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This paper seeks to examine the unidirectional versus bidirectional Granger causality between investors’ sentiment and momentum strategies. It is based on the full sample Granger causality test and the recent rolling-window bootstrap approach. We also applied a probit model to the extent to which the probability that investors’ sentiment and momentum strategies influence each other. Our results suggest bidirectional Granger causality between investor sentiment and momentum strategy with unstable causality dynamics over time. We find that ADS and VIX positively affect the likelihood that investor sentiment Granger causes momentum strategy and negatively impact the probability that momentum strategy Granger causes investor sentiment. Gold harms the likelihood that investors’ sentiment and momentum strategies affect each other. The research design is unique to combine bootstrap rolling-window Granger causality tests between Sentiment and Momentum to assess investors’ implications in terms of confidence, uncertainty, aggressiveness, or optimism versus Pessimism.

Oscillatory systems of interacting Hawkes processes with Erlang memory kernels were introduced by Ditlevsen and Löcherbach (Stoch. Process. Appl., 2017). They are piecewise deterministic Markov processes (PDMP) and can be approximated by a stochastic diffusion. In this paper, first, a strong error bound between the PDMP and the diffusion is proved. Second, moment bounds for the resulting diffusion are derived. Third, approximation schemes for the diffusion, based on the numerical splitting approach, are proposed. These schemes are proved to converge with mean-square order 1 and to preserve the properties of the diffusion, in particular the hypoellipticity, the ergodicity, and the moment bounds. Finally, the PDMP and the diffusion are compared through numerical experiments, where the PDMP is simulated with an adapted thinning procedure.

We propose multilayer information spillover networks, including return spillover layer, volatility spillover layer, and extreme risk spillover layer in the variance decomposition framework for comprehensively investigating the information spillovers and connectedness among 30 Chinese financial institutions (i.e., banks, securities, and insurers) during the period 2011–2018. We analyze the topological characteristics of static and dynamic multilayer networks at the system and institution levels. We find that (i) at a system level, each layer exhibits a unique network structure and spillover evolution behavior, and multilayer information spillover networks provide synthetic information on the connectedness among financial institutions, and (ii) at an individual level, institutions from different financial sectors play different roles in receiving or sending shocks based on different information spillover or contagion channels. Our findings supply valuable knowledge for investors when they assess risk and optimize asset allocation, and for regulators when they measure connectedness and prevent system risk.

This paper explores the effects of global economic policy uncertainty (GEPU) on conditional volatility in the gold futures market using Markov regime‐switching GARCH‐MIDAS models. The in‐sample empirical results suggest that GEPU indeed contains predictive information for the gold futures market, and higher GEPU leads to higher volatility within the gold futures market. Moreover, the novel model, which adds Markov regime switching with time‐varying transition probabilities and the GEPU index, achieves relatively better performance than those of the other competing models from a statistical point of view. Furthermore, we discuss the asymmetric effects of different changes in GEPU on the gold futures market and the models' performances with different horizons, and we find that our new model has better predictive performance under negative changes in GEPU than under positive changes in GEPU. Further discussion also confirms that our previous findings are robust during two special cases, the global financial crisis and European debt crisis, during which the market suffered from fierce fluctuations and was fraught with considerable uncertainty.

This paper has two aims. We first examine the dynamic spillovers between Bitcoin and 12 developed equities, gold, and crude oil for different market conditions using a Bayesian Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with daily spot prices. Our econometric approach enables us to capture the left and right tails as well as the shoulders of the return distribution corresponding to volatility spillovers under the bear, normal, and bull market states among these financial assets. We quantify and trace the dependence and directional predictability from Bitcoin to other assets using the sample cross-quantilogram. Our key findings offer convincing evidence of time variation in the level of volatility. Spillovers between Bitcoin and other financial assets intensify during extreme global market conditions. Secondly, results from the cross-quantilogram indicate strong dependence and positive directional predictability between Bitcoin and most equities and crude oil when market returns are bullish. However, during the bearish market period, there is negative dependence and predictability from Bitcoin to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market only. This implies that Bitcoin can act as a hedge to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market. However, insignificant dependence and directional predictability from Bitcoin to the remaining assets indicate that Bitcoin may act as a safe-haven to these assets during bearish markets. Our findings hold important implications for both international investors and portfolio managers who consider Bitcoin as part of their portfolio diversification and other investment strategies.

