Arbeitspapier

VCRIX - a volatility index for crypto-currencies

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.

Sprache
Englisch

Erschienen in
Series: IRTG 1792 Discussion Paper ; No. 2019-027

Klassifikation
Wirtschaft
Model Construction and Estimation
Model Evaluation, Validation, and Selection
Forecasting Models; Simulation Methods
General Financial Markets: General (includes Measurement and Data)
Thema
index construction
volatility
crypto-currency
VCRIX

Ereignis
Geistige Schöpfung
(wer)
Kim, Alisa
Trimborn, Simon
Härdle, Wolfgang Karl
Ereignis
Veröffentlichung
(wer)
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
(wo)
Berlin
(wann)
2019

Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Kim, Alisa
  • Trimborn, Simon
  • Härdle, Wolfgang Karl
  • Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

Entstanden

  • 2019

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