Does volatility connectedness across major cryptocurrencies behave the same at different frequencies? A portfolio risk analysis

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Abstract

This study investigates dynamic frequency connectedness for volatility differences among eight popular cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dash, Monero, Ripple, Nem and Stellar). It employs the methodologies of Diebold and Yilmaz (2014; 2016) and Baruník and Křehlík (2018). Furthermore, an analysis of diversification benefits and downside risk reductions is carried out. The results demonstrated dynamic spillovers, which intensified after 2017. Furthermore, Bitcoin, Ethereum, and Litecoin are net transmitters of risk, which can be a contagion source; Dash, Ripple, Monero, Stellar, and Nem are net receivers of risk. Moreover, the short-term risk spillover is more pronounced than the medium- and long-term risk spillovers, which also increased after 2017. The directional spillovers among cryptocurrencies is sensitive to frequencies. Finally, adding a cryptocurrency to a benchmark Bitcoin portfolio provides diversification benefits and downside risk reductions. In contrast, adding Bitcoin to a cryptocurrency portfolio do not offers diversification opportunities.

Introduction

Cryptocurrency emerged following the 2008 global financial crisis and has since attracted special attention from investors, speculators, academics, and the media. Its market capitalization has seen spectacular growth since its creation. Bitcoin (BTC), the largest and most popular cryptocurrency, created in 2009, accounts for 50 percent of the total market capitalization. Ethereum (ETH), created in 2015, is the second largest market after BTC; it accounts for 13 percent. Ripple (XRP) accounts for 5 percent.1 Recently, cryptocurrencies experienced episodes of heightened instability and risk. This turbulence allows for understanding whether a relationship exists among these digital currencies; especially, whether these instabilities result in volatility connectedness among cryptocurrencies and whether potential diversification opportunities exist among these markets, as is the aim of this study. This would be useful to investors and traders.

The emerging empirical literature on cryptocurrency markets has addressed various topics including among others efficiency market hypothesis, and safe haven property of BTC. Urquhart (2016) analyzed the efficiency market hypothesis of BTC, showed that BTC becomes increasingly efficient, and explored the price clustering (Urquhart, 2017) in BTC. Bouri et al. (2017) and Selmi et al. (2018) addressed the property of BTC as a hedge and safe haven assets for stock and commodity markets. They show that including BTC in equity or oil commodity portfolio can reduce systematic risk and generate diversification benefits. Koutmos (2018a) investigated the transaction activity of the Bitcoin returns nexus. Platanakis et al. (2018) examined the portfolio diversification of four cryptocurrencies—BTC, Litecoin (LTC), XRP, and Dash (DASH)—using the naïve and optimal approach. Corbet et al. (2018) analyzed the relationship between three cryptocurrencies (BTC, XRP, and LTC) and different financial and commodity assets (S&P500, FX rates, gold, VIX, bond, lite. and GSCI index).

The above empirical literature, however, lacks an important topic: the connectedness across cryptocurrencies under the time-frequency domain and the existence of diversification benefits among cryptocurrencies. Moreover, the huge cryptocurrency market instabilities and volatilities push market participants to monitor spillover across cryptocurrencies; connectedness is crucial to modern financial risk management (Diebold & Yilmaz, 2014). Thus, this study answers the following questions: Is there volatility connectedness among major cryptocurrencies? Is volatility connectedness static or dynamic? Are there differences in volatility connectedness across frequencies (or short-, medium- and long-term horizons)? Which cryptocurrency is a net contributor and net receiver of shocks? Is BTC leader among cryptocurrencies?

