The Prime Ministry of Undersecretariat of Treasury maintaining the financial administration of Republic of Turkey has several tasks to handle one of which is to manage the government&rsquo / s debt in a way that minimizes the cost regarding risk. Choosing the right instrument and maturity composition that has the least cost and risk is the debt management problem to be dealt with and is affected by many stochastic factors. The objective of this thesis is the optimization of the debt management problem of the Turkish Government via a stochastic simulation framework under the constraints of changes in portfolio positions. Value-at-Risk of the optimal portfolio is calculated to measure market risk. Macroeconomic variables in the optimization problem are modeled with econometric models like autoregressive processes (AR), autoregressive integrated moving average processes (ARIMA) and generalized autoregressive conditionally heteroscedastic (GARCH) processes. The simulation horizon is 2005-2015. Debt portfolio is optimized at 2006 and 2015 where the representative scenarios for the optimization are found by clustering the previously generated 25,000 scenarios into 30 groups at each stage.

Portfolio selection has a long tradition in financial economics and plays an integral role in investment management. Portfolio selection provides the framework to determine optimal portfolio choice from a universe of available investments. However, the asset weightings from portfolio selection are optimal only if the empirical characteristics of asset returns do not violate the portfolio selection model assumptions. This thesis explores the empirical characteristics of traditional assets and hedge fund returns and examines their effects on the assumptions of linearity-in-the-mean testing and portfolio selection. The encompassing theme of this thesis is the empirical interplay between traditional assets and hedge fund returns. Despite the paucity of hedge fund research, pension funds continue to increase their portfolio allocations to global hedge funds in an effort to pursue higher risk-adjusted returns. This thesis presents three empirical studies which provide positive insights into the relationships between traditional assets and hedge fund returns. The first two empirical studies examine an emerging body of literature which suggests that the relationship between traditional assets and hedge fund returns is non-linear. For mean-variance investors, non-linear asset returns are problematic as they do not satisfy the assumption of linearity required for the covariance matrix in portfolio selection. To examine the linearity assumption as it relates to a mean-variance investor, a hypothesis test approach is employed which investigates the linearity-in-the-mean of traditional assets and hedge funds. The findings from the first two empirical studies reveal that conventional linearity-in-the-mean tests incorrectly conclude that asset returns are nonlinear. We demonstrate that the empirical characteristics of heteroscedasticity and autocorrelation in asset returns are the primary sources of test mis-specification in these linearity-in-the-mean hypothesis tests. To address this problem, an innovative approach is proposed to control heteroscedasticity and autocorrelation in the underlying tests and it is shown that traditional assets and hedge funds are indeed linear-in-the-mean. The third and final study of this thesis explores traditional assets and hedge funds in a portfolio selection framework. Following the theme of the previous two studies, the effects of heteroscedasticity and autocorrelation are examined in the portfolio selection context. The characteristics of serial correlation in bond and hedge fund returns are shown to cause a downward bias in the second sample moment. This thesis proposes two methods to control for this effect and it is shown that autocorrelation induces an overallocation to bonds and hedge funds. Whilst heteroscedasticity cannot be directly examined in portfolio selection, empirical evidence suggests that heteroscedastic events (such as those that occurred in August 1998) translate into the empirical feature known as tail-risk. The effects of tail-risk are examined by comparing the portfolio decisions of mean-variance analysis (MVA) versus mean-conditional value at risk (M-CVaR) investors. The findings reveal that the volatility of returns in a MVA portfolio decreases when hedge funds are included in the investment opportunity set. However, the reduction in the volatility of portfolio returns comes at a cost of undesirable third and fourth moments. Furthermore, it is shown that investors with M-CVaR preferences exhibit a decreasing demand for hedge funds as their aversion for tail-risk increases. The results of the thesis highlight the sensitivities of linearity tests and portfolio selection to the empirical features of heteroscedasticity, autocorrelation and tail-risk. This thesis contributes to the literature by providing refinements to these frameworks which allow improved inferences to be made when hedge funds are examined in linearity and portfolio selection settings.

