtain independent and identically distributed innovations. A general issue in the approach is the choice of baseline volatilities. VIRF is defined as the expectation of volatility conditional on an initial shock and on history, minus the baseline expectation that conditions on history. This makes the process endogenous, but the choice of the baseline shock within the data set makes a difference. We explore the impact of three different shocks, the first marking the onset of the GFC, which we date as 9 August 2007 (GFC1). This began with the seizure in the banking system precipitated by BNP Paribas announcing that it was ceasing activity in three hedge funds that specialised in US mortgage debt. It took a year for the financial crisis to come to a head, but it did so on 15 September 2008, when the US government allowed the investment bank Lehman Brothers to go bankrupt (GFC2). The third shock is 9 May 2010, which marked the point at which the focus of concern switched from the private sector to the public sector. A further contribution of this paper is the inclusion of leverage, or asymmetric effects. Our modelling is undertaken in the context of a multivariate GARCH model featuring pre-whitened return series, which are then analysed using both BEKK and diagonal BEKK models with the t-distribution. A key result is that the impact of negative shocks is larger, in terms of the effects on variances and covariances, but shorter in duration, in this case a difference between three and six months, in the context of the return series.Volatility Spillovers Between Energy and Agricultural Markets:
A Critical Appraisal of Theory and Practice
http://repub.eur.nl/pub/78349/
Mon, 01 Jun 2015 00:00:01 GMT<div>C-L. Chang</div><div>Y. Li</div><div>M.J. McAleer</div>
__Abstract__
Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the BEKK and DCC models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice
http://repub.eur.nl/pub/78373/
Mon, 01 Jun 2015 00:00:01 GMT<div>C-L. Chang</div><div>Y. Li</div><div>M.J. McAleer</div>
__Abstract__
Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the BEKK and DCC models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.International Technology Diffusion of Joint
and Cross-border Patents
http://repub.eur.nl/pub/78143/
Fri, 15 May 2015 00:00:01 GMT<div>C-L. Chang</div><div>M.J. McAleer</div><div>J-T. Tang</div>
__Abstract__
With the advent of globalization, economic and financial interactions among countries
have become widespread. Given technological advancements, the factors of production
can no longer be considered to be just labor and capital. In the pursuit of economic
growth, every country has sensibly invested in international cooperation, learning,
innovation, technology diffusion and knowledge. In this paper, we use a panel data set
of 40 countries from 1981 to 2008 and a negative binomial model, using a novel set of
cross-border patents and joint patents as proxy variables for technology diffusion, in
order to investigate such diffusion. The empirical results suggest that, if it is desired to
shift from foreign to domestic technology, it is necessary to increase expenditure on
R&D for business enterprises and higher education, exports and technology. If the
focus is on increasing bilateral technology diffusion, it is necessary to increase
expenditure on R&D for higher education and technology.A Stochastic Dominance Approach to the Basel III Dilemma: Expected Shortfall or VaR?
http://repub.eur.nl/pub/78155/
Fri, 01 May 2015 00:00:01 GMT<div>C-L. Chang</div><div>J.A. Jiménez-Martín</div><div>M.J. McAleer</div><div>T. Pérez-Amaral</div>
__Abstract__
The Basel Committee on Banking Supervision (BCBS) (2013) recently proposed shifting the quantitative risk metrics system from Value-at-Risk (VaR) to Expected Shortfall (ES). The BCBS (2013) noted that “a number of weaknesses have been identified with using VaR for determining regulatory capital requirements, including its inability to capture tail risk” (p. 3). For this reason, the Basel Committee is considering the use of ES, which is a coherent risk measure and has already become common in the insurance industry, though not yet in the banking industry. While ES is mathematically superior to VaR in that it does not show “tail risk” and is a coherent risk measure in being subadditive, its practical implementation and large calculation requirements may pose operational challenges to financial firms. Moreover, previous empirical findings based only on means and standard deviations suggested that VaR and ES were very similar in most practical cases, while ES could be less precise because of its larger variance. In this paper we find that ES is computationally feasible using personal computers and, contrary to previous research, it is shown that there is a stochastic difference between the 97.5% ES and 99% VaR. In the Gaussian case, they are similar but not equal, while in other cases they can differ substantially: in fat-tailed conditional distributions, on the one hand, 97.5%-ES would imply higher risk forecasts, while on the other, it provides a smaller down-side risk than using the 99%-VaR. It is found that the empirical results in the paper generally support the proposals of the Basel Committee.International Technology Diffusion of Joint and Cross-border Patents
http://repub.eur.nl/pub/78291/
Fri, 01 May 2015 00:00:01 GMT<div>C-L. Chang</div><div>M.J. McAleer</div><div>J-T. Tang</div>
__Abstract__
With the advent of globalization, economic and financial interactions among countries have become widespread. Given technological advancements, the factors of production can no longer be considered to be just labor and capital. In the pursuit of economic growth, every country has sensibly invested in international cooperation, learning, innovation, technology diffusion and knowledge. In this paper, we use a panel data set of 40 countries from 1981 to 2008 and a negative binomial model, using a novel set of cross-border patents and joint patents as proxy variables for technology diffusion, in order to investigate such diffusion. The empirical results suggest that, if it is desired to shift from foreign to domestic technology, it is necessary to increase expenditure on R&D for business enterprises and higher education, exports and technology. If the focus is on increasing bilateral technology diffusion, it is necessary to increase expenditure on R&D for higher education and technology.A Stochastic Dominance Approach to the Basel III Dilemma: Expected Shortfall or VaR?