This study contributes to recent empirical literature in at least three ways. First, we measure the dynamic volatility connectedness among eight major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Dash (DASH), Monero (XMR), Ripple (XRP), Stellar (XLM), and Nem (NEM)—from August 9, 2015, to February 7, 2019 using the methodology of Diebold and Yilmaz [hereafter DY] (2014; 2016) and that of Baruník and Křehlík [hereafter BK] (2018). The spillover index of Diebold and Yilmaz (DY) (2012, 2014, 2016) predicts the directional measurement of total volatility spillovers among assets. Specifically, it examines the cross-market linkages and identify the source of contagion as the index is flexible to determine the net directional spillovers. The identification of the asset that is a net receiver of risk or a net contributor of risk helps investors and portfolio managers to optimize their investment strategies and as a result to better understand and monitor the portfolio risk. we notice that in 2012, Diebold and Yilmaz have revised the spillover index developed in 2009 (see Diebold & Yilmaz, 2009) by applying a generalized vector autoregressive model in which forecast-error variance decompositions are independent to the variable ordering, and explicitly including the directional volatility spillovers. However, this technique assumes that the spillover effects among markets are the same across different frequencies. This assumption is away from the reality of the market behavior. Thus, the development of the frequency domain spillover analysis of Baruník and Křehlík (2018) (BK) which overcome the limits the DY methodology by assessing the total and directional volatility spillover among markets at multiple frequencies (short-, intermediate-, and long-term). It allows one to check whether the price transmissions from market i to market j are stronger at different frequencies, and if they vary across different categories of assets j. If the short-term volatility spillovers are higher than the intermediate- and long-term, it reveals that most of the investors behave in a similar manner at short-frequencies while in the medium- and long-term horizons they behave more heterogeneously while investing in risky assets (Kang et al., 2019). This decomposition is thus crucial for market participants (investors and policymakers) to quantify the extent of spillovers and identify their direction, which is important to determine the source of contagion and monitor the financial risk management. Cryptocurrency markets are highly volatile and traded 24 h. Thus, the spectral representation of variance decompositions is useful in our study because of significant interactions within crypto markets, with various degree of volatility persistence.Volatility transmission, directional spillovers, net spillovers and connectedness among financial assets are crucial for investors to determine the long and short position. Theoretically, investors sell risky assets (short position) and buy a riskless or safer asset (high-level of liquidity). According to the hypothesis of portfolio-rebalancing, investors change the portfolio composition by purchasing one asset and selling another. Thus, a better understanding of the evolving volatility connectedness among investors and portfolio managers can inform their investment decisions in detecting diversification benefit opportunities.

Second, we consider the time investment horizon factor (or frequencies) which is crucial for market participants. In fact, the directional spillovers and connectedness among markets are not stable across frequencies. More precisely, some investors are interested in the short term (high frequency) while others, the medium term (middle frequency) and the long term (low frequency). Thus, our study relies for the hypothesis of market investor heterogeneity by analyzing the volatility connectedness under short, medium, and long-term horizons. The time horizon decomposition considers whether any asset behaves the same under different time-frequency spaces; that is, is it always a recipient of shocks in the short, medium, or long term, or does it become a transmitter of shocks? Disentangling short-term from long-term movements in connectedness is well-documented in the theoretical literature (Blanchard & Quah, 1989; Gonzalo & Ng, 2001; Quah, 1992). This study considers three-time investment horizons (the short-term ranges between 1 and 5 days, the medium-term, 5–22 days, and the long-term, above 22 days). The decomposition of a system into short- and long-term is significant for co-integration (Engle & Granger, 1987). Baruník and Křehlík (2018) document that a shock with a strong long-term effect will have a high power at low frequencies and, in case it transmits to other variables, points to long-term connectedness. Balke and Wohar (2002) show that long-term volatility spillovers are due to permanent changes in investor expectations about future dividends.

Finally, we analyze the potential portfolio diversification and downside risk reduction benefits in the cryptocurrency markets. Since BTC has the largest market capitalization among more than one hundred cryptocurrencies, we examine the risk evaluation of a portfolio composed of BTC and each of the other cryptocurrencies using different portfolios and risk measures. A benchmark portfolio composed of BTC and three other portfolios (an optimally weighted, equally weighted, and a hedged portfolio) composed of BTC and one other cryptocurrency are considered. Our risk measures are risk reduction, Value-at-Risk (VaR), expected shortfall (ES), semivariance (SV), and regret (Re). For a deepen analysis on diversification, we augment our analysis by plotting the return-variance efficient frontier for a portfolio without and with BTC. An analysis on the portfolio performances without and with BTC by quantifying the portfolio returns, risk and Sharpe ratios are also considered.

This study is, evidently, first to examine volatility connectedness among popular cryptocurrencies under different time-frequency spaces as it considers diversification benefits and downside risk reductions in cryptocurrency markets. Empirically, the used DY methodology presents several advantages: First, it examines the contagion and interdependence across markets. The differences in the dynamics that drive return and volatility spillovers over time have significant implications for asset allocation and portfolio management. The variance decomposition analysis of the vector autoregressive (VAR) model identifies spillovers of the first moment (return) and second moment (volatility) shocks from the indigenous shocks (Yilmaz, 2010). Second, it provides information on directional spillovers from one market to another based on the forecast error variance decompositions. Finally, it allows information on spillover trends, cycles, and bursts (Diebold & Yilmaz, 2012). The BK methodology considers interaction and interdependence among financial assets under different frequencies which are crucial for investors and policymakers. This approach is more in-depth as it considers the degree of connectedness changes according to frequencies and investor expectations. Thus, it helps investors obtain more information on portfolio diversification, hedging strategies, and contagion effects.