The aim of this thesis is to improve risk measurement estimation by incorporating extra information in the form of constraint into completely non-parametric smoothing techniques. A similar approach has been applied in empirical likelihood analysis. The method of constraints incorporates bootstrap resampling techniques, in particular, biased bootstrap. This thesis brings together formal estimation methods, empirical information use, and computationally intensive methods. In this thesis, the constraint approach is applied to non-parametric smoothing estimators to improve the estimation or modelling of risk measures. We consider estimation of Value-at-Risk, of intraday volatility for market risk, and of recovery rate densities for credit risk management. Firstly, we study Value-at-Risk (VaR) and Expected Shortfall (ES) estimation. VaR and ES estimation are strongly related to quantile estimation. Hence, tail estimation is of interest in its own right. We employ constrained and unconstrained kernel density estimators to estimate tail distributions, and we estimate quantiles from the fitted tail distribution. The constrained kernel density estimator is an application of the biased bootstrap technique proposed by Hall & Presnell (1998). The estimator that we use for the constrained kernel estimator is the Harrell-Davis (H-D) quantile estimator. We calibrate the performance of the constrained and unconstrained kernel density estimators by estimating tail densities based on samples from Normal and Student-t distributions. We find a significant improvement in fitting heavy tail distributions using the constrained kernel estimator, when used in conjunction with the H-D quantile estimator. We also present an empirical study demonstrating VaR and ES calculation. A credit event in financial markets is defined as the event that a party fails to pay an obligation to another, and credit risk is defined as the measure of uncertainty of such events. Recovery rate, in the credit risk context, is the rate of recuperation when a credit event occurs. It is defined as Recovery rate = 1 - LGD, where LGD is the rate of loss given default. From this point of view, the recovery rate is a key element both for credit risk management and for pricing credit derivatives. Only the credit risk management is considered in this thesis. To avoid strong assumptions about the form of the recovery rate density in current approaches, we propose a non-parametric technique incorporating a mode constraint, with the adjusted Beta kernel employed to estimate the recovery density function. An encouraging result for the constrained Beta kernel estimator is illustrated by a large number of simulations, as genuine data are very confidential and difficult to obtain. Modelling high frequency data is a popular topic in contemporary finance. The intraday volatility patterns of standard indices and market-traded assets have been well documented in the literature. They show that the volatility patterns reflect the different characteristics of different stock markets, such as double U-shaped volatility pattern reported in the Hang Seng Index (HSI). We aim to capture this intraday volatility pattern using a non-parametric regression model. In particular, we propose a constrained function approximation technique to formally test the structure of the pattern and to approximate the location of the anti-mode of the U-shape. We illustrate this methodology on the HSI as an empirical example.

We utilise several asset pricing models that allow for discontinuities in the returns and volatility time series in order to obtain estimates of Value-at-Risk (VaR). The first class of model that we use mixes a continuous diffusion process with discrete jumps at random points in time (Poisson Jump Diffusion Model). We also apply a purely discontinuous model that does not contain any continuous component at all in the underlying distribution (Variance Gamma Model). These models have been shown to have some success in capturing certain characteristics of return distributions, a few being leptokurtosis and skewness. Calibrating these models onto the returns of an index of Australian stocks (All Ordinaries Index), we then use the resulting parameters to obtain daily estimates of VaR. In order to obtain the VaR estimates for the Poisson Jump Diffusion Model and the Variance Gamma Model, we introduce the use of an innovation from option pricing techniques, which concentrates on the more tractable characteristic functions of the models. Having then obtained a series of VaR estimates, we then apply a variety of criteria to assess how each model performs and also evaluate these models against the traditional approaches to calculating VaR, such as that suggested by J.P. Morgan???s RiskMetrics. Our results show that whilst the Poisson Jump Diffusion model proved the most accurate at the 95% VaR level, neither the Poisson Jump Diffusion or Variance Gamma models were dominant in the other performance criteria examined. Overall, no model was clearly superior according to all the performance criteria analysed, and it seems that the extra computational time required to calibrate the Poisson Jump Diffusion and Variance Gamma models for the purposes of VaR estimation do not provide sufficient reward for the additional effort than that currently employed by Riskmetrics.

