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  • 1.
    Asgharian, Hossein
    et al.
    Department of Economics, Knut Wicksell Center for Financial Studies, Lund University, Sweden.
    Hou, Ai Jun
    Department of Business and Economics, Southern Denmark University, Odense, Denmark.
    Javed, Farrukh
    Department of Statistics, Lund University, Sweden.
    The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach2013In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 32, no 7, p. 600-612Article in journal (Refereed)
    Abstract [en]

    This paper applies the GARCH-MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low-frequency macroeconomic information in the GARCH-MIDAS model improves the prediction ability of the model, particularly for the long-term variance component. Moreover, the GARCH-MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle.

  • 2.
    Awartani, Basel
    et al.
    Westminster Business School, Westminster University, London, United Kingdom.
    Javed, Farrukh
    Örebro University, Örebro University School of Business.
    Maghyereh, Aktham
    Department of Economics and Finance, United Arab Emirates University, United Arab Emirates.
    Virk, Nader
    Plymouth Business School, Plymouth University, Plymouth, United Kingdom.
    Time-varying transmission between oil and equities in the MENA region: New evidence from DCC-MIDAS analyses2018In: Review of Development Finance, ISSN 1879-9337, E-ISSN 1879-9337, Vol. 8, no 2, p. 116-126Article in journal (Refereed)
    Abstract [en]

    In this paper we use the DCC-MIDAS (Dynamic Conditional Correlation-Mixed Data Sampling) model to infer the association between oil and equities in five MENA countries between February 2006 and April 2017. The model indicates that higher oil returns tends to reduce the long-term risk of the Saudi market, but to increase it in other markets. The risk transfer from oil to MENA equities is found to be weak. The dynamic conditional correlation between oil and equities is not always positive and it unexpectedly changes sign during the sample period. However, the association always strengthens when there is a large draw down in oil prices as well as during periods of high volatility. Finally, we find that short term association occasionally breaks from the longer-term correlation particularly in Egypt and Turkey. These patterns of influence and associations are unique, and have important implications for equity portfolio managers who are interested in investing in energy and MENA equities.

  • 3.
    Bohl, Martin T.
    et al.
    Department of Economics, Westphalian Wilhelminian University of Münster, Münster, Germany.
    Javed, Farrukh
    Department of Statistics, Lund University, Lund, Sweden.
    Stephan, Patrick M.
    Department of Economics, Westphalian Wilhelminian University of Münster, Münster, Germany.
    Do Commodity Index Traders Destabilize Agricultural Futures Prices?2013In: Applied Economics Quarterly, ISSN 1611-6607, Vol. 59, no 2, p. 125-148Article in journal (Refereed)
    Abstract [en]

    Motivated by repeated price spikes and crashes over the last decade, we investigate whether the intensive investment activities of commodity index traders (CITs) have destabilized agricultural futures markets. Using a stochastic volatility model, we treat conditional volatility as an unobserved component, and analyze whether it has been affected by the expected and unexpected open interest of CITs. However, with respect to twelve increasingly financialized grain, livestock, and soft commodities, we do not find robust evidence that this is the case. We thus conclude that justifying a tighter regulation of CITs by blaming them for more volatile agricultural futures markets appears to be unwarranted.

  • 4.
    Javed, Farrukh
    Department of Statistics, Lund University, Lund, Sweden.
    Effect of jumps on causation patterns: an international investigation2013In: International Journal of Computational Economics and Econometrics, ISSN 1757-1170, E-ISSN 1757-1189, Vol. 3, no 3/4, p. 187-204Article in journal (Refereed)
    Abstract [en]

    In this paper, we empirically investigate and discuss the effects of jumps in data on causation pattern both in mean and variance. Our data consist of daily stock returns of four countries: France, Sweden, the UK and Finland. A test proposed by Cheung and Ng (1996) and Hong (2001) is applied for testing volatility spillover. We find significant evidence of jump spillover. It is shown that the presence of jump affects the transmission of information between two sets of series. Moreover, it is found that the choice of an appropriate model is essential for understanding the real pattern of transmission.

