Faheem Aslam
@ww2.comsats.edu.pk
Tenured Associate Professor, Department of Management Sciences
Comsats university Islamabad
Dr. Faheem Aslam is Tenured Associate Professor in the Department of Management Sciences at CIIT Islamabad. He earned his Master's and Ph.D. Degrees from Hanyang University Business School, Seoul, South Korea, in 2015.
My educational background, combined with my practical experience in the financial industry, has provided me with a comprehensive understanding of finance. I hold a PHD degree in Hanyang University business school, South Korea (AACSB accredited international academic institution) with CGPA of 3.93/4. I have taught a range of finance courses, from introductory to advanced level, and have received consistently positive feedback (above 90%) from my students. In addition to my teaching experience, I have published more than 50 research papers in top-tier finance journals (including ABDC, ABS) with an impact factor of 155. My research is getting increasing attention among international research community with more than 1300 citations. Likewise, the world health organization.
EDUCATION
PHD (Finance)-Hanyang University Seoul, South Korea.
MBA (Finance)- International Islamic University Islamabad (Gold Medal)
RESEARCH, TEACHING, or OTHER INTERESTS
General Economics, Econometrics and Finance, Finance, Business, Management and Accounting, Developmental Neuroscience
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Saima Latif, Nadeem Javaid, Faheem Aslam, Abdulaziz Aldegheishem, Nabil Alrajeh, and Safdar Hussain Bouk
Elsevier BV
Faheem Aslam, Ahmed Imran Hunjra, Bilal Ahmed Memon, and Mingda Zhang
Elsevier BV
Haji Ahmed, Faheem Aslam, and Paulo Ferreira
MDPI AG
Financial stress can have significant implications for individuals, businesses, asset prices and the economy as a whole. This study examines the nonlinear structure and dynamic changes in the multifractal behavior of cross-correlation between the financial stress index (FSI) and four well-known commodity indices, namely Commodity Research Bureau Index (CRBI), Baltic Dry Index (BDI), London Metal Index (LME) and Brent Oil prices (BROIL), using multifractal detrended cross correlation analysis (MFDCCA). For analysis, we utilized daily values of FSI and commodity index prices from 16 June 2016 to 9 July 2023. The following are the most important empirical findings: (I) All of the chosen commodity market indices show cross correlations with the FSI and have notable multifractal characteristics. (II) The presence of power law cross-correlation implies that a noteworthy shift in FSI is likely to coincide with a considerable shift in the commodity indices. (III) The multifractal cross-correlation is highest between FSI and Brent Oil (BROIL) and lowest with LME. (IV) The rolling windows analysis reveals a varying degree of persistency between FSI and commodity markets. The findings of this study have a number of important implications for commodity market investors and policymakers.
Kashif Zaheer, Faheem Aslam, Yasir Tariq Mohmand, and Paulo Ferreira
MDPI AG
Analysis of the relationships among global stock markets is crucial for international investors, regulators, and policymakers, particularly during a crisis. Complex network theory was applied to analyze the relationship between global stock markets during the Russia–Ukraine war. Daily data from 55 stock markets from 6 August 2021 to 23 September 2023 were retrieved and used to investigate the changes in global stock market networks. The sample period was divided into 22 subsamples, using a 100-day rolling window rolled forward a trading month, and then long-range correlations based on distance matrices were calculated. These distance matrices were utilized to construct stock market networks. Moreover, minimum spanning trees (MSTs) were extracted from these financial networks for analytical purposes. Based on topological and structural analysis, we identified important/central nodes, distinct communities, vulnerable/stable nodes, and changes thereof with the escalation of war. The empirical findings reveal that the Russia–Ukraine war impacted the global stock markets’ network. However, its intensity varied with changes in the region and the passage of time due to the level of stock market integration and stage of war escalation, respectively. Stock markets of France, Germany, Canada, and Austria remained the most centrally connected within communities; surprisingly, the USA’s stock market is not on this list.
