How Crypto Traders Can Take Advantage of Volatile Trading

More specifically, this paper uses the Black (1976) model to compute the implied volatility crypto volatility trading from a market of cryptocurrency options and futures. This is similar to the implied volatility—sometimes called ‘Black’ volatility—that is available, e.g., through Bloomberg’s volatility functions. For investors and traders, understanding their risk tolerance is always the first step before engaging in any form of investments. Different individuals possess a different level of risk tolerance, and this affects their choice of investments.

Impact of volatility on investments

Our paper ventures beyond the market standard by acknowledging that the market liquidity of Bitcoin options, even on the most liquid exchanges, is far inferior to the S&P 500 options that are the basis for the original VIX index. We therefore consider alternatives for the volatility extraction as well as index aggregation. The two resulting volatility indices are cointegrated and the corresponding error correction model can be utilized as a metric https://www.xcritical.com/ for market implied tail-risk.

Disentangling the effects at the individual level

Zhang et al. (2018) explores the stylized facts of the daily returns of eight cryptocurrencies, finding heavy-tailed distributions. In contrast, Phillip et al. (2018) study the stylized facts of a larger cross-section of cryptocurrency daily returns. In particular, the literature analyzed the time-varying relationship between financial returns and their volatility at the daily level is usually investigated through a GARCH framework. Many works (Liu and Serletis, 2019; Fakhfekh and Jeribi, 2020; Wajdi et al., 2020) retrieve an inverted leverage effect in the cryptocurrency market using several variants of the original GARCH model on daily data.

International Review of Economics & Finance

Figure 1 depicts the co-movement of US stock returns’ realized volatilities and Bitcoin prices. The paired series exhibit a negative relationship, with observable peaks in realized volatilities of stock returns being matched with troughs in Bitcoin prices, as shown in the figure. We also observe a significant increase and the highest peak in realized volatilities of stock returns on March 27, 2020, which coincides with the period following the declaration of COVID-19 as a pandemic.

Diversification in the Crypto Market

Through thorough research, risk management, and disciplined trading strategies, traders can navigate market swings and minimise potential losses. Technical analysis tools can assist in predicting and managing volatility, while diversification and portfolio management strategies can mitigate risk and maximise returns. Technical analysis is also a popular approach used by traders to predict and manage volatility in the cryptocurrency market. Various technical analysis tools can assist in identifying patterns, trends, and potential price movements. Trying to time the market by buying low and selling high can be a risky and challenging strategy.

Intermediary asset pricing: New evidence from many asset classes

In this regard, we document the asymmetric effect of returns when accounting for the intraday jump dynamics. This distinction between investor behaviors underpins the varied volatility drivers in the market. The difference eliminates the common integrated variance term and is positive when positive shocks are prevalent in a day and negative if adverse shocks dominate a day. Using the signed jumps component instead of the whole jump component, RV – BV, gives the advantage of separately studying the impact of significant positive and negative price movements on future volatility. Merton (1976) first introduce the idea of a jump component as a jump-diffusion process where large variations of prices occur at a discrete time together with small continuous movements.

The psychological significance of past trends in cryptocurrency market analysis

When diversifying, it’s important to consider the different types of cryptocurrencies available. Invest in a mix of established cryptocurrencies, promising projects, and stablecoins to balance risk and potential returns. Established cryptocurrencies like Bitcoin and Ethereum provide stability to your portfolio, while promising projects offer the potential for exponential growth. Stablecoins, on the other hand, can act as a hedge during times of extreme volatility.

• It appears that during these times, investors are either apathetic towards price, demoralized by the price action, or in some cases have sold and left the bitcoin market altogether.
• This is additional evidence on the limits of diversification during times where it is needed most.
• It is difficult to predict what will happen to prices when the limit is reached; there will no longer be any profit from mining Bitcoin.
• Most of Bitcoin’s price volatility comes from investor fears of missing out on big price movements.
• Because CFDs are leveraged, you can open a position by outlaying an initial amount that’s only a fraction of your total exposure to the market.
• On the contrary, the SJV estimators and their signed components are not strongly correlated with the other estimators for the equity cross-section.

For example, over the last two years, bitcoin has been less volatile than Netflix (NFLX) stock. The realized volatility of NFLX on a 90-day timeframe averaged 53%, while bitcoin’s realized volatility over the same timeframe averaged 46%. This fear index is popularly used by crypto traders not just to indicate volatility, but to search for the entry and exit points. This makes it a wonderful index to use when looking for breakouts to buy into. Created by John Bollinger, the index uses three lines (or bands) to display the moving average alongside positive and negative deviations.

Choi and Yang (2019) show empirically that the approximation error under a jump diffusion process can be as much as 5%; however, the authors also find that the error for the majority of their data is below 1%. Similarly, Chow et al. (2018) claim that VIX undervalues volatility when returns are expected to be negatively skewed and vice versa. Said authors propose an alternative method (‘GVIX’) that aims at resolving these shortcomings. However, for the purpose of this paper, comparability to existing benchmarks outweighs technical improvements.

We specifically assess Bitcoin prices’ ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers.

CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks. In essence, this means that if Bitcoin’s price falls suddenly, it may affect investor sentiment and, as a result, other markets such as the stock market (see Attarzadeh and Balcilar 2022). Given the level of market integration in advanced economies like the United States, the possibility of such a spillover effect is even more plausible. However, for the purpose of our study, we do not assume that this occurs uniformly across all stock sectors.

This paper extends the analysis of the price volatility inherent in the cryptocurrency market at a higher frequency level, exploring its dynamics and explaining its main drivers. The developing interest in cryptocurrencies from regulatory bodies partially stems from the lack of clarity regarding their classification as an asset class (Corbet et al., 2019). A detailed study of market behaviors, such as the asymmetric effects on volatility, can contribute to a deeper understanding of the fundamental characteristics of cryptocurrencies. By comparing these behaviors with those observed in established asset classes, we can provide insights that may assist in developing appropriate regulatory measures other than better-informed investment choices. Such insights are particularly valuable given the cryptocurrency markets’ rapid growth and the increasing number of market participants.

Second, stock market performance is regarded as an important indicator of macroeconomic stability within an economy, as well as a means of attracting foreign investment. In sum, as long as the US stock market is an integral part of the US economy, discussions and analyses about risk predictors will be prominent among investors and policymakers. Methodologically, we apply Westerlund and Narayan’s (2012, 2015) model, which accounts for key salient data features, such as endogeneity, persistence, and conditional heteroscedasticity. We conduct in-sample and out-of-sample analyses over multiple forecast horizons. Our analysis allows for possible structural breaks within the model framework to enhance the predictive capability of the applied model (Salisu et al. 2019a).

A store of value is an asset’s function that allows it to maintain value in the future with some degree of predictability. Many investors believe that Bitcoin will retain its value and continue growing, using it as a hedge against inflation and an alternative to traditional value stores like gold or other metals. It is unclear how Bitcoin whales—investors with BTC holdings large enough to influence market value—would liquidate their significant positions into fiat currency without affecting Bitcoin’s market price. If the whales were to begin selling their Bitcoin holdings suddenly, prices would plummet as other investors panicked as well. Supply and demand influence the prices of most commodities more than any other factor. Bitcoin’s market value is affected by how many coins are in circulation and how much people are willing to pay.