Quantifying the hedge and safe-haven properties of bond markets for cryptocurrency indices

PurposeThis study quantified the hedge and safe haven features of bond markets for multiple cryptocurrency indices from June 2014 to April 2021 to highlight whether bond markets offer hedging facilities to uncertainty indices of cryptocurrencies.Design/methodology/approachThe authors employed the methodology of Baur and McDermott (2010) and AGDCC-GARCH model to measure the hedge and safe-haven characteristics of three bond markets (BBGT, SPGB and SKUK) for three uncertainty indexes of cryptocurrencies (UCRPR, UCRPO and ICEA).FindingsThe authors find that bond markets are neither hedge nor safe havens except for SKUK which is a safe haven investment for cryptocurrency indices and offers substantial diversification during the periods of economic fragility. In addition, the hedge effectiveness of SPGB outperforms other bonds during crisis periods and provides sufficient diversification potential for cryptocurrency indices.Practical implicationsThe findings are important for policymakers, regulatory bodies, financial firms and investors in assessing hedge and safe haven characteristics of bond markets against cryptocurrency indices.Originality/valueEmploying the novel methodology of AGDCC-GARCH with three different bond markets and three uncertainty indices of cryptocurrencies, the current study adds to the existing strand of literature in terms of quantifying hedge and safe-haven attributes of bond markets for cryptocurrency uncertainty indexes.

High-Dimensional Conditional Covariance Matrices Estimation Using a Factor-GARCH Model

Estimation of a conditional covariance matrix is an interesting and important research topic in statistics and econometrics. However, modelling ultra-high dimensional dynamic (conditional) covariance structures is known to suffer from the curse of dimensionality or the problem of singularity. To partially solve this problem, this paper establishes a model by combining the ideas of a factor model and a symmetric GARCH model to describe the dynamics of a high-dimensional conditional covariance matrix. Quasi maximum likelihood estimation (QMLE) and least square estimation (LSE) methods are used to estimate the parameters in the model, and the plug-in method is introduced to obtain the estimation of conditional covariance matrix. Asymptotic properties are established for the proposed method, and simulation studies are given to demonstrate its performance. A financial application is presented to support the methodology.

A Hybrid Space-Time Modelling Approach for Forecasting Monthly Temperature

Spatio-temporal forecasting has various applications in climate, transportation, geo-statistics, sociology, economics and in many other fields of study. The modelling of temperature and it forecasting is a challenging task due to spatial dependency of time series data and nonlinear in nature. To address these challenges, in this study we proposed hybrid Space-Time Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscadicity (STARMA-GARCH) model in order to describe and identify the behaviour of monthly maximum temperature and temperature range in Bihar. At the modelling process of STARMA, spatial characteristics are incorporated into the model using a weight matrix based on great circle distance between the regions. The residuals from the fitted STARMA model have been tested by Brock, Dechert, and Scheinkman (BDS) and Autoregressive Conditional Heteroscadicity-Lagrange Multiplier (ARCH-LM) test for the behaviour of nonlinearity and ARCH effect respectively. The test results revealed that presence of both nonlinearity and ARCH effect. Hence GARCH modelling is necessary. Therefore, the hybrid STARMA-GARCH model is used to capture the dynamics of monthly maximum temperature and temperature range. The results of the proposed hybrid STARMA (1 1 , 0, 0)−GARCH (0, 1) model has better modelling efficiency and forecasting precision over STARMA (1 1 ,0, 0) model.

Return and volatility spillovers between South African and Nigerian equity markets

PurposeThe paper evaluates the cross-transmission of returns and volatility shocks between Nigeria and South Africa stock markets to infer the extent of interdependence between the two markets. The paper also makes inference to optimal portfolio weights of holding assets in the two markets.Design/methodology/approachThe paper uses an asymmetric vector autoregressive-exogenous generalised autoregressive conditional heteroscedasticity (VAR-X GARCH) model to assess the extent of returns and volatility spillovers between Nigeria and South Africa.FindingsThe results of the empirical analysis show evidence of shock spillovers from the South African stock market to the Nigerian stock market. Moreover, based on the dynamic Sharpe ratio and portfolio weight optimisation, the results indicate the possibility of portfolio diversification when holding simultaneous positions in the two stock markets.Practical implicationsThe results imply the possibility of economic profit for investors who take positions in the two stock markets. The lack of synchronisation of stock markets in the two largest economies in Africa is in contrast with the situations in other regions where stock markets returns of large economies often co-move.Originality/valueThe paper is the first to use the asymmetric VAR-X GARCH model to assess the cross-transmission of shocks between stock markets.