Bilal Ahmed Memon, Faheem Aslam, Shakhnoza Asadova, and Paulo Ferreira
Elsevier BV
Faheem Aslam, Bilal Ahmed Memon, Ahmed Imran Hunjra, and Elie Bouri
Elsevier BV
Faheem Aslam, Wahbeeah Mohti, Haider Ali, and Paulo Ferreira
Elsevier BV
Faheem Aslam, Paulo Ferreira, and Wahbeeah Mohti
Emerald
PurposeThe investigation of the fractal nature of financial data has been growing in the literature. The purpose is to investigate the multifractal behavior of frontier markets using multifractal detrended fluctuation analysis (MFDFA).Design/methodology/approachThis study used daily closing prices of nine frontier stock markets up to 31-Aug-2020. A preliminary analysis reveals that these markets exhibit fat tails and clustering patterns. For a more robust analysis, a combination of Seasonal and Trend Decomposition using Loess (STL) and MFDFA has been employed. The former method is used to decompose daily stock returns, where later detected the long rang dependence in the series.FindingsThe results confirm varying degree of multifractality in frontier stock markets, implying that they exhibit long-range dependence. Based on these multifractality levels, Serbian and Romanian stock markets are the ones exhibiting least long-range dependence, while Slovenian and Mauritius stock markets indicating highest dependence in their series. Furthermore, the markets of Kenya, Morocco, Romania and Serbia exhibit mean reversion (anti-persistent) behavior while the remaining frontier markets show persistent behaviors.Practical implicationsThe information given by the detection of the fractal measure of data can support for investment and policymaking decisions.Originality/valueFrontier markets are of great potential from the perspective of international diversification. However, most of the research focused on other emerging and developed markets, especially in the context of multifractal analysis. This study combines the STL method and a physics-based robust technique, MFDFA to detect the multifractal behavior of frontier stock markets.
KHURRUM S. MUGHAL, SADDAM ILYAS, YASIR TARIQ MOHMAND, FAHEEM ASLAM, and MUKHTAR UL HASAN
World Scientific Pub Co Pte Ltd
Net importing countries are very susceptible to changes in the value of their currency. Pakistan, being a small open economy, faces a constant pressure in its current account and BOP, which leads to unavoidable stress on its exchange rate. Exchange rate movements affect the cost of imports directly which have been studied extensively in empirical literature. However, these studies ignore the possibility of asymmetric effects of exchange rate and its impact on import demand in Pakistan. An appreciation in exchange rate may have a different impact on demand for imports than depreciations depending upon the level of rigidity in consumer preferences as well as the availability of substitutes for consumer goods, capital goods and raw materials. We use quarterly data of Pakistan’s consumer goods, capital goods and raw material imports from 2005:Q1 to 2018:Q4 and employ a relatively recent econometric methodology, namely, Nonlinear Autoregressive Distributed Lag (NARDL) technique. The results confirm the existence of asymmetric impact of exchange rate in the long-run. The appreciation of currency has more pronounced impact in increasing imports relative to the depreciation of it in decreasing imports. There are further differences of this effect within imports across consumer goods, capital goods and raw materials. We present policy implications of this asymmetric effect of exchange rate on disaggregated consumer imports.
Kashif Zaheer, Faheem Aslam, Yasir Tariq Mohmand, and Paulo Ferreira
Emerald
PurposeCOVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network.Design/methodology/approachDaily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs.FindingsThe findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node; however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed.Originality/valueIt is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere.