  • 5.
    Javed, Farrukh
    Örebro University, Örebro University School of Business.
    Stochastic volatility modelsManuscript (preprint) (Other academic)
  • 6.
    Javed, Farrukh
    et al.
    Örebro University, Örebro University School of Business.
    Loperfido, Nicola
    Dipartimento di Economia, Societ`a e Politica, Universit`a degli Studi di Urbino ”Carlo Bo”, Urbino (PU), Italy.
    Mazur, Stepan
    Örebro University, Örebro University School of Business.
    Fourth Cumulant of Multivariate Aggregate Claim ModelsManuscript (preprint) (Other academic)
    Abstract [en]

    The fourth cumulant for the aggregated multivariate claims is considered. A formula is presented for the general case when the aggregating variable is independent of the multivariate claims. Two important special cases are considered. In the first one, multivariate skewed normal claims are considered and aggregated by a Poisson variable. The second case is dealing with multivariate asymmetric generalized Laplace and aggregation is made by a negative binomial variable. Due to the invariance property the latter case can be derived directly, leading to the identity involving the cumulant of the claims and the aggregated claims. There is a well established relation between asymmetric Laplace motion and negative binomial process that corresponds to the invariance principle of the aggregating claims for the generalized asymmetric Laplace distribution. We explore this relation and provide multivariate continuous time version of the results. It is discussed how these results that deals only with dependence in the claim sizes can be used to obtain a formula for the fourth cumulant for more complex aggregate models of multivariate claims in which the dependence is also in the aggregating variables.

  • 7.
    Javed, Farrukh
    et al.
    Dept of Statistics, Lund University, Lund, Sweden.
    Mantalos, Panagiotis
    Örebro University, Örebro University School of Business.
    GARCH-Type Models and Performance of Information Criteria2013In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 8, p. 1917-1933Article in journal (Refereed)
    Abstract [en]

    This article discusses the ability of information criteria toward the correct selection of different especially higher-order generalized autoregressive conditional heteroscedasticity (GARCH) processes, based on their probability of correct selection as a measure of performance. Each of the considered GARCH processes is further simulated at different parameter combinations to study the possible effect of different volatility structures on these information criteria. We notice an impact from the volatility structure of time series on the performance of these criteria. Moreover, the influence of sample size, having an impact on the performance of these criteria toward correct selection, is observed.

  • 8.
    Javed, Farrukh
    et al.
    Lund University, Lund, Sweden..
    Mantalos, Panagiotis
    Örebro University, Örebro University School of Business.
    Sensitivity of the causality in variance tests to GARCH(1,1) processes2015In: Chilean Journal of Statistics, ISSN 0718-7912, E-ISSN 0718-7920, Vol. 6, no 1, p. 49-65Article in journal (Refereed)
    Abstract [en]

    This paper studies the impact of a number of volatile data sets on volatility spillover tests. We investigate a type of data generating process, AR(1)-GARCH(1,1), with an extensive set of Monte Carlo simulations. It is found that causation pattern, due to causality between two series, is influenced by the intensity of volatility clustering. Two testing procedures are applied for testing causality in the variance. We notice a severe size and power distortion when the clustering parameter is high and when the process is near integration. Furthermore, whenever there is a severe size distortion, there is a serial autocorrelation in the standardized residuals. This is seen when the asymptotic distribution of the statistics is used to define a critical region. So, instead of relying on the asymptotic distribution, we calculate the percentiles of the test statistic with the null hypothesis of no spillover effect and use them as a critical region for both size and power. We observe a significant improvement in the results.

  • 9.
    Javed, Farrukh
    et al.
    Örebro University, Örebro University School of Business.
    Mazur, Stepan
    Örebro University, Örebro University School of Business.
    Ngailo, Edward
    Department of Mathematics, Stockholm University, Stockholm, Sweden; Department of Mathematics, University College of Education, Dar es Salaam, Tanzania.
    Higher order moments of the estimated tangency portfolio weights2017Report (Other academic)
    Abstract [en]

    In this paper we consider the estimated tangency portfolio weights. We derive analytical expressions for the higher central and non-central moments of these weights. The main focus has been given to skewness and kurtosis due to the importance of asymmetry and heavy tails of the data. We complement our results with an empirical study where we analyze an international diversified portfolio.

