Mohammad Dehghani


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Personal profile


I am a lecturer in finance and economics 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:

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. 

Research interests

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.

Methodological knowledge

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: 

Datecourses      My RolesOther Lecturers
2022-24Foundations of Finance 1 & 2LecturerChristopher Godfrey, Sze Nie Ung, and Ahmed Prapan
2022-24Quantitative Methods in FinanceLecturer I am the only lecturer in this course.
2022-23Investment AnalysisLecturerYoichi Otsubo, Yifan Li, Lijie Yu
2018-22Mathematical Economics       Teaching AssociateKlaus Reiner Schenk-Hoppê
2018-22Game Theory and Dynamic systemTeaching AssociateLeonidas Koutsougeras
2018-22Foundation of Finance 1 & 2Teaching AssistantChristopher Godfrey, Maria Marchica, Stefan Petry, and Brahim Saadouni
2020-22Financial Economics Teaching AssociateLeonidas Koutsougeras 
2020-22Financial Economics (Master)Teaching AssociateIgor Evstigneev
2020-22Microeconomics (Master)Teaching AssociateKlaus Reiner Schenk-Hoppê
2020-22Mathematical Economics II    Teaching AssociateIgor Evstigneev
2020-22 Introduction to Math. Econ. Teaching AssistantChris Wallace 
2018, 22Advanced MathematicsTeaching AssistantRalf Becker
2020-22EconometricsTeaching AssistantRober O' Neil 
2016-17EconometricsTeaching Assistant S. M. Barakchian
2015-16Advanced MacroeoconomicsTeaching Assistant S. A. Madanizadeh
Before 2014   Math, Algebra, calculus, etc.          Teacher at High Schools 

Prizes and awards

Mar 2022Excellence in Teaching Awards, Division of finance, University of Manchester. 
Sep 2020Excellence in Teaching Awards, Department of economics, University of Manchester. 
May 2019Ranked 1st among all PhD students in finance by GPA (83/100), Alliance Manchester Business School, 2017 cohort. 
May 2018Best School Abstract, Doctor Conference, Alliance Manchester Business School, 2017 cohort. 
Sep 2017Scholarship award, PhD in Finance, Alliance Manchester Business School, U of Manchester 
Apr 2017Scholarship/Fellowship award, PhD in Finance or Economics, UCL, Rice, North Carolina , Tilburg , ESSEC , Berlin, etc.  
Dec 2015Ranked 2nd among all PhD students in Economics, GPA: 4.0/4, Sharif University of Technology, 2014 cohort. 
Mar 2014Ranked 2nd in the Nationwide University Entrance Exam for PhD in Economics, Iran. 
Feb 2013Ranked 1st among all Masters students in Management, GPA: 4.0/4, Malek University of Technology, 2011 cohort. 


Jun 2021Fellowship of the Higher Education Academy, AdvanceHE 
Jun 2021TeachECONference2021, University College London, Online.   
Sep 2020Graduate Teaching Assistant workshop, UK Professional Standard Framework, Economic Network, Online 
Sep 2018Graduate 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 2015Scholarship/Fellowship award, PhD in Finance or Economics, UCL, Rice, North Carolina , Tilburg , ESSEC , Berlin, etc.  
Dec 2015IIEA Workshop with top scholars presenting recent advances in micro, macro, and econometrics, Bilgi University, Turkey  Dec 2014  
Dec 201425th Annual Conference on Monetary and Exchange Rate Policies, Iran 
Nov 2014Mechanism Design of social systems Workshop, Sharif University of Technology, Iran 

Other research

Jun 2016Non-Performing Loan: A DSGE model with Financial Friction, PhD research paper 
Jun 2013Investigating the effect of Ambidextrous Strategy and Ambidextrous Innovation on Organizational Performance, MSc thesis 
May 2013A Framework for Managers Performance Measurement and Leadership Pipeline, Journal of criteria Management  
Jan 2013Value Creation in Multi-Business Corporation: Parenting Style for Controlling Business Units, Journal of criteria Management  

Office hours

Wednesdays 13-15, Fridays 14-16Foundations of Finance, Quantitative Methods.Allaince Manchester Business School (AMBS), Room 5.010

Supervision information

MSc dissertation in academic year 2022-23: 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.

