A comparison of cryptocurrency volatility-benchmarking new and mature asset classes Financial Innovation Full Text

Table 3 presents the results for the reference specification of the panel HAR and its variant where the first lag of the volatility is decomposed into its signed components. Both model crypto volatility specifications are separately estimated for the cryptocurrency and the equity entities, as shown in the table. The literature relevant to this study bifurcates into two distinct yet interconnected domains. The first is the foundational body of work on volatility modeling in finance, which has been a key topic in financial econometrics. Bollerslev et al. (2006); Barndorff-Nielsen et al. (2008); Chen and Ghysels (2011) highlights the effect of negative equity returns on increasing future volatility. Table 3 presents the results for the reference specification of the panel HAR and its variant where the first lag of the volatility is decomposed into its signed components.

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Government regulations or https://www.xcritical.com/ policy changes can affect how cryptocurrency can be used and is viewed, leading to increased volatility. A recent example is the approval of spot Bitcoin exchange-traded funds (ETFs) in the US, which led to billions of US dollar inflows into the funds and price volatility. News, social media, and trader sentiment can heavily influence the demand and supply dynamics of cryptocurrencies, leading to volatile price movements. In other words, if it’s all crypto doom and gloom on TikTok and X, expect downward volatility swings.

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And the market capitalization of cryptocurrencies is related to the current demand for a particular coin locally and for bitcoins globally. Unlike fiat currencies, securities and other assets, cryptocurrency as such has emerged relatively recently. The community AML Risk Assessments of participants is still in the formation stage, it is replenished by traders with little experience. Until 2021, Bitcoin and cryptocurrencies at large were considered uncorrelated with traditional assets like stocks and bonds. Structural breaks in market dynamics, occurring around major global events, lead to shifts in trading behaviours and market perceptions, further influencing Bitcoin’s price fluctuations.

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This phenomenon is not entirely driven by the longer-term ups and downs reported in headlines. Bitcoin, Ethereum, and other cryptocurrencies frequently exhibit daily price drops during bull markets and increases during bear markets far in excess of traditional assets. The interactive chart below provides one way to visualize this day-to-day volatility—the daily percentage increase or decrease in price in U.S. dollars from the previous day. Regulatory efforts should enhance transparency through mandatory disclosure of trading data by exchanges and wallet providers, strengthening financial literacy with targeted educational initiatives on cryptocurrency risks and management.

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Figure 6 shows that Bitcoin was slow to react to the overall market distress that was caused by the COVID-19 crisis, which transmitted to cryptocurrencies roughly 30 days after traditional assets already experienced a sharp increase in expected volatility. The results are also similar to the paper on realized volatility from Conrad et al. (2018), where the authors find that equity volatility has a delayed spill-over effect on cryptocurrency volatility. ​​But cryptocurrencies are also exceptionally volatile over much shorter periods of time. ​Day-to-day price fluctuations of cryptocurrencies eclipse those of traditional currencies, stocks, and precious metals, and do so consistently across assets and time periods.

For crypto holders, including investors, the volatility of the cryptocurrency market becomes a source of profit or, conversely, loss. For example, bitcoin rose from $600 to $20,000 after the second halving and then fell to $3,500. If you understand the causes of volatility, which will be explained below, trading cryptocurrency will be less risky. “Many research articles were dedicated to the role of Bitcoin as a hedge or a safe haven from a portfolio perspective.

The effect is robust, even though more pronounced during the overall bull market of the 2020–2021 period. Also, in this model specification, the volatility appears more persistent in the two years, 2020–2021, than in the other estimation period. The purpose of computing the estimators described in this Section is to obtain a daily proxy for the quadratic variation of the log-return prices process from high-frequency data. It is well established that asset returns, especially crypto-assets (Osterrieder & Lorenz, 2017), are heteroskedastic, heavy-tailed, and susceptible to jumps. This has interesting implications, especially for the CVX76, as the index methodology relies on the assumption of normally distributed log-returns in the underlying, which is frequently challenged by strong market movements. More specifically, when comparing the index data of CVX and CVX76, one can see that the indices are more similar during less volatile times and vice versa.

