Farhat Iqbal
@uob.edu.pk
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Maha Shabbir, Sohail Chand, and Farhat Iqbal
Elsevier BV
Q. Mahmood, Farhat Iqbal, Tahani H. Flemban, Eman Algrafy, Hind Althib, M.G.B. Ashiq, Murefah mana AL-Anazy, Hamid Ullah, Amani Rached, Tahani Alqahtani,et al.
Elsevier BV
Maha Shabbir, Sohail Chand, Farhat Iqbal, and Ozgur Kisi
Springer Science and Business Media LLC
Farhat Iqbal, Mamoona Zahid, and Dimitrios Koutmos
MDPI AG
Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies.
Farhat Iqbal, Abdul Raziq, Zil-E-Huma, Cem Tirink, Abdul Fatih, and Muhammad Yaqoob
Springer Science and Business Media LLC
Rehan Kausar, Farhat Iqbal, Abdul Raziq, and Naveed Sheikh
Penerbit Universiti Kebangsaan Malaysia (UKM Press)
The foreign exchange (Forex) market has greatly influenced the global financial market. While Forex trading offers investors substantial yield prospects, some risks are also involved. It is challenging to accurately model financial time series due to their nonlinear, non-stationary and noisy properties with an uncertain and hidden relationship. Thus, developing extremely precise forecasting techniques is crucial for investors and decision-makers. This study introduces a novel hybrid forecasting model, VMD-CEEMDAN-GRU-ATCN, designed to improve Forex price prediction accuracy. To begin with, our proposed model utilizes the variational model decomposition (VMD) technique for breaking down raw prices into multiple sub-components and residual terms. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique is utilized to extract features from the residual terms, which involves further decomposition and analysis of these complex information-containing terms. These sub-components are then predicted by the gated recurrent unit (GRU) model. To enhance the effectiveness of our hybrid model, we include the open, high, low, and close prices and seven Forex market technical indicators. Finally, an attention-based temporal convolutional network (ATCN) model is used to obtain the Forex price forecasts. For both one-step and multi-step ahead forecasting, our proposed VMD-CEEMDAN-GRU-ATCN model has demonstrated superior and consistent performance in predicting USD/PKR exchange rate price series.
Maha Shabbir, Sohail Chand, and Farhat Iqbal
Informa UK Limited
Ridge regression is a variant of linear regression that aims to circumvent the issue of collinearity among predictors. The ridge parameter (Formula presented.) has an important role in the bias-variance tradeoff. In this article, we introduce a new approach to select the ridge parameter to deal with the multicollinearity problem with different behavior of the error term. The proposed ridge estimator is a function of the number of predictors and the standard error of the regression model. An extensive simulation study is conducted to assess the performance of the estimators for the linear regression model with different error terms, which include normally distributed, non-normal and heteroscedastic or autocorrelated errors. Based upon the criterion of mean square error (MSE), it is found that the new proposed estimator outperforms OLS, commonly used and closely related estimators. Further, the application of the proposed estimator is provided on the COVID-19 data of India. © 2023 Taylor & Francis Group, LLC.
Mamoona Zahid, Farhat Iqbal, and Dimitrios Koutmos
MDPI AG
The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.
Mamoona Zahid, Farhat Iqbal, Abdul Raziq Abdul Raziq, and Naveed Sheikh
Penerbit Universiti Kebangsaan Malaysia (UKM Press)
Using the high-frequency data of Bitcoin, this study aims to model the time-varying volatility identified in the residuals of the heterogeneous autoregressive (HAR) model of realized volatility using the symmetric, asymmetric and long-memory generalized autoregressive conditional heteroscedastic models (GARCH) models. We further extended these models by incorporating jumps and continuous components in the realized volatility estimators and investigating the impact of the inverse leverage effect. The Diebold Mariano and model confidence set test confirm that the forecasting performance of HAR-type models can be effectively improved by these innovations. The long memory HAR-GARCH model with jumps and continuous components provided better forecasting accuracy for Bitcoin volatility as compared to other realized volatility models. The findings of this study may benefit individual investors and risk managers who wish to minimize risks and diversify their portfolios to maximize profits in Bitcoin’s investment.
