FRM for Cryptos

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  • 12 Students Enrolled

FRM for Cryptos

Financial risk meter -- cryptocurrency market.

  • 3.3 Rating
  • 1 Reviews
  • 12 Students Enrolled
  • Free

Courselet Content

2 components


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General Overview


This research has two major contributions. First, our study expands existing FRM papers in the economic interpretation of the risk index. Supported by simulation based empirical tests, Zbonakova, Härdle, and Wang (2017) show that index depends on three major factors: the variance of the error term, the correlation structure of the covariates and the number of non-zero coefficients of the model. Ren, Lu, Li, and Härdle (2022) and Wang, Althof, and Härdle (2023) further extend it by adding Lagrange interpretation. Based on their analysis, this work offering both empirical and analytical evidence of the correlation between FRM index and the pricing kernel’s volatility.

Second, our study proves that FRM reflects forward-looking risk information in cryptocurrencies. The option markets for cryptocurrencies are less mature in comparison to more established assets such as equities. Therefore, calculating the risk-neutral distribution used to price these assets presents greater complexity, and even when a reliable risk neutral distribution is available, it is still difficult to estimate the physical distribution due to the lack of the characteristic information of the marginal investors. As a result, obtaining forward-looking risk estimates from the option market is challenging. In this particular context, we employ FRM to compute the spread between in-sample and out-of-sample predictability to derive forward-looking insights within the realm of cryptocurrencies.

Courses that include this CL

Last Updated 8th January 2024
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Meet the instructors !

About the Instructor

Wolfgang Karl HÄRDLE attained his Dr. rer. nat. in Mathematics at Universität Heidelberg in 1982 and in 1988 his habilitation at Universität Bonn.  He is Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin and the director of the Sino German Graduate School (洪堡大学 + 厦门大学) IRTG1792 on “High dimensional non stationary time series analysis”.  He also serves as head of the joint BRC (with U Zürich).  He is guest professor at WISE, Xiamen U, SMU, Singapore, NYCU, Hsinchu TW, Charles U, Prague CZ.

His research focuses on data sciences, dimension reduction and quantitative finance.  He has published over 30 books and more than 300 papers in top statistical, econometrics and finance journals. He is highly ranked and cited on Google Scholar, REPEC and SSRN. He has professional experience in financial engineering, SMART (Specific, Measurable, Achievable, Relevant, Timely) data analytics, machine learning and cryptocurrency markets. He has created the platform, a financial risk meter, FRM, a cryptocurrency index, CRIX and organises regularly . He is 玉山学者 (Yushan Scholar), web page  


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