This paper analyzes the causal relationship between renewable energy consumption, oil prices, and economic activity in the United States from July 1989 to July 2016, considering all quantiles of the ...distribution. Although the concept of Granger-causality is defined for the conditional distribution, the majority of papers have tested Granger-causality using conditional mean regression models in which the causal relations are linear. We apply a Granger-causality in quantiles analysis that evaluates causal relations in each quantile of the distribution. Under this approach, we can discriminate between causality affecting the median and the tails of the conditional distribution. We find evidence of bi-directional causality between changes in renewable energy consumption and economic growth at the lowest tail of the distribution; besides, changes in renewable energy consumption lead economic growth at the highest tail of the distribution. Our results also support unidirectional causality from fluctuations in oil prices to economic growth at the extreme quantiles of the distribution. Finally, we find evidence of lower-tail dependence from changes in oil prices to changes in renewable energy consumption. Our findings call for government policies aimed at developing renewable energy markets, to increase energy efficiency in the U.S.
•We study the causality between renewable energy, oil prices, and growth in the U.S.•We test for Granger-causality for each quantile of the distribution.•There is causality between renewable energy and economic growth at extreme tails.•Fluctuations in oil prices lead economic growth at the extreme quantiles.•Our results call for policies to develop renewable energy markets in the U.S.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This paper proposes a consistent parametric test of Granger-causality in quantiles. Although the concept of Granger-causality is defined in terms of the conditional distribution, most articles have ...tested Granger-causality using conditional mean regression models in which the causal relations are linear. Rather than focusing on a single part of the conditional distribution, we develop a test that evaluates nonlinear causalities and possible causal relations in all conditional quantiles, which provides a sufficient condition for Granger-causality when all quantiles are considered. The proposed test statistic has correct asymptotic size, is consistent against fixed alternatives, and has power against Pitman deviations from the null hypothesis. As the proposed test statistic is asymptotically nonpivotal, we tabulate critical values via a subsampling approach. We present Monte Carlo evidence and an application considering the causal relation between the gold price, the USD/GBP exchange rate, and the oil price.
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BFBNIB, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The Australian financial sector (AFS) is highly concentrated and interconnected. Besides, Australian banks' lending portfolios are dominated by residential mortgage loans, and 70% of insurance ...companies' revenues arise from non-policyholder sources. The AFS also performed relatively well during the global financial crisis (GFC). Given these distinctive features, in this paper, we examine the systemic risk contribution of Australian banks, insurance companies, and other financial services providers. We use a flexible copula-based delta conditional value-at-risk (ΔCoVaR) method across different frequencies. Further, we study the systemic risk determinants in a panel setting. We find that the major Australian banks are systemically more important than all other financial institutions. Systemic risk is typically higher after the GFC than in the pre-crisis period, despite the introduction of more stringent capital requirements. In addition, the short-term ΔCoVaR is significantly higher than the medium- and long-term ΔCoVaRs. Finally, institution-specific characteristics and market-wide variables explain the cross-sectional and time-series variation in systemic risk, and their explanatory power varies across frequencies.
•We examine the systemic risk determinants of Australia across different frequencies.•We consider banks, insurance companies, and other financial services providers.•We explain the Australian systemic risk by institution-specific characteristics.•VaR, size, liquidity, and profitability are important systemic risk determinants.•Systemic risk across frequencies depends on different sets of explanatory variables.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Banks may be reluctant to remove bad loans from their portfolios during liquidity shortfalls, giving rise to a moral hazard problem. In this paper, we analyze how liquidity shortages affect the ...ability of the interbank market to provide liquidity in a moral hazard setting. We distinguish two types of liquidity shocks that arise due to a deposit flight (a contraction in the deposit supply) or to a cash-flow shock (an increase in the non-performing loans). We show that the source of a liquidity shortfall is the main determinant of the decision of banks to stop lending in the interbank market, rather than the extra amount of funds that banks need to cover. An increase in the non-performing loans has more adverse effects on balance sheets than a deposit flight. We also demonstrate that competition has a dual effect on financial stability. Interbank competition enhances financial stability by reducing the liquidity provision cost, whereas credit market competition worsens financial stability by inducing banks to take riskier profiles.
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CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This paper proposes a practical and consistent specification test of conditional distribution models for dependent data in a general setting. Our approach covers conditional distribution models ...indexed by function-valued parameters, allowing for a wide range of useful models for risk management and forecasting, such as the quantile autoregressive model, the CAViaR model, and the distributional regression model. The new specification test (i) is valid for general linear and nonlinear conditional quantile models under dependent data, (ii) allows for dynamic misspecification of the past information set, (iii) is consistent against fixed alternatives, and (iv) has nontrivial power against Pitman deviations from the null hypothesis. As the test statistic is non-pivotal, we propose and theoretically justify a subsampling approach to obtain valid inference. Finally, we illustrate the applicability of our approach by analyzing models of the returns distribution and Value-at-Risk (VaR) of two major stock indexes.