Farhat Iqbal, Abdul Waheed, Zil-e Huma, and Asim Faraz
ResearchersLinks Ltd
Farhat Iqbal1, Abdul Waheed2*, Zil-e-Huma3 and Asim Faraz2 1Department of Statistics, University of Balochistan, Quetta, Pakistan 2Departmentof Livestock and Poultry Production, Faculty of Veterinary Sciences, Bahauddin Zakariya University, Multan, Pakistan 3Department of Zoology, Sardar Bahadur Khan Women’s University, Quetta, Pakistan Article Information Received 03 October 2019 Revised 13 January 2020 Accepted 24 January 2020 Available online 28 January 2021
Maha Shabbir, Sohail Chand, and Farhat Iqbal
Informa UK Limited
Maha Shabbir, Sohail Chand, and Farhat Iqbal
Springer Science and Business Media LLC
Farhat Iqbal, Ecevit Eyduran, Abdul Raziq, Muhammad Ali, Zil-e-Huma, Cem Tirink, and Harun Sevgenler
Springer Science and Business Media LLC
Farhat IQBAL, Abdul RAZIQ, Zil E HUMA, and Muhammad ALI
The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS
Farhat Iqbal and Kostas Triantafyllopoulos
Informa UK Limited
Abstract Bayesian inference is proposed for volatility models, targeting financial returns, which exhibit high kurtosis and slight skewness. Rotated GARCH models are considered which can accommodate the multivariate standard normal, Student t, generalized error distributions and their skewed versions. Inference on the model parameters and prediction of future volatilities and cross-correlations are addressed by Markov chain Monte Carlo inference. Bivariate simulated data is used to assess the performance of the method, while two sets of real data are used for illustration: the first is a trivariate data set of financial stock indices and the second is a higher dimensional data set for which a portfolio allocation is performed.
Mamoona Zahid and Farhat Iqbal
Penerbit Universiti Kebangsaan Malaysia (UKM Press)
This paper compares a number of stochastic volatility (SV) models for modeling and predicting the volatility of the four most capitalized cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin). The standard SV model, models with heavy-tails and moving average innovations, models with jumps, leverage effects and volatility in mean were considered. The Bayes factor for model fit was largely in favor of the heavy-tailed SV model. The forecasting performance of this model was also found superior than the other competing models. Overall, the findings of this study suggest using the heavy-tailed stochastic volatility model for modeling and forecasting the volatility of cryptocurrencies.
T. Sengul, S. Celik, E. Eyduran, and F. Iqbal
Verlag Eugen Ulmer
Akhtar Bibi
Bolan Society for Pure and Applied Biology (BSPAB)
The present study was conducted with the aimed to determine the frequency of anemia and lack of hemoglobin in human pregnancy at Bolan Medical College Hospital Quetta during 2017. As many as 625 pregnant women patients were examined, of these 350 were found anemic. Blood samples of randomly selected three hundred and fifty pregnant women of age group 17-44 years were analyzed, and was classified under first trimester (21), second trimester (58) and third trimester (271). The data was analyzed to estimate the frequency of anemia during pregnancy ranging from mild: 288 (46.1%) to moderate: 54 (8.6%) and severe: 8 (1.3%). Findings revealed that anemia was found to be prevalent in third trimester (271) as compared to the second (58) and first (21) trimester of pregnancy. Chi-square test was applied at 5% level of significance to check the association between age groups, parity and gestation age (G age). Significant association was found with anemic level at p-value <0.001 in age group and G age, whereas parity was not found associated with anemic level at 5% level of significance. The present study revealed that overall prevalence rate of anemia was 56% which indicated high prevalence of this disease in the region. The study concluded that pregnant women are at high hazard of blood anemia. Fresh balanced diets rich in iron, folic acid and vitamin B12 are recommended for their health and fine growth of developing baby.
Keywords: Anemia; Frequency; Gestation-age; Hemoglobin; Parity; Quetta
http://dx.doi.org/10.19045/bspab.2019.80045
F. Iqbal, E. Eyduran, N. Mikail, V. Sarıyel, Z.E. Huma, A. Aygün, and İ. Keskin
Verlag Eugen Ulmer
Zil E HUMA and Farhat IQBAL
The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS
* Correspondence: farhatiqb@gmail.com
Farhat Iqbal, Mohammad Masood Tariq, Ecevit Eyduran, Zil-e Huma, Abdul Waheed, Farhat Abbas, Muhammad Ali, Nadeem Rashid, Majed Rafeeq, Asadullah Asadullah,et al.
ResearchersLinks Ltd