| 1. |
- Ahmed, Shahbaz, et al.
(författare)
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Accurate First-Principles Evaluation of Structural, Electronic, Optical and Photocatalytic Properties of BaHfO3 and SrHfO3 Perovskites
- 2022
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Ingår i: Journal of Alloys and Compounds. - : Elsevier. - 0925-8388 .- 1873-4669. ; 892
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Tidskriftsartikel (refereegranskat)abstract
- A reliable first-principles account of experimentally observed physical properties of perovskite oxides is crucial for realizing their employment in electronic and optical devices. In this context, SCAN meta-GGA functional of DFT offers good approximation for the exchange-correlation energy; facilitating accurate determination of structural and energetic properties. However, SCAN is unable to reproduce electronic and optical properties of wide bad gap materials. In the present study, we report systematic DFT calculations to show that structural, energetic, electronic and optical properties of hafnium based BaHfO3 and SrHfO3 perovskite oxides can be accurately determined through a combine application of SCAN and Tran-Blaha modified Becke-Johnson (TB-mBJ) meta-GGAs. The structural and energetic properties computed using SCAN functional for both BaHfO3 and SrHfO3 are found to be in good agreement with experimental data; achieving a level of accuracy comparable to computationally expansive hybrid DFT calculations. On the other hand, TB-mBJ calculated band gaps computed using the SCAN optimized lattice parameters provide better agreement with experimental data at a low computational cost. The optical properties, band edge potentials and effective masses of the charge carriers in BaHfO3 and SrHfO3 are also computed to examine the combined application of SCAN and TB-mBJ meta-GGAs in predicting the photocatalytic performance of these wide band gap materials. Our results clearly show that the combination of the two meta-GGAs provide a computationally economical route for evaluating the photocatalytic performance of alkaline-earth metal hafnates.
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| 2. |
- Alfelt, Gustav, 1985-, et al.
(författare)
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Singular Conditional Autoregressive Wishart Model for Realized Covariance Matrices
- 2022
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Ingår i: Journal of business & economic statistics. - : Taylor & Francis Group. - 0735-0015 .- 1537-2707. ; 41:3, s. 833-845
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Tidskriftsartikel (refereegranskat)abstract
- Realized covariance matrices are often constructed under the assumption that richness of intra-day return data is greater than the portfolio size, resulting in nonsingular matrix measures. However, when for example the portfolio size is large, assets suffer from illiquidity issues, or market microstructure noise deters sampling on very high frequencies, this relation is not guaranteed. Under these common conditions, realized covariance matrices may obtain as singular by construction. Motivated by this situation, we introduce the Singular Conditional Autoregressive Wishart (SCAW) model to capture the temporal dynamics of time series of singular realized covariance matrices, extending the rich literature on econometric Wishart time series models to the singular case. This model is furthermore developed by covariance targeting adapted to matrices and a sector wise BEKK-specification, allowing excellent scalability to large and extremely large portfolio sizes. Finally, the model is estimated to a 20-year long time series containing 50 stocks and to a 10-year long time series containing 300 stocks, and evaluated using out-of-sample forecast accuracy. It outperforms the benchmark models with high statistical significance and the parsimonious specifications perform better than the baseline SCAW model, while using considerably less parameters.
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| 3. |
- Asgharian, Hossein, et al.
(författare)
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Importance of macroeconomic variables for variance prediction: a GARCH-MIDAS approach
- 2013
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Ingår i: Journal of Forecasting. - 1099-131X. ; 32:7, s. 600-612
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
- 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 various 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 a good proxy of the business cycle.
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| 4. |
- Asgharian, Hossein, et al.
(författare)
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The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH-MIDAS Approach
- 2013
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Ingår i: Journal of Forecasting. - : Wiley. - 1099-131X .- 0277-6693. ; 32:7, s. 600-612
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Tidskriftsartikel (refereegranskat)abstract
- 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. Copyright (c) 2013 John Wiley & Sons, Ltd.
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| 5. |
- Awartani, Basel, et al.
(författare)
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Time-varying transmission between oil and equities in the MENA region : New evidence from DCC-MIDAS analyses
- 2018
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Ingår i: Review of Development Finance. - : Elsevier. - 1879-9337. ; 8:2, s. 116-126
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
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| 7. |
- Billah, Mohammad Ehtasham, et al.
(författare)
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Bayesian Convolutional Neural Network-based Models for Diagnosis of Blood Cancer
- 2022
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Ingår i: Applied Artificial Intelligence. - : Taylor & Francis. - 0883-9514 .- 1087-6545. ; 36:1
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Tidskriftsartikel (refereegranskat)abstract
- Deep learning methods allow computational models involving multiple processing layers to discover intricate structures in data sets. Classifying an image is one such problem where these methods are found to be very useful. Although different approaches have been proposed in the literature, this paper illustrates a successful implementation of the Bayesian Convolution Neural Networks (BCNN)-based classification procedure to classify microscopic images of blood samples (lymphocyte cells) without involving manual feature extractions. The data set contains 260 microscopic images of cancerous and noncancerous lymphocyte cells. We experiment with different network structures and obtain the model that returns the lowest error rate in classifying the images. Our developed models not only produce high accuracy in classifying cancerous and noncancerous lymphocyte cells but also provide useful information regarding uncertainty in predictions.
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| 8. |
- Bohl, Martin T., et al.
(författare)
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Do Commodity Index Traders Destabilize Agricultural Futures Prices?
- 2013
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Ingår i: Applied Economics Quarterly. - : Duncker & Humblot. - 1611-6607 .- 1865-5122. ; 59:2, s. 125-148
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
- Duras, Toni, et al.
(författare)
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Using machine learning to select variables in data envelopment analysis : Simulations and application using electricity distribution data
- 2023
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Ingår i: Energy Economics. - : Elsevier. - 0140-9883 .- 1873-6181. ; 120
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Tidskriftsartikel (refereegranskat)abstract
- Agencies that regulate electricity providers often apply nonparametric data envelopment analysis (DEA) to assess the relative efficiency of each firm. The reliability and validity of DEA are contingent upon selecting relevant input variables. In the era of big (wide) data, the assumptions of traditional variable selection techniques are often violated due to challenges related to high-dimensional data and their standard empirical properties. Currently, regulators have access to a large number of potential input variables. Therefore, our aim is to introduce new machine learning methods for regulators of the energy market. We also propose a new two-step analytical approach where, in the first step, the machine learning-based adaptive least absolute shrinkage and selection operator (ALASSO) is used to select variables and, in the second step, selected variables are used in a DEA model. In contrast to previous research, we find, by using a more realistic data-generating process common for production functions (i.e., Cobb–Douglas and Translog), that the performance of different machine learning techniques differs substantially in different empirically relevant situations. Simulations also reveal that the ALASSO is superior to other machine learning and regression-based methods when the collinearity is low or moderate. However, in situations of multicollinearity, the LASSO approach exhibits the best performance. We also use real data from the Swedish electricity distribution market to illustrate the empirical relevance of selecting the most appropriate variable selection method.
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| 10. |
- Javed, Farrukh, 1984-, et al.
(författare)
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A comparison of feature selection methods when using motion sensors data : a case study in Parkinson’s disease
- 2018
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Ingår i: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). - : IEEE. - 9781538636466 ; , s. 5426-5429
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Konferensbidrag (refereegranskat)abstract
- 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.
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