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BFBNIB, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
In this paper, we perform a quantile regression analysis of flights-to-safety with the implied market volatilities of stock, gold, gold-mining, and silver. We verify whether flights-to-safety from US ...equities to gold are significant under different volatility conditions. We test for linear and nonlinear Granger-causality in quantiles. We find unidirectional causality running from the volatility of stock market to the market volatilities of gold, gold-mining, and silver. Besides, there is no causality between gold and silver market volatilities. We also find evidence of unidirectional causality from the market volatilities of stock, gold, and silver to the gold-mining volatility in lower- and upper-tail quantiles. Therefore, gold-mining stocks act as a good substitute for gold, coupled with negative return correlations between these two assets. Overall, our results have important implications for adopting optimal hedging and investing strategies.
•We analyze the causal nexus between implied volatilities across all quantiles.•We apply a causality-in-quantiles test that allows for nonlinear specifications.•We find causality from stock volatility to the volatilities of precious metals.•We report no evidence of volatility spillovers between gold and silver markets.•Our findings have relevant implications for developing optimal portfolio strategies.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•We apply heavy-tailed GARCH and GAS models to explain bitcoin returns and risk.•We compare the out-of-sample forecast performance of 45 models for bitcoins.•We perform three backtesting procedures ...for 1%-VaR forecasts.•Heavy-tailed GAS models have the best goodness-of-fit for bitcoin returns.•We find that heavy-tailed GAS models provides the best coverage for bitcoin risk.
This paper performs a general GARCH and GAS analysis for modelling and forecasting bitcoin returns and risk. Since Bitcoin trading exhibits excess volatility compared with other securities, it is important to model its risk and returns. We consider heavy-tailed GARCH models as well as GAS models based on the score function of the predictive conditional density of the bitcoin returns. We compare out-of-sample 1%-Value-at-Risk (VaR) forecasts under 45 different specifications using three backtesting procedures. We find that GAS models with heavy-tailed distributions provide the best out-of-sample forecast and goodness-of-fit properties to bitcoin returns and risk modelling. Normally-distributed GARCH models are always outperformed by heavy-tailed GARCH or GAS models. Besides, heavy-tailed GAS models provide the best conditional and unconditional coverage for 1%-VaR forecasts, illustrating the importance of modelling excess kurtosis for bitcoin returns. Hence, our findings have important implications for risk managers and investors for using bitcoin in optimal hedging or investment strategies.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•We investigated dynamic spillover between world markets and ASEAN-5 stock markets.•We used a multivariate AR-GARCH-DECO model and the directional spillover index.•We found positive spillover effects ...between the ASEAN-5 and world stock markets.•We identified an increase in return and volatility spillovers during the crises.•Most of the ASEAN-5 countries were net recipients during recent financial crises.
We investigate dynamic spillovers between ASEAN-5 and world stock markets using a dynamic equicorrelation (DECO) model and the spillover index of Diebold and Yilmaz (2012), which identifies net directional spillovers for each one of the markets. The DECO model uses more information to calculate dynamic correlations between each pair of returns than standard dynamic conditional correlation models, decreasing the estimation noise of the correlations. Directional spillovers from world stock markets to ASEAN-5 stock markets are higher than in the opposite direction. Besides, our results indicate heterogeneity among the ASEAN-5 stock markets in the degree of spillover to world markets over time. We verify an increase in both return and volatility spillovers during financial crises, confirming the intensity of information transmission during periods of turmoil. These findings help understand the economic channels through which the ASEAN-5 equity markets are connected, and have important implications for emerging and frontier markets.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
This paper shows that lagged information transmission between industry portfolio and market prices entails cointegration. We analyze monthly industry portfolios in the US market for the period ...1963–2015. We find cointegration between six industry portfolio and market prices. We show that the equilibrium error, the long‐term common factor between industry portfolio and market cumulative returns, has strong predictive power for excess industry portfolio returns. In line with gradual information diffusion across connected industries, the equilibrium error proxies for changes in the investment opportunity set that lead to industry return predictability by informed investors. Forecasting models including the equilibrium error have superior forecasting performance relative to models without it, illustrating the importance of cointegration between the industry portfolio and market prices. Overall, our findings have important implications for investment and risk‐management decisions, since the out‐of‐sample explanatory power of the equilibrium error is economically meaningful for making optimal portfolio allocations.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
In this study, we explore the critical demand drivers of electricity consumption in Thailand based on monthly data from 2002 to 2020. Using Autoregressive Distributed Lag (ARDL), cross-quantile ...correlation (CQC), Generalized Method of Moments (GMM), and Granger-causality-in-quantile approaches, we find that industrial production and oil production positively contribute to next month's aggregate and provincial energy consumption in Thailand, both in the short and long run. We also find that industrial production positively affects current electricity consumption, whereas electricity prices negatively affect current electricity consumption. Oil production, however, has no effect on current electricity consumption. Moreover, the CQC analysis finds evidence of cross-predictability running from industrial production and electricity prices to next month's electricity consumption at the extreme and median quantiles of the distribution. Further, industrial production, electricity prices, and oil production Granger-cause energy consumption at the extreme and median quantiles of the distribution. Nevertheless, we show that the Thai government's energy policies are ineffective for reducing electricity consumption. Our findings have crucial policy implications for the electricity market efficient allocation and its reform.
•We explore the critical demand drivers of electricity consumption in Thailand.•Industrial and oil production positively contribute to next month's aggregate and provincial energy consumption.•Current oil production has no effect on current electricity consumption.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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