In the Dynamic Conditional Correlation with Mixed Data Sampling (DCC-MIDAS) framework, we scrutinize the correlations between the macro-financial environment and CO2 emissions in the aftermath of the COVID-19 diffusion. The main original idea is that the economy&rsquo, s lock-down will alleviate part of the greenhouse gases&rsquo, burden that human activity induces on the environment. We capture the time-varying correlations between U.S. COVID-19 confirmed cases, deaths, and recovered cases that were recorded by the Johns Hopkins Coronavirus Center, on the one hand, U.S. Total Industrial Production Index and Total Fossil Fuels CO2 emissions from the U.S. Energy Information Administration on the other hand. High-frequency data for U.S. stock markets are included with five-minute realized volatility from the Oxford-Man Institute of Quantitative Finance. The DCC-MIDAS approach indicates that COVID-19 confirmed cases and deaths negatively influence the macro-financial variables and CO2 emissions. We quantify the time-varying correlations of CO2 emissions with either COVID-19 confirmed cases or COVID-19 deaths to sharply decrease by &minus, 15% to &minus, 30%. The main takeaway is that we track correlations and reveal a recessionary outlook against the background of the pandemic.

Kenya's fiscal policy landscape is characterized by primary deficit spending forcing the government to rely on debt to meet its objectives. The justification often being that as a developing economy, annual growth rates and future prospects may in the short run justify the uptake of debt to finance infrastructural development. However, given potential fiscal limits, fiscal cycles usually alternates between sustainable and unsustainable regimes and this has a bearing on long run sustainability. This study therefore sought to investigate the nature of fiscal policy regime in Kenya and the extent to which fiscal policy is sustainable in the long run taking into account periodic regime shifts. Markov switching models were used to endogenously determine fiscal policy regimes. Regime switching tests were used to test whether No-Ponzi game condition and debt stabilizing condition were met. The results established that regime switching model was suitable in explaining regime sustainable and unsustainable cycles. An investigation of fiscal policy regimes established that both sustainable and unsustainable regimes were dominant, and each lasted for an average of four years. There was evidence to imply the existence of procyclical fiscal policy in Kenya. Regime switching tests for long run sustainability suggested that the No-Ponzi game condition weakly holds in the Kenyan economy. Regime-based sensitivity analysis indicated that persistence of unsustainability regime for more than 4 years could threaten long-run fiscal sustainability.

This paper investigates hybrid time series forecasting models, which are based on combinations of ensemble empirical mode decomposition and least squares support vector machines. Several algorithms are considered: the genetic algorithm, the grid search, and particle swarm optimization. Theoretical guarantees of prediction accuracy are tested with sine curves. From a numerical testing perspective, we are interested in showing the superiority of one approach to another based on theoretical prediction and time series applications in finance (S&P 500), commodities (WTI oil price), or cryptocurrencies (Bitcoin). The superiority of hybrid models to soft- and hard-computed models is further assessed through a ‘horse race’ and trading performance, as well as through fine-tuning of the algorithms.

Purpose In asset management, what if clients want to purchase protection from risk factors, under the form of variance risk premia. This paper aims to address this topic by developing a portfolio optimization framework based on the criterion of the minimum variance risk premium (VRP) for any investor selecting stocks with an expected target return while minimizing the risk aversion associated to the portfolio according to “good” and “bad” times. Design/methodology/approach To accomplish this portfolio selection problem, the authors compute variance risk-premium as the difference from high-frequencies' realized volatility and options' implied volatility stemming from 19 stock markets, estimate a 2-state Markov-switching model on the variance risk-premia and optimize variance risk-premia portfolios across non-overlapping regions. The period goes from March 16, 2011, to March 28, 2018. Findings The authors find that optimized portfolios based on variance-covariance matrices stemming from VRP do not consistently outperform the benchmark based on daily returns. Several robustness checks are investigated by minimizing historical, realized or implicit variances, with/without regime switching. In a boundary case, accounting for the realized variance risk factor in portfolio decisions can be seen as a promising alternative from a portfolio performance perspective. Practical implications As a new management “style”, the realized volatility approach can, therefore, bring incremental value to construct the conditional covariance matrix estimates. Originality/value The authors assess the portfolio performance determined by the variance-covariance matrices that are derived by four models: “naive” (Markowitz returns benchmark), non-switching VRP, maximum likelihood regime-switching VRP and Bayesian regime switching VRP. The authors examine the best return-risk combination through the calculation of the Sharpe ratio. They also assess another different portfolio strategy: the risk parity approach.

Energy commodities and precious metals differ from other trading products. Both oil and gold prices are leading economic variables, and drive the evolution of the world economy. Since the US dollar is used as the primary currency of international crude oil and gold trading, the relationship between commodities, metals and exchange rates has become a major research agenda recently. Therefore, this study proposes a Nested copula-based GJR-GARCH model to explore the dependence structure between oil, gold, and the USD exchange rate. More importantly, a comparative framework based on two sub-periods is implemented to capture the co-movement during normal and crisis periods. Empirical results suggest that for both crisis periods the dependence between oil, gold and the USD exchange rate is stronger compared with the dependence during the untroubled period. Moreover, the co-movement is accelerated which is explained by the unusual movement of the USD during the global financial crisis of 2007–2009.