Garch
Time series
Stochastic volatility models
Value at risk
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| Συλλογές | |
| Τίτλος |
Backtesting value at risk models |
| Δημιουργός |
Λυκούδης, Παναγιώτης, Lykoudis, Panagiotis |
| Συντελεστής |
Οικονομικό Πανεπιστήμιο Αθηνών, Τμήμα Πληροφορικής |
| Τύπος |
Text |
| Φυσική περιγραφή |
56σ. |
| Γλώσσα |
en |
| Αναγνωριστικό |
http://www.pyxida.aueb.gr/index.php?op=view_object&object_id=5269 |
| Περίληψη |
This thesis investigates the concept of risk and how to quantify this notion with measures such as the Value-at-Risk (VaR). This method provides the concerned party with a figure that expresses the maximum expected loss on an investment for a given period and a given level of confidence. The calculation of VaR as before is done through a complex econometric approach, through out various models such as the conditional Autoregressive Heteroscedasticity model, known as Arch or the generalized autoregressive conditional heteroskedastic model known as Garch models. From this point and after we are going to refer to the above models only with their initials, presented next to each one of them in parentheses. In this process VaR or any other risk measure arises as a forecast of the period under the implementation process. In other words we will quantify the notion of risk using and comparing different methods as have been proposed by the literature through out the years. As dataset, we used 4 indices with the majority of them listed in the USA’s stock exchanges and the others listed to the Europe’s stock exchanges. More specifically, in our research we used time series returns by the indices: FTSE 100, CAC 40, Nikkei 224 and Nasdaq. |
| Λέξη κλειδί |
Arch Garch Time series Stochastic volatility models Value at risk |
| Διαθέσιμο από |
2017-02-13 22:00:42 |
| Ημερομηνία έκδοσης |
11/30/2016 |
| Ημερομηνία κατάθεσης |
2017-02-13 22:00:42 |
| Δικαιώματα χρήσης |
Free access |
| Άδεια χρήσης |
https://creativecommons.org/licenses/by/4.0/ |