Could stock hedge Bitcoin risk(s) and vice versa?

Abstract

This paper is saddled with the task of investigating the Bitcoin market behavior in the presence of a government risk. This is because both the institutional and retail investors' interests in the Bitcoin market are growing rapidly. Conversely, the seemingly unregulated nature of this market is a serious concern to most economies and results to the placement of ban on Initial Coin Offering (ICO) in some economies by the government. Daily series of return and volume within the window of the ICO ban in China was used for the Bitcoin market and S&P500 stock market to examine the effect of a government risk in the Bitcoin market and possible hedging capabilities. Empirical results show that the ban dampened Bitcoin returns and the returns from each market can predict the other. The Exogenous Dynamic Conditional Correlation (Exo-DCC) model result suggests that, yes! the S&P500 stocks are capable of hedging Bitcoin risk, while Bitcoin can also hedge S&P500 stocks' risks and vice versa. The Exogenous BEKK (Exo-BEKK) model result shows evidence of bidirectional volatility spill over between the two markets studied. In practice, investors (institutions and retailers) can comfortably form a robust investment portfolio with (at least) these two assets and develop a hedging strategy, such that the impacts of risks on the portfolio's returns are safely hedged.

Using US Stock Sectors to Diversify, Hedge, and Provide Safe Havens for NFT Coins

“…Nekhili and Sultan (2021) found that copper futures were the best for insample hedging for Bitcoin in a long-term horizon, whereas live cattle futures had the best out-of-sample performance. Okorie (2020) found that the S&P 500 could be used to hedge an investment in Bitcoin.…”

A network analysis of electricity demand and the cryptocurrency markets

“…The measure of the unconditional volatility of an asset market is rather an area of key interest in studying market volatility structure. Unlike conditional volatility measures of the autoregressive conditional heteroscedastic models (Baba, Engle, Kraft, & Kroner, 1995; Bollerslev, 1986; Engle & Shepard, 2002; Okorie, 2019) the unconditional measures of a market's volatility have taken different forms, for instance, the squared return, the squared conditional mean return residual, even more, sophisticated approaches, etc. This paper adopts the best analytic scale‐invariant unconditional volatility estimator model proposed by Garman and Klass (1980) has been adopted in literature by Okorie and Lin (2020b), Diebold and Yilmaz (2016), Ji et al (2019), etc.…”

“…Considering the global futures market connectedness, there exist higher levels of information spillover during financial crisis periods with the FTSE 100 index as the leading transmitter (Kang & Lee, 2019). Considering return and volatility information spillover and conditional correlation, the Bitcoin and S&P500 can form a hedging portfolio (Okorie, 2019). Zhang, Zhuang, and Wu (2019) and Zhang, Zhuang, Lu, and Wang (2020) investigated volatility construction and spillover network in the G20 stocks and show that the network blocks have a “rich‐club” effect.…”

Crude oil market and Nigerian stocks: An asymmetric information spillover approach

“…However, as an alternative to the network modelling connectedness (Okorie, 2020c; Okorie & Lin, 2020a), we move further to adopt the MGARCH − GJR − BEKK ( C , D ) volatility model in Equation (5) to factor the role of asymmetric volatility effects between the markets using the asymmetric component (Glosten et al, 1993) in the volatility spillover model analysis for the oil prices and the Nigerian stock market return. The MGARCH − GJR − BEKK ( C , D ) has been adopted by several researchers like Okorie (2020a, 2020b), Okorie and Lin (2020c), Tule et al (2017), and others. …”

“…We, therefore, estimate the conditional mean equations and the conditional variance equation using our dataset and various estimation techniques. The mean model VAR system of equations is estimated using the Ordinary Least Squares (OLS) estimation approach in Equation (6), due to its BLUE properties while the constrained Maximum Likelihood Estimation (Okorie, 2020a, 2020b) approach is used to estimate the conditional heteroscedastic variance model in Equation (7). The error vector loss function and joint density function are specified as …”