Διδακτορικές διατριβές
Μόνιμο URI για αυτήν τη συλλογήhttps://pyxida.aueb.gr/handle/123456789/14
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Πλοήγηση Διδακτορικές διατριβές ανά Συγγραφέα "Bisiotis, Konstantinos"
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Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ Ψ Ω
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Τεκμήριο Affine models: change point detection and applications(12/17/2021) Bisiotis, Konstantinos; Μπισιώτης, Κωνσταντίνος; Athens University of Economics and Business, Department of Statistics; Yannacopoulos, Athanasios; Tzavalis, Elias; Vrontos, Ioannis; Tsekrekos, Andrianos; Weber, Gerhard-Wilhelm; Moguerza, Javier M.; Psarakis, SteliosThe purpose of the present thesis is the application of statistical process control (SPC) techniques, specifically control charts in Gaussian affine term structure models (ATSM). In recent years SPC methods have been widely in several non-industrial scientific areas such as in finance. Gaussian ATSMs under no-arbitrage conditions have been a very important research tool in the area of term structure of interest rates.In our work we propose several control chart procedures that develop the ATSMs from the change point perspective. First, we construct control chart procedures for monitoring the parameters of an ATSM and examine their ability of detecting changes for various types of shifts in the yield curve. Also, we propose a technique for reestimating the target process of the control chart procedure in case of a detection of a change. The results show that there is no single chart that performs well in all types of shifts but a combination of control chart is needed. Second, we extent the class of term structure models estimated using the minimum chi-square estimation (MCSE) method by constructing fixed-income government bond portfolios. The proposed bond portfolio strategies from the ATSM in most of the cases perform better than traditional bond portfolio strategies. Next, the control charts are applied for monitoring the optimal portfolio weights. Third, we propose and construct control charts for monitoring shifts in the autoregressive and moving average matrix of a VARMA ATSM. In the estimation procedure of the model in the first step, among standard estimation procedures, we apply a minimum distance estimation method based on the impulse responses and define its advantages in the forecasting of the yield curve. In the second step, we estimate the market prices of risk.