  • Dehghani, M., Cho, S., & Hyde, S. (2022). Friedman plucking model and Okun’s law. Third chapter of the PhD thesis, The University of Manchester, Manchester.
  • Durusu-Ciftci, D., Ispir, M. S., & Kok, D. (2019). Do stock markets follow a random walk? New evidence for an old question. International Review of Economics & Finance, 64, 165-175.
  • Fama, E.F. (1970). Efficient capital markets: a review of theory and empirical work. The Journal of Finance 25, 383–417.
  • Hill, J. B., & Motegi, K. (2019). Testing the white noise hypothesis of stock returns. Economic Modelling, 76, 231-242.
  • Samuelson, P. A. (1965). Rational theory of warrant pricing. Industrial Management Review, 6, 13-31.

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.

  • Engle, R. (2004). Risk and volatility: Econometric models and financial practice. American economic review, 94(3), 405-420.
  • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.
  • Bollerslev T. (1986). Generalized autoregressive conditional heteroscedasticity Journal of econometrics., 31(3), 307-327.
  • Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, 48(5), 1779-1801.
  • Tsay, R. S. (2005). Analysis of financial time series. John wiley & sons.

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 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?

  • Blanchard, O. J., & Watson, M. W. (1983). Bubbles, rational expectations and speculative markets, NBER Working Paper 0945.
  • Cheah, E. T., & Fry, J. (2015). Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin. Economics letters, 130, 32-36.
  • Diba, B. T., & Grossman, H. I. (1988). Explosive rational bubbles in stock prices?. The American Economic Review, 78(3), 520-530.
  • Johansen, A., Ledoit, O., & Sornette, D. (2000). Crashes as critical points. International Journal of Theoretical and Applied Finance, 3(02), 219-255.
  • Tirole, J. (1985). Asset bubbles and overlapping generations. Econometrica: Journal of the Econometric Society, 1499-1528.

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.

  • Fama, E. F., & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of economic perspectives, 18(3), 25-46.
  • Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. The journal of finance, 51(1), 55-84.
  • Groenewold, N., & Fraser, P. (1999). Time-varying estimates of CAPM betas. Mathematics and Computers in Simulation, 48(4-6), 531-539.
  • Tsay, R. S. (2005). Analysis of financial time series. John wiley & sons.
  • Chen, S.-W. and Huang, N.-C. (2007). “Estimates of the ICAPM with regime-switching betas: evidence form four pacific rim economies”, Applied Financial Economics, 17: 313- 327.

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.  

  1. Financial markets asymmetric return distribution and asymmetric volatility: Leverage effect and volatility effect.
  2. Financial markets persistent and asymmetric volatility clustering.
  3. Speculative bubbles in asset prices, particularly stock markets and cryptocurrencies.
  4. Flash crashes (steep fall in a stock price at a higher frequency).
  5. Stock market crash (steep fall in the stock market at a lower frequency).
  6. Testing random walk hypothesis and efficient market hypothesis (Does market incorporates all of the available information?).
  7. CAPM with constant coefficient or time-varying coefficients.
  8. Forecasting trend and cycle output for the U.S., the U.K., or other economies.
  9. Quantity theory of money, zero lower bound, conventional and unconventional monetary policy before and after COVID-19.
  10. Asset prices, inflation, quantity and velocity of money.      

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. 



Units taught

The selected courses I passed and my marks in my PhD degree are:

courses      Scores achieved University
Financial Econometrics                       92/100Arthur Sinko   University of Manchester
Applied Macroeconometrics90/100 Arthur Sinko   University of Manchester
Advanced Finance Theory 90/100Hening LiuUniversity of Manchester
Econometrics 2 (Time Series)17.3/20S. M. BarakchianSharif University of Technology
Econometrics 119.4/20S. M. BarakchianSharif University of Technology
Selected Topic in Macroeconomics    19.5/20S. A. MadanizadehSharif University of Technology
Advanced Macroeconomics19/20S. M. RahmatiSharif University of Technology
Selected Topic in Microeconomics18.3/20M. Vesal and F. Fatemi   Sharif University of Technology
Advanced Microeconomics 18.2/20 G. R. KeshavarzSharif University of Technology


Expertise related to UN Sustainable Development Goals

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):

  • SDG 8 - Decent Work and Economic Growth

Areas of expertise

  • HB Economic Theory
  • Macroeconomics
  • Time Series Econometrics
  • Business cycles
  • Asymmetric Fluctuations
  • HG Finance
  • Financial Econometrics
  • Financial Crisis
  • Asset Pricing
  • Efficienct Market Hypothesis
  • Adaptive Market Hypothesis
  • Speculative Bubbles


  • Macroeconomics
  • Asset Pricing
  • Time Series Econometrics
  • Financial Crisis
  • Economic Recession


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