Faheem Aslam, Skander Slim, Mohamed Osman, and Ibrahim Tabche
Emerald
PurposeThis paper examines the impact of Russian invasion of Ukraine on the intraday efficiency of four major energy markets, namely, diesel oil, Brent oil, light oil and natural gas.Design/methodology/approachThis study applies the multifractal detrended fluctuation analysis (MFDFA) to high-frequency returns (30-min intervals) for the period from October 21, 2021, to May 20, 2022. The data sample of 5,141 observations is divided into two sub-samples, before and after the invasion of 24th February 2022. Additionally, the magnitude of long memory index is employed to investigate the presence of herding behavior around the invasion period.FindingsResults confirm the presence of multifractality in energy markets and reveal significant changes of multifractal strength due to the invasion, indicating a decline of intraday efficiency for oil markets. Surprisingly, the natural gas market, being the least efficient before the invasion, turns out to be more efficient after the invasion. The findings also suggest that investors in these energy markets are likely to show herding, more prominently after the invasion.Practical implicationsThe multifractal patterns, in particular the long memory property of energy markets, can help investors develop profitable investment strategies. Furthermore, the improved efficiency observed in the natural gas market, after the invasion, highlights its unique traits and underlying complexity.Originality/valueThis study is the first attempt to assess the impact of the Russia–Ukraine war on the efficiency of global commodity markets. This is quite important because the adverse effects of the war on financial markets may potentially cause destabilizing outcomes and negative effects on social welfare on a global scale.
Faheem Aslam, Ijaz Ali, Fahd Amjad, Haider Ali, and Inza Irfan
Springer Science and Business Media LLC
FAHEEM ASLAM, ZIL-E-HUMA, RASHIDA BIBI, and PAULO FERREIRA
World Scientific Pub Co Pte Ltd
We take the novel Twitter-based economic uncertainty (TEU) to examine if it has cross-correlation characteristics with four major cryptocurrencies i.e. Bitcoin, Ethereum, Litecoin, and Ripple. To conduct a more thorough analysis, we apply multifractal detrended cross-correlation analysis (MFDCCA) on seasonal-trend decomposition using Loess (STL) decomposed series as well as without decomposed series on the daily data, ranging from 1 June 2011 to 30 June 2021. The findings of this study indicate that: (i) all pairs of TEU with cryptocurrencies are multifractal and have power-law behavior; (ii) the pairs of Ethereum and Bitcoin with TEU are found to be the most multifractal while Litecoin with TEU has the lowest multifractal characteristics; (iii) all STL decomposed series of cryptocurrency have persistent cross-correlation with TEU with the exception of Ethereum which has anti-persistent cross-correlation with TEU; (iv) all without decomposed series of cryptocurrencies show significant persistent cross-correlation characteristics with TEU; (v) the highest linkage is found for the pair of Bitcoin with TEU. Moreover, to reveal the dynamic characteristics in the cross-correlation of TEU with cryptocurrencies, the rolling window is employed for MFDCCA. These findings have important managerial and academic implications for policymakers, investors, and market participants.
Faheem Aslam, Paulo Ferreira, Haider Ali, Arifa, and Márcia Oliveira
MDPI AG
There is ample evidence that Islamic stock markets perform differently from conventional stock markets, particularly when economic policy uncertainty (EPU) or any other uncertainty such as geopolitical uncertainty is present. Considering this context, this paper examines the US EPU’s cross-correlation with both conventional and Islamic stock markets from the perspective of multifractality. Daily stock market prices of five main countries are considered: US, Thailand, Indonesia, Pakistan, and India. Using the multifractal detrended cross-correlation analysis (MF-DCCA), we validate the existence of long-range cross-correlation between US EPU and all the stock markets considered, demonstrating that all pairs of US EPU have strong power law and multifractal characteristics. Furthermore, all pairs display varying levels of multifractal strength, with the US EPU and US conventional stock market exhibiting the strongest multifractal patterns. Additionally, a cross-correlation between US EPU and the different stock markets is found to be persistent. The results of this study are pertinent to the various market participants in both conventional and Islamic markets, particularly investors, who may be able to draw useful conclusions from them for purposes such as portfolio diversification.
