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VCRIX - a volatility index for crypto-currencies

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  • Kim, Alisa
  • Trimborn, Simon
  • Härdle, Wolfgang Karl

Abstract

Public interest, explosive returns, and diversification opportunities gave stimulus to the adoption of traditional financial tools to crypto-currencies. While the CRIX index offered the first scientifically-backed proxy to the crypto- market (analogous to S&P 500), the introduction of Bitcoin futures by Cboe became the milestone in the creation of the derivatives market for crypto- currencies. Following the intuition of the "fear index" VIX for the American stock market, the VCRIX volatility index was created to capture the investor expectations about the crypto-currency ecosystem. VCRIX is built based on CRIX and offers a forecast for the mean annualized volatility of the next 30 days, re-estimated daily. The model was back-tested for its forecasting power, resulting in low MSE performance and further examined by the simulation of VIX (resulting in a correlation of 78% between the actual VIX and VIX estimated with the VCRIX model). VCRIX provides forecasting functionality and serves as a proxy for the investors’ expectations in the absence of the de- veloped derivatives market. These features provide enhanced decision making capacities for market monitoring, trading strategies, and potentially option pricing.

Suggested Citation

  • Kim, Alisa & Trimborn, Simon & Härdle, Wolfgang Karl, 2019. "VCRIX - a volatility index for crypto-currencies," IRTG 1792 Discussion Papers 2019-027, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  • Handle: RePEc:zbw:irtgdp:2019027
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    Cited by:

    1. Victoria Dobrynskaya & Mikhail Dubrovskiy, 2022. "Cryptocurrencies Meet Equities: Risk Factors And Asset Pricing Relationships," HSE Working papers WP BRP 86/FE/2022, National Research University Higher School of Economics.
    2. Wang, Bingling & Li, Yingxing & Härdle, Wolfgang Karl, 2022. "K-expectiles clustering," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    3. Brauneis, Alexander & Mestel, Roland & Theissen, Erik, 2021. "What drives the liquidity of cryptocurrencies? A long-term analysis," Finance Research Letters, Elsevier, vol. 39(C).
    4. Ugolini, Andrea & Reboredo, Juan C. & Mensi, Walid, 2023. "Connectedness between DeFi, cryptocurrency, stock, and safe-haven assets," Finance Research Letters, Elsevier, vol. 53(C).

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    More about this item

    Keywords

    index construction; volatility; crypto-currency; VCRIX;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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