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Publications (10 of 15) Show all publications
Javed, F., Thomas, I. & Memedi, M. (2018). A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease. In: : . Paper presented at 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18), Honolulu, Hawaii, USA, July 17-21, 2018. IEEE
Open this publication in new window or tab >>A comparison of feature selection methods when using motion sensors data: a case study in Parkinson’s disease
2018 (English)Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Feature extraction, Support vector machines, Principal component analysis, Correlation, Machine learning, Predictive models, Sensors
National Category
Computer and Information Sciences
Research subject
Informatics; Statistics
Identifiers
urn:nbn:se:oru:diva-69954 (URN)10.1109/EMBC.2018.8513683 (DOI)
Conference
40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'18), Honolulu, Hawaii, USA, July 17-21, 2018
Funder
Knowledge Foundation
Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-10-31Bibliographically approved
Awartani, B., Javed, F., Maghyereh, A. & Virk, N. (2018). Time-varying transmission between oil and equities in the MENA region: New evidence from DCC-MIDAS analyses. Review of Development Finance, 8(2), 116-126
Open this publication in new window or tab >>Time-varying transmission between oil and equities in the MENA region: New evidence from DCC-MIDAS analyses
2018 (English)In: Review of Development Finance, ISSN 1879-9337, E-ISSN 1879-9337, Vol. 8, no 2, p. 116-126Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
GARCH-MIDAS, DCC-MIDAS, Risk transfer, Oil, MENA Equities
National Category
Business Administration
Identifiers
urn:nbn:se:oru:diva-71148 (URN)10.1016/j.rdf.2018.11.001 (DOI)000453435400005 ()2-s2.0-85056704769 (Scopus ID)
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved
Javed, F., Mazur, S. & Ngailo, E. (2017). Higher order moments of the estimated tangency portfolio weights. Örebro, Sweden: Örebro University School of Business
Open this publication in new window or tab >>Higher order moments of the estimated tangency portfolio weights
2017 (English)Report (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.

Place, publisher, year, edition, pages
Örebro, Sweden: Örebro University School of Business, 2017. p. 18
Series
Working Papers, School of Business, ISSN 1403-0586 ; 10
Keywords
Higher moments, tangency portfolio, portfolio weights, Skewness, Kurtosis
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:oru:diva-57933 (URN)
Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2018-10-19Bibliographically approved
Javed, F. & Podgórski, K. (2017). Tail Behavior and Dependence Structure in the APARCH Model. Journal of Time Series Econometrics, 9(2), Article ID UNSP 20160002.
Open this publication in new window or tab >>Tail Behavior and Dependence Structure in the APARCH Model
2017 (English)In: Journal of Time Series Econometrics, ISSN 1941-1928, E-ISSN 1941-1928, Vol. 9, no 2, article id UNSP 20160002Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Berlin, Germany: Walter de Gruyter, 2017
Keywords
Time series models, heavy tails, leverage effect, estimation
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:oru:diva-55189 (URN)10.1515/jtse-2016-0002 (DOI)000414952500003 ()2-s2.0-85026844668 (Scopus ID)
Funder
Riksbankens Jubileumsfond, P13-1024:1Swedish Research Council, 2013–5180
Available from: 2017-02-01 Created: 2017-02-01 Last updated: 2018-09-07Bibliographically approved
Javed, F. & Mantalos, P. (2015). Sensitivity of the causality in variance tests to GARCH(1,1) processes. Chilean Journal of Statistics, 6(1), 49-65
Open this publication in new window or tab >>Sensitivity of the causality in variance tests to GARCH(1,1) processes
2015 (English)In: Chilean Journal of Statistics, ISSN 0718-7912, E-ISSN 0718-7920, Vol. 6, no 1, p. 49-65Article in journal (Refereed) Published
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.

