Time frequency analysis of the commonalities between Bitcoin and major Cryptocurrencies: Portfolio risk management implications

被引:75
作者
Mensi, Walid [1 ,2 ]
Rehman, Mobeen Ur [3 ]
Al-Yahyaee, Khamis Hamed [2 ]
Al-Jarrah, Idries Mohammad Wanas [4 ]
Kang, Sang Hoon [5 ,6 ]
机构
[1] Univ Tunis El Manar, Dept Finance & Accounting, BP 248, Tunis 2092, Tunisia
[2] Sultan Qaboos Univ, Dept Econ & Finance, Coll Econ & Polit Sci, Muscat, Oman
[3] South Ural State Univ, Chelyabinsk, Russia
[4] Qatar Univ, Coll Business & Econ, Doha, Qatar
[5] Pusan Natl Univ, Dept Business Adm, Busan 609735, South Korea
[6] Univ South Australia, Sch Commerce, Adelaide, SA, Australia
基金
新加坡国家研究基金会;
关键词
Cryptocurrencies; Time frequency analysis; Hedging effectiveness; Downside risk; Wavelet techniques; INEFFICIENCY;
D O I
10.1016/j.najef.2019.02.013
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
This paper uses wavelet coherence and cross wavelet transform approaches to examine co-movement between Bitcoin and five major cryptocurrencies (Dash, Ethereum, Litecoin, Monero and Ripple) and their portfolio risk implications. The results show evidence of co-movements in time frequency space with leading relationships of Bitcoin with Dash, Monero and Ripple, lagging relationship with Ethereum, and out of phase movements with Litecoin. By considering different portfolios (risk-minimizing portfolio, equally weighted portfolio and hedging portfolio), we show evidence that a mixed portfolio (Bitcoin with other cryptocurrencies) provides better diversification benefits for investors and portfolio managers. Finally, an Ethereum-Bitcoin (Monero-Bitcoin) hedging portfolio offers the highest risk reductions and hedging effectiveness under medium and long term (short term) horizon. The results of downside risk reductions are time horizon dependent.
引用
收藏
页码:283 / 294
页数:12
相关论文
共 21 条
[1]   Using wavelets to decompose the time-frequency effects of monetary policy [J].
Aguiar-Conraria, Luis ;
Azevedo, Nuno ;
Soares, Maria Joana .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2008, 387 (12) :2863-2878
[2]   Estimating the Taylor rule in the time-frequency domain [J].
Aguiar-Conraria, Luis ;
Martins, Manuel M. F. ;
Soares, Maria Joana .
JOURNAL OF MACROECONOMICS, 2018, 57 :122-137
[3]   California's carbon market and energy prices: a wavelet analysis [J].
Aguiar-Conraria, Luis ;
Soares, Maria Joana ;
Sousa, Rita .
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2018, 376 (2126)
[4]   THE CONTINUOUS WAVELET TRANSFORM: MOVING BEYOND UNI- AND BIVARIATE ANALYSIS [J].
Aguiar-Conraria, Luis ;
Soares, Maria Joana .
JOURNAL OF ECONOMIC SURVEYS, 2014, 28 (02) :344-375
[5]   Oil and the macroeconomy: using wavelets to analyze old issues [J].
Aguiar-Conraria, Luis ;
Soares, Maria Joana .
EMPIRICAL ECONOMICS, 2011, 40 (03) :645-655
[6]  
[Anonymous], 2018, FINANCE RES LETT
[7]   Bull or bear markets: A wavelet dynamic correlation perspective [J].
Benhmad, Francois .
ECONOMIC MODELLING, 2013, 32 :576-591
[8]   Virtual relationships: Short- and long-run evidence from BitCoin and altcoin markets [J].
Ciaian, Pavel ;
Rajcaniova, Miroslava ;
Kancs, d'Artis .
JOURNAL OF INTERNATIONAL FINANCIAL MARKETS INSTITUTIONS & MONEY, 2018, 52 :173-195
[9]  
Dahlby B, 2014, CAN TAX J, V62, P301
[10]   WAVELET TRANSFORMS AND ATMOSPHERIC-TURBULENCE [J].
HUDGINS, L ;
FRIEHE, CA ;
MAYER, ME .
PHYSICAL REVIEW LETTERS, 1993, 71 (20) :3279-3282