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 RepositoryAre the S&P 500 Index and Crude Oil, Natural Gas and Ethanol Futures Related for Intra-Day Data?
http://repub.eur.nl/pub/79731/
Mon, 01 Feb 2016 00:00:01 GMT<div>M. Caporin</div><div>C-L. Chang</div><div>M.J. McAleer</div>
The energy sector is one of the most important in the world, so that time series fluctuations in leading energy sources have been analysed widely. As the leading energy commodities are traded on international stock exchanges, the analysis of the fluctuations in stock and financial derivatives prices and returns have also been investigated extensively in recent years. Much of the empirical analysis has concentrated on using daily, weekly or monthly data, with little research based on intra-day data. The paper analyses the relationships among the S&P 500 Index and futures prices, returns and volatility of three leading energy commodities, namely crude oil, natural gas and ethanol, using intra- day data. The detailed analysis of intra-day temporal aggregation in examining returns relationships and volatility spillovers across the equity and energy futures markets, and the effects of overnight returns, volume, realized volatility, asymmetry, and spillovers across the four financial markets, leads to interesting and useful results for decision making and hedging strategies. The empirical results relating to alternative models of mean and variance feedback and asymmetry for intra-daily returns, asymmetry and volatility spillovers, and dynamic conditional correlations and covariances, show that the relationships between the stock market and alternative energy financial derivatives, specifically futures prices and returns, can and do vary according to the trading range, whether daily or overnight effects are considered, and the temporal aggregation and time frequencies that are used.A Bayesian Approach to Excess Volatility, Short-term Underreaction and Long-term Overreaction during Financial Crises
http://repub.eur.nl/pub/79730/
Fri, 01 Jan 2016 00:00:01 GMT<div>X. Guo</div><div>M.J. McAleer</div><div>W-K. Wong</div><div>L. Zhu</div>
In this paper, we introduce a new Bayesian approach to explain some market anomalies during financial crises and subsequent recovery. We assume that the earnings shock of an asset follows a random walk model with and without drift to incorporate the impact of financial crises. We further assume the earning shock follows an exponential family distribution to take care of symmetric as well as asymmetric information. By using this model setting, we develop some properties on the expected earnings shock and its volatility, and establish prop- erties of investor behavior on the stock price and its volatility during financial crises and subsequent recovery. Thereafter, we develop properties to explain excess volatility, short-term underreaction, long-term overreaction, and their magnitude effects during financial crises and subsequent recovery.Choosing Expected Shortfall over VaR in Basel III Using Stochastic Dominance
http://repub.eur.nl/pub/79539/
Tue, 01 Dec 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>
Bank risk managers follow the Basel Committee on Banking Supervision (BCBS) recommendations that recently proposed shifting the quantitative risk metrics system from Value-at-Risk (VaR) to Expected Shortfall (ES). The Basel Committee on Banking Supervision (2013, p. 3) noted that: “a number of weaknesses have been identified with using VaR for determining regulatory capital requirements, including its inability to capture tail risk”. The proposed reform costs and impact on bank balances may be substantial, such that the size and distribution of daily capital charges under the new rules could be affected significantly. Regulators and bank risk managers agree that all else being equal, a “better” distribution of daily capital charges is to be preferred. The distribution of daily capital charges depends generally on two sets of factors: (1) the risk function that is adopted (ES versus VaR); and (2) their estimated counterparts. The latter is dependent on what models are used by bank risk managers to provide for forecasts of daily capital charges. That is to say, while ES is known to be a preferable “risk function” based on its fundamental properties and greater accounting for the tails of alternative distributions, that same sensitivity to tails can lead to greater daily capital charges, which is the relevant (that is, controlling) practical reference for risk management decisions and observations. In view of the generally agreed focus in this field on the tails of non-standard distributions and low probability outcomes, an assessment of relative merits of estimated ES and estimated VaR is ideally not limited to mean variance considerations. For this reason, robust comparisons between ES and VaR will be achieved in the paper by using a Stochastic Dominance (SD) approach to rank ES and VaR.Down-side Risk Metrics as Portfolio Diversification Strategies across the GFC
http://repub.eur.nl/pub/79216/