http://repub.eur.nl/pub/78292/
Fri, 01 May 2015 00:00:01 GMT<div>C-L. Chang</div><div>J.A. Jiménez-Martín</div><div>E. Maasoumi</div><div>M.J. McAleer</div><div>T. Pérez-Amaral</div>
__Abstract__
The Basel Committee on Banking Supervision (BCBS) (2013) recently proposed shifting the quantitative risk metrics system from Value-at-Risk (VaR) to Expected Shortfall (ES). The BCBS (2013) noted that “a number of weaknesses have been identified with using VaR for determining regulatory capital requirements, including its inability to capture tail risk” (p. 3). For this reason, the Basel Committee is considering the use of ES, which is a coherent risk measure and has already become common in the insurance industry, though not yet in the banking industry. While ES is mathematically superior to VaR in that it does not show “tail risk” and is a coherent risk measure in being subadditive, its practical implementation and large calculation requirements may pose operational challenges to financial firms. Moreover, previous empirical findings based only on means and standard deviations suggested that VaR and ES were very similar in most practical cases, while ES could be less precise because of its larger variance. In this paper we find that ES is computationally feasible using personal computers and, contrary to previous research, it is shown that there is a stochastic difference between the 97.5% ES and 99% VaR. In the Gaussian case, they are similar but not equal, while in other cases they can differ substantially: in fat-tailed conditional distributions, on the one hand, 97.5%-ES would imply higher risk forecasts, while on the other, it provides a smaller down-side risk than using the 99%-VaR. It is found that the empirical results in the paper generally support the proposals of the Basel Committee.Bibliometric Rankings of Journals based on the Thomson Reuters Citations Database
http://repub.eur.nl/pub/77925/
Sun, 01 Mar 2015 00:00:01 GMT<div>C-L. Chang</div><div>M.J. McAleer</div>
__Abstract__
Virtually all rankings of journals are based on citations, including self citations by journals and individual academics. The gold standard for bibliometric rankings based on citations data is the widely-used Thomson Reuters Web of Science (2014) citations database, which publishes, among others, the celebrated Impact Factor. However, there are numerous bibliometric measures, also known as research assessment measures, based on the Thomson Reuters citations database, but they do not all seem to have been collected in a single source. The purpose of this paper is to present, define and compare the 16 most well-known Thomson Reuters bibliometric measures in a single source. It is important that the existing bibliometric measures be presented in any rankings papers as alternative bibliometric measures based on the Thomson Reuters citations database can and do produce different rankings, as has been documented in a number of papers in the bibliometrics literature.Bibliometric Rankings of Journals Based on the Thomson Reuters Citations Database
http://repub.eur.nl/pub/78070/
Sun, 01 Mar 2015 00:00:01 GMT<div>C-L. Chang</div><div>M.J. McAleer</div>
__Abstract__
Virtually all rankings of journals are based on citations, including self citations by journals and individual academics. The gold standard for bibliometric rankings based on citations data is the widely-used Thomson Reuters Web of Science (2014) citations database, which publishes, among others, the celebrated Impact Factor. However, there are numerous bibliometric measures, also known as research assessment measures, based on the Thomson Reuters citations database, but they do not all seem to have been collected in a single source. The purpose of this paper is to present, define and compare the 16 most well-known Thomson Reuters bibliometric measures in a single source. It is important that the existing bibliometric measures be presented in any rankings papers as alternative bibliometric measures based on the Thomson Reuters citations database can and do produce different rankings, as has been documented in a number of papers in the bibliometrics literature.On the Invertibility of EGARCH(p,q )
http://repub.eur.nl/pub/77762/
Sun, 01 Feb 2015 00:00:01 GMT<div>G.G. Martinet</div><div>M.J. McAleer</div>
__Abstract__
Of the two most widely estimated univariate asymmetric conditional volatility models, the exponential GARCH (or EGARCH) specification can capture asymmetry, which refers to the different effects on conditional volatility of positive and negative effects of equal magnitude, and leverage, which refers to the negative correlation between the returns shocks and subsequent shocks to volatility. However, the statistical properties of the (quasi-) maximum likelihood estimator (QMLE) of the EGARCH parameters are not available under general conditions, but only for special cases under highly restrictive and unverifiable conditions, such as EGARCH(1,0) or EGARCH(1,1), and possibly only under simulation. A limitation in the development of asymptotic properties of the QMLE for the EGARCH(p,q) model is the lack of an invertibility condition for the returns shocks underlying the model. It is shown in this paper that the EGARCH(p,q) model can be derived from a stochastic process, for which the invertibility conditions can be stated simply and explicitly. This will be useful in re-interpreting the existing properties of the QMLE of the EGARCH(p,q) parameters.Frontiers in Time Series and Financial