  • 10.
    Javed, Farrukh
    et al.
    School of Economics and Management, Lund University, Lund, Sweden.
    Podgórski, Krzysztof
    School of Economics and Management, Lund University, Lund, Sweden.
    Leverage Effect for Volatility with Generalized Laplace Error2014In: Economic Quality Control, ISSN 0940-5151, Vol. 29, no 2, p. 157-166Article in journal (Refereed)
    Abstract [en]

    We propose a new model that accounts for the asymmetric response of volatility to positive (`good news') and negative (`bad news') shocks in economic time series – the so-called leverage effect. In the past, asymmetric powers of errors in the conditionally heteroskedastic models have been used to capture this effect. Our model is using the gamma difference representation of the generalized Laplace distributions that efficiently models the asymmetry. It has one additional natural parameter, the shape, that is used instead of power in the asymmetric power models to capture the strength of a long-lasting effect of shocks. Some fundamental properties of the model are provided including the formula for covariances and an explicit form for the conditional distribution of `bad' and `good' news processes given the past – the property that is important for the statistical fitting of the model. Relevant features of volatility models are illustrated using S&P 500 historical data.

  • 11.
    Javed, Farrukh
    et al.
    Örebro University, Örebro University School of Business.
    Podgórski, Krzysztof
    Örebro School of Business, Örebro University, Örebro, Sweden.
    Tail Behavior and Dependence Structure in the APARCH Model2017In: Journal of Time Series Econometrics, ISSN 1941-1928, E-ISSN 1941-1928, Vol. 9, no 2, article id UNSP 20160002Article in journal (Refereed)
    Abstract [en]

    The APARCH model attempts to capture asymmetric responses of volatility to positive and negative ‘news shocks’ – the phenomenon known as the leverage effect. Despite its potential, the model’s properties have not yet been fully investigated. While the capacity to account for the leverage is clear from the defining structure, little is known how the effect is quantified in terms of the model’s parameters. The same applies to the quantification of heavy-tailedness and dependence. To fill this void, we study the model in further detail. We study conditions of its existence in different metrics and obtain explicit characteristics: skewness, kurtosis, correlations and leverage. Utilizing these results, we analyze the roles of the parameters and discuss statistical inference. We also propose an extension of the model. Through theoretical results we demonstrate that the model can produce heavy-tailed data. We illustrate these properties using S&P500 data and country indices for dominant European economies.

  • 12.
    Javed, Farrukh
    et al.
    Örebro University, Örebro University School of Business.
    Podgórski, Krzysztof
    Department of Statistics, Lund University, Lund, Sweden.
    Volatility leverage autoregressive models with non-Gaussian innovationsManuscript (preprint) (Other academic)
    Abstract [en]

    In this article we discuss the non-Gaussian alternatives to model financial volatility. The comparison has been made between two special cases of the GH distributions. We derive the stationarity conditions, moments, dependence structure to account for heavy tails and leverage in the data and discuss several estimation strategies to the proposed non-Gaussian model. Finally through empirical investigation the model efficiency has been evaluated using real data.

  • 13.
    Javed, Farrukh
    et al.
    Örebro University, Örebro University School of Business.
    Thomas, Ilias
    Dalarna University, Falun, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease2018Conference paper (Refereed)
    Abstract [en]

    The objective of this study is to investigate the effects of feature selection methods on the performance of machine learning methods for quantifying motor symptoms of Parkinson’s disease (PD) patients. Different feature selection methods including step-wise regression, Lasso regression and Principal Component Analysis (PCA) were applied on 88 spatiotemporal features that were extracted from motion sensors during hand rotation tests. The selected features were then used in support vector machines (SVM), decision trees (DT), linear regression, and random forests models to calculate a so-called treatment-response index (TRIS). The validity, testretest reliability and sensitivity to treatment were assessed for each combination (feature selection method plus machine learning method). There were improvements in correlation coefficients and root mean squared error (RMSE) for all the machine learning methods, except DTs, when using the selected features from step-wise regression inputs. Using step-wise regression and SVM was found to have better sensitivity to treatment and higher correlation to clinical ratings on the Unified PD Rating Scale as compared to the combination of PCA and SVM. When assessing the ability of the machine learning methods to discriminate between tests performed by PD patients and healthy controls the results were mixed. These results suggest that the choice of feature selection methods is crucial when working with data-driven modelling. Based on our findings the step-wise regression can be considered as the method with the best performance.