Sun, 01 Nov 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 features an analysis of the effectiveness of a range of portfolio diversification strategies, with a focus on down-side risk metrics, as a portfolio diversification strategy in a European market context. We apply these measures to a set of daily arithmetically compounded returns on a set of ten market indices representing the major European markets for a nine year period from the beginning of 2005 to the end of 2013. The sample period, which incorporates the periods of both the Global Financial Crisis (GFC) and subsequent European Debt Crisis (EDC), is challenging one for the application of portfolio investment strategies. The analysis is undertaken via the examination of multiple investment strategies and a variety of hold-out periods and back-tests. We commence by using four two year estimation periods and subsequent one year investment hold out period, to analyse a naive 1/N diversification strategy, and to contrast its effectiveness with Markowitz mean variance analysis with positive weights. Markowitz optimisation is then compared with various down- side investment opimisation strategies. We begin by comparing Markowitz with CVaR, and then proceed to evaluate the relative e effctiveness of Markowitz with various draw-down strategies, utilising a series of backtests. Our results suggest that none of the more sophisticated optimisation strategies appear to dominate naive diversification.Nonlinear time series and neural-network models of exchange rates between the US dollar and major currencies
http://repub.eur.nl/pub/79217/
Sun, 01 Nov 2015 00:00:01 GMT<div>D.E. Allen</div><div>M.J. McAleer</div><div>S. Peiris</div><div>A.K. Singh</div>
This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non- linear models, including smooth transition regression models, logistic smooth transition regressions models, threshold autoregressive models, nonlinear autoregressive models, and additive nonlinear autoregressive models, plus Neural Network models. The results suggest that there is no dominating class of time series models, and the different currency pairs relationships with the US dollar are captured best by neural net regression models, over the ten year sample of daily exchange rate returns data, from August 2005 to August 2015.Informatics, Data Mining, Econometrics and Financial Economics: A Connection
http://repub.eur.nl/pub/79219/
Sun, 01 Nov 2015 00:00:01 GMT<div>C-L. Chang</div><div>M.J. McAleer</div><div>W-K. Wong</div>
This short communication reviews some of the literature in econometrics and financial economics that is related to informatics and data mining. We then discuss some of the research on econometrics and financial economics that could be extended to informatics and data mining beyond the existing areas in econometrics and financial economics.The Fundamental Equation in Tourism Finance
http://repub.eur.nl/pub/79221/
Sun, 01 Nov 2015 00:00:01 GMT<div>M.J. McAleer</div>
The purpose of the paper is to present the fundamental equation in tourism finance that connects tourism research to empirical finance and financial econometrics. The energy industry, which includes, oil, gas and bio-energy fuels, together with the tourism industry, are two of the most important industries in the world today in terms of employment and generating income. The primary purpose in attracting domestic and international tourists to a country, region or city is to maximize tourism expenditure. The paper will concentrate on daily tourism expenditure, regardless of whether such data might be readily available. If such data are not available, a practical method is presented to calculate the appropriate data.Market Integration Dynamics and Asymptotic Price Convergence in Distribution
http://repub.eur.nl/pub/79213/
Thu, 01 Oct 2015 00:00:01 GMT<div>A. García-Hiernaux</div><div>D.E. Guerrero</div><div>M.J. McAleer</div>
This paper analyzes the market integration process of nominal prices, develops a model to analyze market integration, and presents a test of increasing market integration. A distinction is made between the economic concepts of price conver- gence in mean and variance. When both types of convergence occur, prices are said to converge in distribution. We present concepts and definitions related to the market integration process, link these to price convergence in distribution, argue that the Law of One Price is not a sufficient condition for market integration, and present a test of price convergence in distribution. We apply our methodology to two different cases, namely the integration of: i) the inland grains market in 19th Century USA, and ii) the Eurozone long-term bonds market after the euro entered circulation.From Disorder to Order
http://repub.eur.nl/pub/79214/
Thu, 01 Oct 2015 00:00:01 GMT<div>X-G. Yue</div><div>Y. Cao</div><div>M.J. McAleer</div>
In the physical sciences, order and disorder refer to the presence or absence of some symmetry or correlation in a many-particle system. It follows that it is important to examine whether there is any regularity hidden in the phase transition of the disorder- order relationship. In this paper a series of experiments are devised and executed to reveal the power law relationship between order and disorder, and to determine that the power law is indeed an important regular pattern in the phase transition from disorder to order.Research 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/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.