Econometrics:
An Overview
http://repub.eur.nl/pub/77763/
Sun, 01 Feb 2015 00:00:01 GMT<div>S. Ling</div><div>M.J. McAleer</div><div>H. Tong</div>
__Abstract__
Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contributed significantly to time series and financial econometrics, including forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance, prediction of Lévy-driven CARMA processes, functional index coefficient models with variable selection, LASSO estimation of threshold autoregressive models, high dimensional stochastic regression with latent factors, endogeneity and nonlinearity, sign-based portmanteau test for ARCH-type models with heavy-tailed innovations, toward optimal model averaging in regression models with time series errors, high dimensional dynamic stochastic copula models, a misspecification test for multiplicative error models of non-negative time series processes, sample quantile analysis for long-memory stochastic volatility models, testing for independence between functional time series, statistical inference for panel dynamic simultaneous equations models, specification tests of calibrated option pricing models, asymptotic inference in multiple-threshold double autoregressive models, a new hyperbolic GARCH model, intraday value-at-risk: an asymmetric autoregressive conditional duration approach, refinements in maximum likelihood inference on spatial autocorrelation in panel data, statistical inference of conditional quantiles in nonlinear time series models, quasi-likelihood estimation of a threshold diffusion process, threshold models in time series analysis - some reflections, and generalized ARMA models with martingale difference errors.The Impact of Jumps and Leverage in Forecasting Co-Volatility
http://repub.eur.nl/pub/78068/
Sun, 01 Feb 2015 00:00:01 GMT<div>M. Asai</div><div>M.J. McAleer</div>
__Abstract__
The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage
effects. We modify the jump-robust two time scale covariance estimator of Boudt and Zhang (2013)
such that the estimated matrix is positive definite. Using this approach we can disentangle the
estimates of the integrated co-volatility matrix and jump variations from the quadratic covariation
matrix. Empirical results for three stocks traded on the New York Stock Exchange indicate that
the co-jumps of two assets have a significant impact on future co-volatility, but that the impact
is negligible for forecasting weekly and monthly horizons.Frontiers in Time Series and Financial Econometrics
http://repub.eur.nl/pub/78069/
Sun, 01 Feb 2015 00:00:01 GMT<div>S. Ling</div><div>M.J. McAleer</div><div>H. Tong</div>
__Abstract__
Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contributed significantly to time series and financial econometrics, including forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance, prediction of Lévy-driven CARMA processes, functional index coefficient models with variable selection, LASSO estimation of threshold autoregressive models, high dimensional stochastic regression with latent factors, endogeneity and nonlinearity, sign-based portmanteau test for ARCH-type models with heavy-tailed innovations, toward optimal model averaging in regression models with time series errors, high dimensional dynamic stochastic copula models, a misspecification test for multiplicative error models of non-negative time series processes, sample quantile analysis for long-memory stochastic volatility models, testing for independence between functional time series, statistical inference for panel dynamic simultaneous equations models, specification tests of calibrated option pricing models, asymptotic inference in multiple-threshold double autoregressive models, a new hyperbolic GARCH model, intraday value-at-risk: an asymmetric autoregressive conditional duration approach, refinements in maximum likelihood inference on spatial autocorrelation in panel data, statistical inference of conditional quantiles in nonlinear time series models, quasi-likelihood estimation of a threshold diffusion process, threshold models in time series analysis - some reflections, and generalized ARMA models with martingale difference errors.On the Invertibility of EGARCH(p,q)
http://repub.eur.nl/pub/78126/
Sun, 01 Feb 2015 00:00:01 GMT<div>G.G. Martinet</div><div>M.J. McAleer</div>
__Abstract__
Of the two most widely estimated univariate asymmetric conditional volatility models,
the exponential GARCH (or EGARCH) specification can capture asymmetry, which
refers to the different effects on conditional volatility of positive and negative effects of
equal magnitude, and leverage, which refers to the negative correlation between the
returns shocks and subsequent shocks to volatility. However, the statistical properties of
the (quasi-) maximum likelihood estimator (QMLE) of the EGARCH parameters are not
available under general conditions, but only for special cases under highly restrictive and
unverifiable conditions, such as EGARCH(1,0) or EGARCH(1,1), and possibly only
under simulation. A limitation in the development of asymptotic properties of the QMLE
for the EGARCH(p,q) model is the lack of an invertibility condition for the returns
shocks underlying the model. It is shown in this paper that the EGARCH(p,q) model can
be derived from a stochastic process, for which the invertibility conditions can be stated
simply and explicitly. This will be useful in re-interpreting the existing properties of the
QMLE of the EGARCH(p,q) parameters.