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Fools ignore complexity. Pragmatists suffer it. Some can avoid it. Geniuses remove it.

Alan J. Perlis


Photo credit: Amir Holakoo

I am the Senior Lecturer of Quantitative Finance at the School of Mathematics at the Monash University and a member of Monash Centre for Quantitative Finance. Before, I was a Post-Doctoral researcher at the Swiss Finance Institute at EPFL, EPFL and the European Center for Advanced Research in Economics and Statistics (ECARES), Free University of Brussels, Belgium. My CV. Google Scholar.

Working Papers (Latest First: date written)(Comments Welcome!):

  1. 1.Inside the Mind of Investors During the COVID-19 Pandemic: Evidence from the StockTwits Data, 2020.

       [Submitted] . [Replication Python Code] . [Replication Data]

       Media Coverage: [RIAIntel] . [Monash] . [PHYS.org] . [Miragenews] . [ausbiz]

                                         [stockhead] . [Lens] 


  1. 2.Asset Pricing with Neural Networks: A Variable Significant Test  (joint with Vincentius Franstianto), 2020.

       [Submitted] . [Replication Python Code] . [Slides]

  1. 3.Towards Explaining Deep Learning: Asymptotic Properties of ReLU FFN Sieve Estimators (joint with Vincentius Franstianto and Gregoire Loeper), 2019.

       [Replication Python Code: GitHub]

  1. 4.Model Risk and Disappointment Aversion (joint with Loriano Mancini and Stoyan Stoyanov), 2019.

       [under major revision: new draft coming soon!]

  1. 5.Risk Premia and Lévy Jumps: Theory and Evidence (joint with Julien Hugonnier and Loriano Mancini), 2019.


  1. 6.Time-changed Lévy processes and option pricing: a critical comment (joint with Kihun Nam), 2019.


Papers in Progress:

  1. 7.Assessing Pricing Errors in Asset Pricing Models: A Novel Econometric Evaluation Framework (Joint with Stoyan Stoyanov)

        [First draft coming soon!]   

  1. Objectives: We develop a novel econometric test, with the higher statistical power than extant tests, for the hypothesis that addresses the misspecification -- goodness-of-fit -- for a general class of asset pricing models.  

  1. 8.Smoothed Generalized Disappointment Aversion (Joint with Stoyan Stoyanov and Roméo Tédongap)

  2. Objectives: We extend the standard disappointment aversion model of Gul (Econometrica, 1991) and Routledge and Zin (JF, 2010) such that one is able to apply the traditional Taylor series expansion to an asset allocation problem. In addition, the model is more flexible to address some anomalies in the asset pricing such equity risk premium and Allais paradox.

  1. 9.Hedging Climate Change in Real Time

  2. Objectives: We introduce a dynamic hedging strategy for the climate change risk based on messages of large number of investors on the stock market. Thanks to the availability of the data, our hedging strategy can be implemented at the HF level.

  1. 10.The Structure of Market Sentiment

  2. 11.Who Influences Who? (Joint with Xin Lin)

Latest Publications:


  1. Fractional Calculus and Fractional Processes with Applications in Financial Economics (joint with Frank J. Fabozzi and Sergio Focardi), Elsevier (2017).


  1. Modeling Tail Risk with Tempered Stable Distributions: An Overview (joint with Gregoire Loeper),  Annals of Operations Research, (2019).

  2. Quanto Option Pricing with Lévy Models (joint with Frank J. Fabozzi, Young S. Kim and Jiho Park), Computational Economics, (2018).

  3. Quantile-based Inference for Univariate Tempered Stable Distributions, (joint with David Veredas and Frank J. Fabozzi) Computational Economics, (2017).

  4. Elliptical Tempered Stable Distribution, (joint with Y. S. Kim and Frank J. Fabozzi)  Quantitative Finance, (2016).


Research Interests:

Theoretical and Empirical Asset pricing Financial Econometrics

Machine Learning

Contact Details:

Email: hasan.fallahgoul@monash.edu,


Phone: +61 (0) 3 990 59894


School of Mathematics, Level 4

9 Rainforest Walk

Monash University

3800, Victoria