Multivariate Hyper-Rotated GARCH-BEKK

For large multivariate models of generalized autoregressive conditional heteroskedasticity (GARCH), it is important to reduce the number of parameters to cope with the ‘curse of dimensionality’. Recently, Laurent, Rombouts and Violante (2014 “Multivariate Rotated ARCH Models” Journal of Econometrics 179: 16–30) developed the rotated multivariate GARCH model, which focuses on the parameters for standardized variables. This paper extends the rotated multivariate GARCH model by considering a hyper-rotation, which uses a more flexible structure for the rotation matrix. The paper shows an alternative representation based on a random coefficient vector autoregressive and moving-average (VARMA) process, and provides the regularity conditions for the consistency and asymptotic normality of the quasi-maximum likelihood (QML) estimator for VARMA with hyper-rotated multivariate GARCH. The paper investigates the finite sample properties of the QML estimator for the new model. Empirical results for four exchange rate returns show the new specifications works satisfactory for reducing the number of parameters.

The impact of world oil and food price shocks on the interdependence of Brazil and Russia: SVAR-DCC-GARCH model

This study seeks to identify the extent to which global oil and food price volatilities affected the interdependence of the Brazilian and Russian economies in the period from 1996 to 2021. The ARCH/GARCH framework was used to model the volatility of oil and food prices. The Structural Vector Autoregressive (SVAR) approach was used to ascertain the sensitivity of key economic indicators to oil and food shocks. The Impulse Response Function (IRF) was used to trace short-term effects over a period of 12 months. Subsequently, the multivariate dynamic conditional correlation DCC-GARCH model, created by Engle & Sheppard (2001), was used to model time-varying correlations of paired macroeconomic variables. This study contributes to the empirical literature in two fundamental ways. Firstly, it pairs the two largest oil and food producers in the BRICS bloc. Secondly, unlike some earlier studies, the applied methodology ensures the effectiveness of the results by using stationary time series data. The results show that Brazil and Russia have long-run spillover effects for all macroeconomic variables in response to both oil and food price shocks. Furthermore, money supply and exchange rate variables exhibited declining positive correlation coefficients during the global financial crisis of 2008–2009, but peaked in early 2020 due to the Covid-19 pandemic. As a corollary of the main findings, the researchers recommend that investors should diversify their portfolios beyond these two economies in order to minimize the risk of contagion during severe global crises.

Adoption of deep learning Markov model combined with copula function in portfolio risk measurement

In order to accurately describe the risk dependence structure and correlation between financial variables, carry out scientific financial risk assessment, and provide the basis for accurate financial decision-making, first the basic theory of Copula function is established and the mixed Copula model is constructed. Then the hybrid Copula model is nested in a hidden Markov model (HMM), the risk dependences among banking, insurance, securities and trust industries are analysed, and the Copula–Garch model is constructed for empirical analysis of investment portfolio. Finally, the deep learning Markov model is adopted to predict the financial index. The results show that the mixed Copula model based on HMM is more effective than the single Copula and the mixed Copula models. The empirical structure shows that among the four major financial industries in China, the banking and insurance industries have strong interdependence and high probability of risk contagion. The investment failure rate under 95%, 97.5% and 99% confidence intervals calculated by Copula–Garch model are 4.53%, 2.17% and 1.08%, respectively. Moreover, the errors of deep learning Markov model in stock price prediction of Shanghai Pudong Development Bank (sh600000), Guizhou Moutai (sh600519) and China Ping An Insurance (sh601318) are 2.56%, 2.98% and 3.56% respectively, which indicates that the four major financial industries in China have strong interdependence and risk contagion, so that the macro or systemic risks may arise, and the deep-learning Markov model can be adopted to predict the stock prices.

Liquidity effects on oil volatility forecasting: From fintech perspective

Fin-tech is an emerging field, inspiring revolutionary innovations in the financial field. It may initiate the evolutionary episode of the financial research, where volatility forecasting is a crucial topic in finance. For forecasting volatility, GARCH model is a prevailing model, however, further improvement of the GARCH model is still challenging. In this paper, we demonstrate how Fintech can play a part in volatility forecasting by employing a metaheuristic procedure called Genetic Programming. On the basis, we are able to develop a new volatility forecasting model, which can beat GARCH family models (including GARCH, IGARCH and TGARCH models) in a significant way. Since genetic programming is an evolutionary algorithm based on the principles of natural selection, this innovative work will be a breakthrough point in the financial area. The innovation of this paper demonstrates how GP technology can be applied in the financial field, attempting to explore the volatility forecasting area from the combination of new technology and finance, known as fintech. More importantly, when the formula of volatility forecasting is unknown as we introduce a new factor, namely, the liquidity factor, we unveil that how GP method can be helpful in determining the specific volatility forecasting model format. We thereby exhibit the liquidity effects on volatility forecasting filed from the fintech perspective.