In that regard, the cryptocurrency market historically showed high volatility traits (Bouri et al. 2019, 2020a, b) and potential for significant returns, resulting in being attractive for retail investors. Therefore, despite its growing popularity, the cryptocurrency market, if viewed in the context of price versus more mature asset classes, is notoriously unstable, with frequent and substantial fluctuations in value. The empirical analysis presented in our study reveals significant insights into the dynamics of cryptocurrency volatility, challenging traditional financial theories with empirical evidence from high-frequency data. Particularly, our findings on the inversion of the traditional leverage effect, where positive returns correspond to increased volatility, suggest a deviation from classical financial theory. This anomaly can be connected to the presence of momentum effects in cryptocurrencies, as reported in Yang (2019). Established behavioral finance theories, such as those addressing investor overconfidence, herd behavior, and the disposition effect (De Long et al. 1990; Barberis et al. 1998; Hong and Stein 1999; Daniel et al. 1998), can explain these unique volatility patterns.

3 and 4 because the BV estimators converge in the limit to the continuous component of the quadratic variation of the price process. 1 highlights the different scale of magnitude of the estimated realized variance for the cryptocurrency cross-section compared to the equity one as a signal of more frequent large shifts in the price variation. However, this difference does not impact how we construct n-days volatility estimators that follow the same process for both asset classes. It is also flexible because it can be generalized to other volatility measures that reflect specific aspects of the volatility process, such as the realized semivariance to assess the downside risk of an asset (see Aït-Sahalia and Jacod (2014) for more details).

Despite much public discussion about cryptocurrencies as speculative investments or world-changing technology, their success ultimately hinges on widespread adoption as currencies—including as a medium of exchange. This creates problems for a currency’s usefulness as a medium of exchange if one or both parties to the transaction need to quickly move their money into a different currency. Either the buyer or seller, or both, must take this exchange rate risk, increasing the transaction cost and, ultimately, the price. Few asset classes have been more volatile over the past several years than cryptocurrencies.

• Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’ future volatility, unlike what emerges from the same study on a cross-section of stocks.
• Output from Alpha should not be construed as investment research or recommendations, and should not serve as the basis for any investment decision.
• Depending on where you find yourself in the cryptocurrency space, that word can mean a lot of joy or heartbreak.
• This Section provides the different results of our empirical analysis where we comment on the estimation results of various model specifications described in .
• The stories of early adopters and miners, who held a bucketload of Bitcoin when they were worth pennies reaching millionaire status certainly caught the imagination of the planet.
• Indeed, the cryptocurrency ecosystem lives on blockchains that produce a lot of data and do not follow the same logic as the traditional financial market.

The latter relies on replication, e.g., the replication of an option payout trough dynamic hedging in the underlying. A lack of liquidity leads to unstable and intransparent prices, which in turn limit our ability to assess the fair value of a position (mark-to-market), manage risk, and ultimately trade at a fair price. Before formalizing the index and its rules, this section reviews the underlying market of cryptocurrency derivatives.

Section 3.1 lays out general index rules, such as option selection criteria and the interpolation method. Those index rules are designed to be as similar to existing volatility indices as possible, while accounting for the specifics of cryptocurrency markets. Sections 3.2 and 3.3 introduce two alternative volatility measures that are suitable for the index.

One of the most prominent ways that people believe they have found to avoid volatility in the cryptocurrency market is to peg their digital assets to a commodity or currency. JP Morgan has become the latest to give this a go with their controversial JPM Coin which operates on a permissioned blockchain and has the value tied to the dollar. Deposits into this account are used to purchase 10 investment-grade and high-yield bonds. The Bond Account’s yield is the average, annualized yield to worst (YTW) across all ten bonds in the Bond Account, before fees. A bond’s yield is a function of its market price, which can fluctuate; therefore a bond’s YTW is not “locked in” until the bond is purchased, and your yield at time of purchase may be different from the yield shown here.