Rm voldep conclusions slide
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index.qmd
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index.qmd
@@ -3763,38 +3763,6 @@ plot_quant_data %>% ggplot(aes(x = date, y = value)) +
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## Conclusion
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:::: {.columns}
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::: {.column width="48%"}
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Accounting for heteroscedasticity or stabilizing the variance via log transformation is crucial for good performance in terms of ES
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- Price dynamics emerged way before the Russian invasion into Ukraine
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- Linear dependence between the series reacted only right after the invasion
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- Improvements in forecasting performance is mainly attributed to:
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- the tails
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- the dependence structure between the marginals
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:::
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::: {.column width="4%"}
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:::
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::: {.column width="48%"}
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<center>
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<img src="assets/voldep/frame.svg" width="250">
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</center>
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<i class="fa fa-fw fa-newspaper" style="color:var(--col_grey_9);"></i> Berrisch, J., Pappert, S., Ziel, F., & Arsova, A. [-@berrisch2023modeling]. Modeling volatility and dependence of European carbon and energy prices. <em>Finance Research Letters</em>, 52, 103503.
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:::
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::::
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# Final Remarks {visibility="uncounted"}
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## Contributions {#sec-contributions}
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