Research output per year
Research output per year
I am a lecturer in finance at Alliance Manchester Business School. Regarding research, my interests cover empirical finance and asset pricing, empirical macroeconomics and business cycle, with a focus on employing advanced times series econometrics. See my papers in the research output or on my personal website: sites.google.com/view/mohammaddehghani
Regarding teaching, I have been delivering lectures in undergraduate and postgraduate courses, including Foundations of Finance, Investment Analysis, Quantitative Methods, Financial Economics, Mathematical Economics, Econometrics, Advanced Mathematics, etc.
I received undergraduate degree in Materials Engineering from Sharif University of Technology, Tehran, Iran, and my master degree in strategic management from Malek University of Technology, Tehran, Iran. I was a PhD dstudent in economics at Sharif University of Technology; but after passing comprehensive exam, in 2017, I decided to re-apply and continue my PhD abroad. Having admission from different universities such as Rice, North Carolina, UCL, ESSEC, Tilburg, etc. I selected University of Manchester to study PhD in finance. I have completed my PhD in finance in 2022.
My fields of interest are empirical finance and macroeconomics. My PhD thesis “Financial Crises and Economic Recessions,” conducted under the supervision of Stuart Hyde and Sungjun Cho, includes three journal-format papers:
Paper 1: Asymmetric Co-fluctuations of U.S. output and Unemployment: Friedman’s plucking model and Okun’s law.
Paper 2: Asymmetric Fads and inefficient plunges: Evaluating the Adaptive vs. Efficient Market Hypotheses.
Paper 3: Slow recovery of output after the 2007−09 financial crisis: U.S. shortfall spillovers and the U.K. productivity puzzle.
I'm expert in the following time series Econometric methods:
1. Dynamic Factor Model; 2. Univariate and multivariate state-space models; 3. State-space models with Markov- switching (combining Kalman Filter and Hamilton filter for approximate maximum likelihood estimation).
My experience as a lecturer, teaching associate and/or teaching assistant are:
Date | courses | My Roles | Other Lecturers |
2022-25 | Foundations of Finance 1 & 2 | Lecturer | Christopher Godfrey, Sze Nie Ung, and Ahmed Prapan |
2022-25 | Quantitative Methods | Lecturer | I am the only lecturer in this course. |
2022-25 | Investment Analysis | Lecturer | Yoichi Otsubo, Yifan Li, Lijie Yu |
2018-22 | Mathematical Economics | Teaching Associate | Klaus Reiner Schenk-Hoppê |
2018-22 | Game Theory and Dynamic system | Teaching Associate | Leonidas Koutsougeras |
2018-22 | Foundation of Finance 1 & 2 | Teaching Assistant | Christopher Godfrey, Maria Marchica, Stefan Petry, and Brahim Saadouni |
2020-22 | Financial Economics | Teaching Associate | Leonidas Koutsougeras |
2020-22 | Financial Economics (Master) | Teaching Associate | Igor Evstigneev |
2020-22 | Microeconomics (Master) | Teaching Associate | Klaus Reiner Schenk-Hoppê |
2020-22 | Mathematical Economics II | Teaching Associate | Igor Evstigneev |
2020-22 | Introduction to Math. Econ. | Teaching Assistant | Chris Wallace |
2018, 22 | Advanced Mathematics | Teaching Assistant | Ralf Becker |
2020-22 | Econometrics | Teaching Assistant | Rober O' Neil |
2016-17 | Econometrics | Teaching Assistant | S. M. Barakchian |
2015-16 | Advanced Macroeoconomics | Teaching Assistant | S. A. Madanizadeh |
Before 2014 | Math, Algebra, calculus, etc. | Teacher at High Schools |
Mar 2022 | Excellence in Teaching Awards, Division of finance, University of Manchester. | |
Sep 2020 | Excellence in Teaching Awards, Department of economics, University of Manchester. | |
May 2019 | Ranked 1st among all PhD students in finance by GPA (83/100), Alliance Manchester Business School, 2017 cohort. | |
May 2018 | Best School Abstract, Doctor Conference, Alliance Manchester Business School, 2017 cohort. | |
Sep 2017 | Scholarship award, PhD in Finance, Alliance Manchester Business School, U of Manchester | |
Apr 2017 | Scholarship/Fellowship award, PhD in Finance or Economics, UCL, Rice, North Carolina , Tilburg , ESSEC , Berlin, etc. | |
Dec 2015 | Ranked 2nd among all PhD students in Economics, GPA: 4.0/4, Sharif University of Technology, 2014 cohort. | |
Mar 2014 | Ranked 2nd in the Nationwide University Entrance Exam for PhD in Economics, Iran. | |
Feb 2013 | Ranked 1st among all Masters students in Management, GPA: 4.0/4, Malek University of Technology, 2011 cohort. |
Jun 2021 | Fellowship of the Higher Education Academy, AdvanceHE | |
Jun 2021 | TeachECONference2021, University College London, Online. | |
Sep 2020 | Graduate Teaching Assistant workshop, UK Professional Standard Framework, Economic Network, Online | |