Keywords
Causality, GARCH, Spillover, Volatility
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:oru:diva-54702 (URN)000360110300004 ()
Available from: 2017-01-13 Created: 2017-01-13 Last updated: 2017-11-08Bibliographically approved
Javed, F. & Podgórski, K. (2014). Leverage Effect for Volatility with Generalized Laplace Error. Economic Quality Control, 29(2), 157-166
Open this publication in new window or tab >>Leverage Effect for Volatility with Generalized Laplace Error
2014 (English)In: Economic Quality Control, ISSN 0940-5151, Vol. 29, no 2, p. 157-166Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Walter de Gruyter, 2014
Keywords
Heavy Tails, Volatility Clustering, Generalized Asymmetric Laplace Distribution, Leverage Effect, Conditional Heteroskedasticity, Asymmetric Power Volatility, GARCH Models
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:oru:diva-57932 (URN)10.1515/eqc-2014-0015 (DOI)
Funder
Riksbankens Jubileumsfond, P13-1024:1Swedish Research Council, 2013-5180
Note

Published 2017-08-29 according to De Gruyter's policy for parallell publishing:

https://www.degruyter.com/dg/page/open-access-policy

Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-10-18Bibliographically approved
Bohl, M. T., Javed, F. & Stephan, P. M. (2013). Do Commodity Index Traders Destabilize Agricultural Futures Prices?. Applied Economics Quarterly, 59(2), 125-148
Open this publication in new window or tab >>Do Commodity Index Traders Destabilize Agricultural Futures Prices?
2013 (English)In: Applied Economics Quarterly, ISSN 1611-6607, Vol. 59, no 2, p. 125-148Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Duncker & Humblot, 2013
National Category
Probability Theory and Statistics Economics
Research subject
Business Studies; Statistics
Identifiers
urn:nbn:se:oru:diva-57940 (URN)10.3790/aeq.59.2.125 (DOI)
Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-10-18Bibliographically approved
Javed, F. (2013). Effect of jumps on causation patterns: an international investigation. International Journal of Computational Economics and Econometrics, 3(3/4), 187-204
Open this publication in new window or tab >>Effect of jumps on causation patterns: an international investigation
2013 (English)In: 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) Published
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.

Place, publisher, year, edition, pages
InderScience Publishers, 2013
Keywords
causality, GARCH model, jumps, volatility spillover, jump spillover, causation patterns, financial crisis, information transmission
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
urn:nbn:se:oru:diva-57941 (URN)10.1504/IJCEE.2013.058497 (DOI)
Available from: 2017-06-07 Created: 2017-06-07 Last updated: 2017-10-18Bibliographically approved
Javed, F. & Mantalos, P. (2013). GARCH-Type Models and Performance of Information Criteria. Communications in statistics. Simulation and computation, 42(8), 1917-1933
Open this publication in new window or tab >>GARCH-Type Models and Performance of Information Criteria
2013 (English)In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 42, no 8, p. 1917-1933Article in journal (Refereed) Published
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.

Keywords
GARCH, Leverage, Spillover, Volatility, Primary 62J02, Secondary 65C05, 65C60
National Category
Economics and Business
Research subject
Economics
Identifiers
urn:nbn:se:oru:diva-34330 (URN)000314352500015 ()
Note

DOI 10.1080/03610918.2012.683924

Available from: 2014-03-14 Created: 2014-03-14 Last updated: 2018-05-22Bibliographically approved
Asgharian, H., Hou, A. J. & Javed, F. (2013). The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach. Journal of Forecasting, 32(7), 600-612
Open this publication in new window or tab >>The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach
2013 (English)In: Journal of Forecasting, ISSN 0277-6693, E-ISSN 1099-131X, Vol. 32, no 7, p. 600-612Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2013
Keywords
Mixed data sampling, long-term variance component, macroeconomic variables, principal component, variance prediction
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:oru:diva-62085 (URN)10.1002/for.2256 (DOI)000326065800003 ()2-s2.0-84887062530 (Scopus ID)
Available from: 2017-11-01 Created: 2017-11-01 Last updated: 2018-05-29Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1488-4703

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