  • 14.
    Sabzevari, Hassan
    et al.
    Lund University, Lund, Sweden.
    Javed, Farrukh
    Örebro University, Örebro University School of Business.
    Measuring Exposure of European Banking to the GIIPS Banking SectorManuscript (preprint) (Other academic)
    Abstract [en]

    This paper attempts at evaluating the systemic risk contributions of GIIPS-block (Greece, Ireland, Italy, Portugal, and Spain) banks on the rest of major European countries' banking systems. To quantify systemic risk, a Conditional Value-at-Risk (CoVaR) approach has been employed. In order to empirically calculate the magnitude of risk, CoVaR measure is further evaluated by quantile regression and Dynamic Conditional Correlation (DCC). Our results firstly indicate a significant spillover effect of GIIPS banking on the examined banking systems. Second, larger systemic risk is evident during the recent financial crisis. This period is highly volatile and  uropean banking indices have higher correlations with GIIPS banking index. A robustness analysis is made between the two estimated CoVaR measures and a simple non-conditional VaR measure. Finally, the Guntay-Kupiec test is employed to distinguish between systemic risk and systematic risk. Our findings indicate that the non-parametric method, such as quantile regression, yields larger CoVaR values than the parametric method based on the Gaussian distribution.

  • 15.
    Ulfat, I.
    et al.
    Department of Applied Physics, Chalmers University of Technology, Göteborg, Sweden; Energy and Environment Research Group, Department of Physics, University of Karachi, Karachi, Pakistan.
    Javed, Farrukh
    Energy and Environment Research Group, Department of Physics, University of Karachi, Karachi, Pakistan; Department of Statistics, School of Economics and Management, Lund University, Lund, Sweden.
    Abbasi, F. A.
    Energy & Environment Research Group, Department of Physics, University of Karachi, Karachi, Pakistan; Department of Physics, Government National College, Karachi, Pakistan.
    Kanwal, F.
    Energy & Environment Research Group, Department of Physics, University of Karachi, Karachi, Pakistan .
    Usman, A.
    Pakistan Meteorological Department, Karachi, Pakistan.
    Jahangir, M.
    Energy & Environment Research Group, Department of Physics, University of Karachi, Karachi, Pakistan; Department of Physics, Government National College, Karachi, Pakistan.
    Ahmed, F.
    Energy & Environment Research Group, Department of Physics, University of Karachi, Karachi, Pakistan .
    Estimation of solar energy potential for Islamabad, Pakistan2012In: / [ed] Chafic Salame, Michel Aillerie, Gaby Khoury, Elsevier, 2012, Vol. 18, p. 1496-1500Conference paper (Refereed)
    Abstract [en]

    In order to design a solar energy system with optimized performance a thorough knowledge of solar radiation data for a considerably long period (20-25 years) is a pre-requisite. For developing countries like Pakistan, the need of empirical models to assess the feasibility of solar energy utilization seems inevitable due to the absence and scarcity of trustworthy solar radiation data. We present such models for the capital city of Pakistan, Islamabad to estimate global and diffuse solar radiation. It is found that with the exception of monsoon month, solar energy can be utilized very efficiently throughout the year. The models suggested could be used for most of the north-eastern areas of Pakistan, which are similar to Islamabad with respect to the climate and the availability of solar radiation but lack in the record of solar radiation data.

  • 16.
    Virk, Nader
    et al.
    Plymouth Business School, Plymouth, United Kingdom.
    Javed, Farrukh
    Örebro University, Örebro University School of Business.
    European equity market integration and joint relationship of conditional volatility and correlations2017In: Journal of International Money and Finance, ISSN 0261-5606, E-ISSN 1873-0639, Vol. 71, p. 53-77Article in journal (Refereed)
    Abstract [en]

    We analyse the integration patterns of seven leading European stock markets from 1990 to 2013 using daily data and mismatched monthly macroeconomic data. To study the mismatch of data frequencies we use the DCC-MIDAS (Dynamic Conditional Correlation - Mixed Data Sampling) technique developed by Colacito, Engle and Ghysels (journal of Econometrics, 2011). We benchmark European integration patterns against the German stock market. The reported integration patterns show a clear divide between large and (relatively) small equity markets' short run and long run return correlations: the small markets display higher short run European convergences than the large markets and vice versa. The across-the-board divergence from Greek risk, during the crisis period, is the most unambiguous conclusion of our study. During this period, cross-country joint relationships of conditional variances and return correlations - a 'convergence of risks' resulting in global/regional contagious spillovers - are typically positive. Only exceptions are the German stock market's joint relationships.

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