Sep 2018 | Graduate Teaching Assistant workshop, UK Professional Standard Framework, Economic Network, Manchester | |
Jun 2016 | Math and Stat Camp which covered topics such as real analysis, measure theory, probability, estimation, Rice University, USA | |
Aug 2015 | Scholarship/Fellowship award, PhD in Finance or Economics, UCL, Rice, North Carolina , Tilburg , ESSEC , Berlin, etc. | |
Dec 2015 | IIEA Workshop with top scholars presenting recent advances in micro, macro, and econometrics, Bilgi University, Turkey Dec 2014 | |
Dec 2014 | 25th Annual Conference on Monetary and Exchange Rate Policies, Iran | |
Nov 2014 | Mechanism Design of social systems Workshop, Sharif University of Technology, Iran |
Jun 2016 | Non-Performing Loan: A DSGE model with Financial Friction, PhD research paper | |
Jun 2013 | Investigating the effect of Ambidextrous Strategy and Ambidextrous Innovation on Organizational Performance, MSc thesis | |
May 2013 | A Framework for Managers Performance Measurement and Leadership Pipeline, Journal of criteria Management | |
Jan 2013 | Value Creation in Multi-Business Corporation: Parenting Style for Controlling Business Units, Journal of criteria Management |
Date | Course | Location |
Mondays 11-13, Tuesdays 12-14 | Foundations of Finance, Quantitative Methods. | Allaince Manchester Business School (AMBS), Room 5.010 |
I do not take PhD student. For MSc dissertation, I encourage students with an interest in the areas of empirical finance, asset pricing, and other topics related to time series econometrics to select me as the dissertation supervisor. In particular, I am willing to supervise the following topics, though you are more than welcome if you consider any other related topic within the above areas or want to amend or change these topics. |
Topic A. Testing the random walk hypothesis, the efficient market hypothesis, and/or the predictability of stock returns and/or cryptocurrencies: The Efficient Market Hypothesis (EMH) states that market prices reflect all available information (Samuelson, 1965; Fama, 1970), and no one can beat the market because market prices are not predictable. There are different forms of EMH, one of which is the Random Walk Hypothesis (RWH). Based on the RWH, the stock market moves randomly. On the other hand, behavioural economists suggest that due to overreaction, panic, human errors, etc., markets are not always efficient. The recent empirical results concerning market inefficiency are mixed. For example, Durusu-Ciftci et al. (2019) review the literature and the methodologies and conclude in favour of the RWH. However, Hill and Motegi (2019) report against RWH for the U.S. and U.K. markets during financial crises. In this context, to reconcile EMH and behavioural economics, the Adaptive Market Hypothesis (AMH) supposes that market inefficiency is time-varying. This may motivate you to investigate the AMH versus the EMH by characterizing the evolution of market inefficiency over time.
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Topic B. Modeling volatility in financial markets (stocks and cryptocurrencies) and their differences: Engle (1982) captured volatility clustering by developing an ARCH model. Since then, several versions of this model have been applied to capture the volatility of different financial assets. Bollerslev (1986), Engle’s student, elaborated the ARCH into the GARCH. Glosten, Jaganathan, and Runkle (1993) test asymmetric volatility by developing TGARCH. Given the growing interest in cryptocurrency, it is appealing to apply and/or elaborate those models to a set of data for different cryptocurrencies.
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Topic C. Modeling bubbles in financial markets (stocks and cryptocurrencies): The history of financial markets hints at many episodes of speculative bubbles. Speculative bubbles are often defined as a positive deviation from the fundamental price (intrinsic value) that is followed by a burst. The dot.com bubble in the late-1990s, the real estate bubble in 2005, and the cryptocurrency bubbles in 2017 and 2021 are examples of notorious bubbles. Blanchard and Watson (1983), Tirole (1985), Diba and Grossman (1988), and Johansen et al. (2000) present “rational bubbles,” several models that attempt to rationalize the formation of speculative bubbles in the stock markets. Based on this model, speculative bubbles occur even if all investors have rational expectations and know that the bubble will eventually burst. Recently, Cheah and Fry (2015) repurposed the Johansen et al. (2000) model to inspect bubbles in bitcoin markets. Can we repurpose other models, e.g., Diba and Grossman (1988), and apply them to a set of cryptocurrencies to characterize the crypto-bubbles?
