Commodity futures hedging, risk aversion and the hedging horizon

Conlon, Thomas; Cotter, John; Gençay, Ramazan

doi:10.1080/1351847x.2015.1031912

Cited by 54 publications

(28 citation statements)

References 62 publications

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“…Baruník and Křehlík (2018) argue that shocks to economic activity impact variables at various frequencies with various strengths, and to understand the sources of connectedness in an economic system, it is crucial to understand the frequency dynamics of connectedness. The key reason is that agents operate on different investment horizons -these are associated with various types of investors, trading tools, and strategies that correspond to different trading frequencies (Gençay et al, 2010;Conlon et al, 2016). Shorter or longer frequencies are the result of the frequency-dependent formation of investors' preferences, as shown in the modeling strategies of Bandi and Tamoni (2017); Cogley (2001); Ortu et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets

Baruník¹,

Kočenda²

2019

The Energy Journal

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We analyze total, asymmetric and frequency connectedness between oil and forex markets using high-frequency, intra-day data over the period 2007 -2017. By employing variance decompositions and their spectral representation in combination with realized semivariances to account for asymmetric and frequency connectedness, we obtain interesting results. We show that divergence in monetary policy regimes affects forex volatility spillovers but that adding oil to a forex portfolio decreases the total connectedness of the mixed portfolio. Asymmetries in connectedness are relatively small. While negative shocks dominate forex volatility connectedness, positive shocks prevail when oil and forex markets are assessed jointly. Frequency connectedness is largely driven by uncertainty shocks and to a lesser extent by liquidity shocks, which impact long-term connectedness the most and lead to its dramatic increase during periods of distress.

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Section: Introductionmentioning

confidence: 99%

Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets

Baruník¹,

Kočenda²

2019

The Energy Journal

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“…Hence, WA is ideal for studying the multihorizon properties of time series as they can be used to decompose a signal into different time horizon or frequency components. Furthermore, the wavelet approach overcomes the data reduction problem generally found for low-frequency data, capturing information associated with all available data (Conlon, Cotter, & Gençay, 2016). Ramsey (1999) contends that WA has the ability to represent highly complex structures without knowing the underlying functional form, which is of great benefit in economic and financial research.…”

Section: Introductionmentioning

confidence: 99%

Global financial crisis and multiscale systematic risk: Evidence from selected European stock markets

Alexandridis

Hasan

2019

Int. J Fin Econ

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In this paper, we have investigated the impact of the global financial crisis on the multihorizon nature of systematic risk and market risk using daily data of eight major European equity markets over the period of 2005-2018. The method is based on a wavelet multiscale approach within the framework of a capital asset pricing model. Empirical results demonstrate that beta coefficients have a multiscale tendency, and betas tend to increase at higher scales (lower frequencies). In addition, the size of betas and R 2 s tends to increase during the crisis period compared with the precrisis period. The multiscale nature of the betas is consistent with the fact that stock market investors have different time horizons due to different trading strategies. Our results based on scale dependent value at risk (VaR) suggest that market risk tends to be more concentrated at lower time scales (higher frequencies) of the data. Moreover, the scale-by-scale estimates of VaR have increased almost three folds for every market during the crisis period compared with the precrisis period. Finally, our approach allows for accurately forecasting time-dependent betas and VaR.

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“…1 However, a majority of the empirical analyses that investigate dynamic co-movements employ a time-domain approach that is limited to dynamic links while the frequency analysis of investment horizons is omitted (Ramsey, 2002). Yet, dynamic correlations among assets have been documented to have specific characteristics for particular investment horizons (Conlon et al, 2012), which may be instructive both for policy-makers (financial stability measures) and market participants (predictions of price changes). To better understand the co-movements in asset prices a combined time and frequency analysis is needed.…”

Section: Introductionmentioning

confidence: 99%

Gold, oil, and stocks: Dynamic correlations

Baruník

Kočenda

Vácha

2016

International Review of Economics & Finance

120

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investment horizons between pairs of assets is a dominant feature during times of economic downturn and financial turbulence for all three pairs of the assets under research. Heterogeneity prevails in correlations between gold and stocks. After the 2008 crisis, correlations among all three assets increase and become homogenous: the timing differs for the three pairs but coincides with the structural breaks that are identified in specific correlation dynamics. A strong implication emerges: during the period under research, and from a different-investment-horizons perspective, all three assets could be used in a well-diversified portfolio only during relatively short periods. Terms of use: Documents inJEL-Code: C010, C130, C580, F370, G110, G150.

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Commodity futures hedging, risk aversion and the hedging horizon

Cited by 54 publications

References 62 publications

Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets

Total, Asymmetric and Frequency Connectedness between Oil and Forex Markets

Global financial crisis and multiscale systematic risk: Evidence from selected European stock markets

Gold, oil, and stocks: Dynamic correlations

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