The choice of the eight cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dash, Monero, Ripple, Nem and Stellar) is motivated by three principal reasons. First, the considered cryptocurrency assets are a good proxy for our analysis. They show a significant portion of market capitalization and traded volume. It accounts for more than 67% of total market capitalization. They are the most liquid in term of trading volume, making potential price manipulation more difficult and thus inferences from the empirical analyses more solid (Bouri et al., Bouri et al., 2020, Bouri et al., 2020b). They are selected from the largest 30 cryptos by market capitalization and trading volume. Specifically, BTC, ETH, XRP, LTC, XLM, XMR, DASH, and NEM are ranked 1, 2, 4, 7, 14, 16, 23, and 30, respectively.2 BTC is traded at any time and traded with main currencies at low transaction costs (Cagli, 2019; Kim, 2017). These eight crypto assets cover large and emerging cryptocurrencies making our study useful for cryptocurrency market participants.

Empirical results from the DY methodology show that BTC, ETH, and LTC are net transmitters of risk while the others are net receivers. In addition, the risk spillovers exhibit an upside trend after 2017, corresponding to the US subprime crisis period and the huge cryptocurrency price decline in 2018. Using the BK methodology, short-term risk spillovers are higher than medium- and long-term horizons, indicating an asymmetry in risk spillovers. More importantly, we observe a change in the directional spillovers of cryptocurrencies (except BTC, DASH, LTC and XRP) when the frequencies vary, indicating that the investors should alter their position and should rely on the time investment horizon variable to build their portfolio. BTC and LTC markets are net transmitters of risk while DASH and XRP are net receivers of risk, regardless of the frequencies. The results of the rest depend on frequencies. More precisely, ETH is a net receiver of shocks only in the short term (between 1 and 5 days) and a net transmitter of risk above 5 days (medium and long term). NEM and XLM are net contributors of risk in the short term and net receivers in both the medium and long term. We note that BTC, ETH, NEM, XLM, and XMR markets transmit more risk to other markets in the short term than in the medium and long term while the results for the remaining markets are mixed. A portfolio risk assessment shows that a portfolio composed of BTC and one other cryptocurrency provides diversification benefits and downside risk reductions. The risk-minimizing portfolio offers higher risk reductions for LTC, DASH, XMR, and NEM; the hedging portfolio provides the highest risk reduction for ETH, XRP, and XLM. Overall, an LTC-BTC optimally weighted Portfolio generates the highest risk reductions. However, including BTC to the other cryptocurrencies rises risk and reduce returns. This result is not surprising as BTC remains too volatile.

The rest of this study is organized as follows. Section 2 presents a review of literature. Section 3 presents data and explains the methodology. Section 4 discusses the empirical results. Section 5 concludes.

Section snippets

Literature review

An emerging and growing empirical literature review has tackled the interdependencies in the crypto markets. Yi et al. (2018) use the spillover index methodology to analyze the volatility connectedness of eight major cryptocurrencies and show cyclical fluctuations in their connectedness and a significant upside trend since the end of 2016. Extending their analysis to 52 cryptocurrencies, the authors built a volatility connectedness network and show that the cryptocurrencies are tightly

Data

We employ daily prices for the aforementioned eight major cryptocurrencies (Bitcoin, Ethereum, Litecoin, Dash, Monero, Ripple, Nem and Stellar). The choice of these cryptocurrencies is motivated by their large market capitalization and trading volume in respect to the other cryptomarkets. The market capitalization of both Bitcoin and Ethereum represents more than 50 per cent of total market capitalization in the cryptomarket.3 They are

Risk spillover analysis for raw series

Table 2 reports the estimation results of static volatility spillover index and shows that the total spillover is 24.7%. This indicates the presence of market integration and interdependence. We can see from Panel A that three out of eight cryptocurrencies (BTC, LTC, and ETH), having similar percentages, transmit risk the most (more than 25%) to the rest of the markets. The remaining four—ETH, DASH, XMR, and NEM—are low shock transmitters (less than 20%). More precisely, LTC is the most

Conclusion

Cryptocurrencies have significantly increased since its creation. Investors are, thus, demonstrably interested in this new investment. More than one hundred cryptocurrencies exist, indicating their importance for investors as speculative investments. This study contributes to the cryptocurrency literature by exploring dynamic volatility connectedness among major cryptocurrencies at different frequencies, which is important to protect against contagion effects. Using the DY methodology, we show

Author statement

This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood your journal's policies, and we believe that neither the manuscript nor the study violates any of these. There are no conflicts of interest to declare.

Acknowledgment

This research is partly funded by the University of Economics Ho Chi Minh City, Vietnam. The last author acknowledges the financial support by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A5B8103268).

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