Faheem Aslam, Ahmed Imran Hunjra, Elie Bouri, Khurrum Shahzad Mughal, and Mrestyal Khan
Elsevier BV
Faheem Aslam, Paulo Ferreira, and Haider Ali
MDPI AG
The investigation of the fractal nature of financial data has been growing in the literature. The purpose of this paper is to investigate the impact of the COVID-19 pandemic on the efficiency of agricultural futures markets by using multifractal detrended fluctuation analysis (MF-DFA). To better understand the relative changes in the efficiency of agriculture commodities due to the pandemic, we split the dataset into two equal periods of seven months, i.e., 1 August 2019 to 10 March 2020 and 11 March 2020 to 25 September 2020. We used the high-frequency data at 15 min intervals of cocoa, cotton, coffee, orange juice, soybean, and sugar. The findings reveal that the COVID-19 pandemic has great but varying impacts on the intraday multifractal properties of the selected agricultural future markets. In particular, the London sugar witnessed the lowest multifractality while orange juice exhibited the highest multifractality before the pandemic declaration. Cocoa became the most efficient while the cotton exhibited the minimum efficient pattern after the pandemic. Our findings show that the highest improvement is found in the market efficiency of orange juice. Furthermore, the behavior of these agriculture commodities shifted from a persistent to an antipersistent behavior after the pandemic. The information given by the detection of multifractality can be used to support investment and policy-making decisions.
Faheem Aslam, Ahmed Imran Hunjra, Zied Ftiti, Wael Louhichi, and Tahira Shams
Elsevier BV
Leonardo H.S. Fernandes, José W.L. Silva, Fernando H.A. de Araujo, Paulo Ferreira, Faheem Aslam, and Benjamin Miranda Tabak
Elsevier BV
Faheem Aslam, Tahir Mumtaz Awan, and Tayyba Fatima
Springer Science and Business Media LLC
Faheem Aslam, Paulo Ferreira, Haider Ali, and Sumera Kauser
Springer Science and Business Media LLC
Faheem Aslam, Paulo Ferreira, Haider Ali, and Ana Ercília José
MDPI AG
Since the industrial revolution, the geopolitics of energy has been a driver of global prosperity and security, and determines the survival of life on our planet. This study examines the nonlinear structure and multifractal behavior of the cross-correlation between geopolitical risk and energy markets (West Texas Intermediate (WTI), Brent, natural gas and heating oil), using the multifractal detrended cross-correlation analysis. Furthermore, an in-depth analysis reveals different associations of the indices of overall geopolitical risk, geopolitical acts, and geopolitical threats against the four energy products. Based on daily data ranging from 1 January 1985 to 30 August 2021, the findings confirm the presence of nonlinear dependencies, suggesting that geopolitical risk and energy markets are interlinked. Furthermore, significant multifractal characteristics are found and the degree of multifractality is stronger between the overall geopolitical risk and WTI while the lowest degree of multifractality is with Brent. Overall, for the WTI and heating-oil markets, the influence of geopolitical threats is more pronounced rather than their fulfilment. Contrarily, the Brent and natural gas are more correlated to geopolitical acts. Energy products exhibit heterogeneous persistence levels of cross-correlation with all the indicators of geopolitical risk, being more persistent in the case of small fluctuations compared to large fluctuations.
Faheem Aslam, Ahmed Imran Hunjra, Tahar Tayachi, Peter Verhoeven, and Yasir Tariq Mohmand
SAGE Publications
This study investigates calendar anomalies in eight Islamic frontier markets. Our sample consists of the daily closing prices of their stock indices for the period of January 2006 to September 2019. The data are categorized with respect to day-of-the-week and month-of-the-year according to both the Gregorian and Islamic calendars. We control for time-varying systematic risk using Morgan Stanley Capital International (MSCI) index as the proxy for the market portfolio and apply Bonferroni correction to reduce the occurrence of false-positive results. We find little support for the proposition that any of the Islamic calendar months generate abnormal returns, bar a slight negative abnormal return during the holy month of Rajab for Kuwait. We find evidence of a small negative Monday effect for the stock markets of Bangladesh and Pakistan and a small positive January effect for the stock market of Morocco. The results support weak-form market efficiency, suggesting that investors in frontier stock markets should not expect to outperform the market on a consistent basis when trading on particular days of the week or months of the year according to the Islamic or Gregorian calendar.
Faheem Aslam, Zil-e-huma, Rashida Bibi, and Paulo Ferreira
Elsevier BV
Tahir Mumtaz Awan, Tayyaba Latif, Faheem Aslam, and Tayyba Fatima
Inderscience Publishers