nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒09‒18
two papers chosen by
Jaqueson K. Galimberti, Asian Development Bank

  1. Investigating Volatility Transmissions among Sovereign Bonds in African and Emerging Markets Using Multivariate GARCH Models By Debalke, Negash Mulatu
  2. REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market By Paweł Jakubowski; Robert Ślepaczuk; Franciszek Windorbski

  1. By: Debalke, Negash Mulatu
    Abstract: The study examined volatility transmissions between Ethiopia and Ghana's sovereign bonds and emerging markets. Five Multivariate GARCH models were estimated using time series price indices. AIC and BIC criteria identified the VCC-MGARCH model as the best. The result shows own-volatility spillovers are higher than cross-volatility spillovers. In addition, it confirms cross-spillovers were unidirectional, from emerging markets to Ethiopia, with no significant spillover to Ghana. There is no bidirectional volatility spillover. Both Ethiopia and Ghana exhibit significant ARCH and GARCH effects, emphasizing the importance of addressing past variations and squared returns in volatility management. Significant adjustment parameters suggest that deviations from long-term equilibrium are corrected, indicating the markets’ stability mechanisms. Thus, policymakers should monitor these mechanisms for market stability. Finally, policy implications emphasize monitoring and managing external influences, addressing market dynamics persistence, and implementing policies to reduce excessive volatility.
    Keywords: Sovereign Bond; Ethiopia; Ghana; Africa; Emerging Markets; Return; Volatility; Spillover; M-GARCH.
    JEL: E58 E63 F65
    Date: 2023–08–22
  2. By: Paweł Jakubowski (University of Warsaw, Faculty of Economic Sciences); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance); Franciszek Windorbski (University of Warsaw, Faculty of Management)
    Abstract: This paper presents the results of investment strategies based on predictions from an ARIMA with exogenous variables (ARIMAX/ARIMAX-Garch) model, using the prices of selected commodities and companies from the DJIA index as explanatory variables. The explained variables are four Invesco ETF funds (DBE, DBA, DBP, DBB) corresponding to baskets of energy, agricultural, precious, and industrial metals. The models are optimized using the Walk-Forward technique, and the selection of exogenous variables is based on Granger causality tests. By analyzing the results, we conclude that ARIMAX/ARIMAX-Garch models are not useful tools for making buy or sell decisions for the selected commodity baskets. Out of the 80 estimated models, 44 outperform the Buy & Hold strategy, however, none achieved statistically significant results. Combining individual models into an investment portfolio reduced the risk without significantly reducing the profit, enabling us to consistently beat the benchmark. We also observe that using returns of commodities listed on stock exchanges is more effective than using stock returns. Sensitivity analysis shows instability in results with changes in the length of the training and testing windows. The highest annual return rate of 15.37% from 02.01.2008 to 01.12.2022 was characterized by an ARIMAX model with one commodity exogenous variable.
    Keywords: ARIMA(X), GARCH, ARIMA(X)/GARCH, Algorithmic Investment Strategies, Granger Causality, Investment Performance Evaluation, Trading Systems, Forecasting Models
    JEL: C4 C14 C45 C53 C58 G11 G13 G15
    Date: 2023

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