Update slides

index.qmd

@@ -1041,39 +1041,24 @@ chart = {
 
 ###  
 
-:::: {.columns}
-
-::: {.column width="48%"}
-
 Each day, $t = 1, 2, ... T$
 
 - The **forecaster** receives predictions $\widehat{X}_{t,k}$ from $K$ **experts**
 - The **forecaster** assigns weights $w_{t,k}$ to each **expert**
-- The **forecaster** calculates her prediction:
+- The **forecaster** calculates the prediction:
+
 \begin{equation}
     \widetilde{X}_{t} = \sum_{k=1}^K w_{t,k} \widehat{X}_{t,k}.
     \label{eq_forecast_def}
 \end{equation}
+
 - The realization for $t$ is observed
 
-:::
+<i class="fa fa-fw fa-arrow-right" style="color:var(--col_grey_9);"></i> The experts can be institutions, persons, or models
 
-::: {.column width="4%"}
+<i class="fa fa-fw fa-arrow-right" style="color:var(--col_grey_9);"></i> The forecasts can be point-forecasts (i.e., mean or median) or full predictive distributions
 
-:::
-
-::: {.column width="48%"}
-
-- The experts can be institutions, persons, or models
-- The forecasts can be point-forecasts (i.e., mean or median) or full predictive distributions
-- We do not need a distributional assumption concerning the underlying data
-- @cesa2006prediction
-
-:::
-
-::::
-
----
+<i class="fa fa-fw fa-arrow-right" style="color:var(--col_grey_9);"></i> @cesa2006prediction
 
 ## The Regret
 
@@ -3005,8 +2990,7 @@ Berrisch, J., Pappert, S., Ziel, F., & Arsova, A. (2023). *Finance Research Lett
 
 ### Motivation
 
-Understanding European Allowances (EUA) dynamics is important
-for several fields:
+Understanding European Emission Allowances (EUA)
 
 <i class="fa fa-fw fa-chart-pie" style="color:var(--col_grey_9);"></i> Portfolio & Risk Management,
 
@@ -3179,7 +3163,8 @@ $$\mathbf{F} = (F_1, \ldots, F_K)^{\intercal}$$
 
 Generalized non-central t-distributions
 
-- Time varying: expectation $\boldsymbol{\mu}_t = (\mu_{1,t}, \ldots, \mu_{K,t})^{\intercal}$
+- Time varying: 
+  - expectation $\boldsymbol{\mu}_t = (\mu_{1,t}, \ldots, \mu_{K,t})^{\intercal}$
   - variance: $\boldsymbol{\sigma}_{t}^2 = (\sigma_{1,t}^2, \ldots, \sigma_{K,t}^2)^{\intercal}$
 - Time invariant 
   - degrees of freedom: $\boldsymbol{\nu} = (\nu_1, \ldots, \nu_K)^{\intercal}$