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Topic D. CAPM with a time-varying beta: The main aim of the Capital Asset Pricing Model (CAPM) is to estimate the cost of capital (required return) for firms according to the market risk premium (market excess return). According to Fama and French (2004), beta is a measure of the sensitivity of an asset with respect to the variation in the market return. Later, Fama and French (1996) proposed a three-factor model by adding a size factor and a book-market ratio factor to the market excess return factor. Nevertheless, there is ample evidence that beta is not stable in the long run (Groenwold and Fraser, 1999; Chen and Huang, 2007). If this is the case, CAPM suffers from this misspecification. How to test for the stability of the betas and how to model time-varying betas is what this research will be built on. An easy way is to define a rolling window and estimate beta for each window. You will also use structural break tests to check the potential breakpoint in beta before and after an event (Covid-19 is a good example). Another model is to estimate the CAPM with a time-varying coefficient by using the Kalman filter (in MATLAB and R, there is code to run this model). Page 510 of the book written by Tsay explains this model. Additionally, a Markov-switching model for beta is an idea that comes to mind.
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Data and methodology: Regarding the data, individual stock returns, market returns, and cryptocurrency returns or their indices are accessible in the Wharton Research Data Service (WRDS), Bloomberg Terminal, and other online databases. Depending on the topic, to address the research question(s), you will use one or some of the econometric models, including linear models, ARCH and GARCH models, correlation models, vector auto regressive, principal component analysis, structural break tests, random walk tests, asymmetric random walk models, Markov switching, state-space models, value at risk, etc. | |
Pre-requisite: Basic econometrics background gained through passing BMAN-71122 (time-series econometrics) and/or BMAN-70211 (cross-sectional econometrics) is expected, though motivation to learn and apply new methods is more important. It is essential to know a programming language (e.g., MATLAB, R, or Python), either by taking the corresponding university course or by self-studying. The logic and syntax of the above languages are similar enough that knowing one is enough to learn another. This is the student’s choice to select one of the above languages depending on the topic, method, and availability of the codes and packages. Although I advise using one of the above to improve programming skills, it is acceptable to use STATA. | |
MSc dissertation in academic year 2021-22: I encourage students with an interest around the areas of empirical finance and assert pricing, empirical macroeconomics and business cycles, monetary policy and other topics that is related to the time series econometrics. Regarding the research topics, I am willing to supervise the following topics. If you consider any other research topics within the above areas or want to amend/change these topics, you are more than welcomed.
Regarding the methodology, you may apply one or some methods to address the research question(s). I am familiar with these methods: Linear models (e.g., Regression), ARCH and GARCH models, correlation models, Vector Auto Regressive (VAR), Principal Component Analysis (PCA) and factor models, structural break tests, random walk tests, asymmetric random walk, Markov Switching, state-space models and trend cycle decomposition, Value at Risk (VaR), event study, difference in difference regression. |
The selected courses I passed and my marks in my PhD degree are:
courses | Scores achieved | University | |
Financial Econometrics | 92/100 | Arthur Sinko | University of Manchester |
Applied Macroeconometrics | 90/100 | Arthur Sinko | University of Manchester |
Advanced Finance Theory | 90/100 | Hening Liu | University of Manchester |
Econometrics 2 (Time Series) | 17.3/20 | S. M. Barakchian | Sharif University of Technology |
Econometrics 1 | 19.4/20 | S. M. Barakchian | Sharif University of Technology |
Selected Topic in Macroeconomics | 19.5/20 | S. A. Madanizadeh | Sharif University of Technology |
Advanced Macroeconomics | 19/20 | S. M. Rahmati | Sharif University of Technology |
Selected Topic in Microeconomics | 18.3/20 | M. Vesal and F. Fatemi | Sharif University of Technology |
Advanced Microeconomics | 18.2/20 | G. R. Keshavarz | Sharif University of Technology |
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Research output: Preprint/Working paper › Working paper
Research output: Preprint/Working paper › Working paper
Research output: Preprint/Working paper › Working paper
Student thesis: Phd