Zugl.: Hagen, FernUniversity, Diss., 2006.

Univ., Diss.--Zürich, 2007. / ALM = Asset- und Liability Management. - CVaR = Conditional Value-at-Risk.

Universiẗat, Diss.--Köln, 1998. / Literaturverz. S. 185-197.

Zugl.: Marburg, Universiẗat, Diss., 2008.

<p>Value at Risk (VaR) approach has been extensively used by investment and commercial banks since its development by JP Morgan in 1990s. As time passes, it has become interesting to investigate whether VaR could be used also by other financial intermediaries like pension funds and insurance companies. The aim of this paper is to outline Value at Risk (VaR) methodology by giving more emphasis on parametric approach which is used for empirical section and to investigate the applicability and usefulness of VaR in pension funds. After providing theoretical framework for VaR approach, the paper continues with pension fund systems in general and especially highlights AP funds of Swedish National pension fund system by trying to show why VaR could be an invaluable risk management tool for these funds together with other traditional risk measures used. Based on this given theoretical frame, a practical application of VaR –parametric or covariance/variance method- is executed on 50 biggest investments in the fixed income and equity portfolios of three selected Swedish national pension funds – AP1, AP2 and AP3. Results of one day VaR (DEAR) estimations on 30/12/2005 for each fund have been presented and it is aimed to show the additional information that could be obtained by using VaR and which is not always apparent from other risk measures employed by funds. According to the two traditional risk measures which are active risk and Sharpe ratio; AP2 and AP3 lie in the same risk level for 2005 which can create a contradiction by considering their different returns. On the other hand, obtained DEAR estimates show their different risk exposures even with the 50 biggest investments employed. The results give a matching relationship between return of funds and DEAR estimates meaning that; the fund with the highest return has the highest DEAR value and the fund with the lowest return has the lowest DEAR value; which is consistent with the main rule- “higher risk, higher return”. Thus, we can conclude that VaR could be applied additionally to get a better picture about real risk exposures and also to get valuable information on expected possible loss together with other traditional risk measures used.</p><p>Key words: Value at Risk, DEAR, Pension funds, Risk management, Swedish pension plan, AP1, AP2, AP3</p>

Το ζητούμενο σε κάθε επιχείρηση είναι η αντιμετώπιση καταστάσεων οι οποίες μπορεί να παρουσιάσουν αυξημένη πιθανότητα απωλειών. Για να επιτευχθεί ο συγκεκριμένος στόχος είναι αναγκαίος ο εντοπισμός και ο καθορισμός της σημαντικότητας των επικείμενων κινδύνων. Αυτούς τους κινδύνους μπορούμε να τους κατατάξουμε σε επιχειρησιακούς, μη επιχειρησιακούς και χρηματοοικονομικούς. Η διαχείριση του κινδύνου δεν έχει ως πρώτο σκοπό την αποφυγή του κινδύνου, αλλά την ελαχιστοποίησή του, αφού πρώτα εντοπιστεί και καθοριστεί το πόσο σημαντικός είναι. Στόχος είναι να ποσοτικοποιηθεί ο κίνδυνος και να υπολογίζεται ένα μέτρο συνολικού κινδύνου, έτσι ώστε δίνοντας μια τιμή σε αυτόν, να αποφασίσουμε αν θα πάρουμε το ρίσκο να τον αναλάβουμε ή όχι, με μεγαλύτερη ευκολία. Ένα μέτρο συνολικού κινδύνου, προκύπτει από την προσέγγιση της δυνητικής ζημίας {VAR(Value−At−Risk)}. Η προσέγγιση αυτή αποτελεί μια ποσοστιαία κατανομή κέρδους και απώλειας σε ένα συγκεκριμένο χρονικό διάστημα. Μπορεί να χρησιμοποιηθεί από οποιοδήποτε οργανισμό εκτίθεται σε χρηματοοικονομικό κίνδυνο και συνοψίζει τη χειρότερη ζημία με δεδομένο διάστημα εμπιστοσύνης. Σκοπός της παρούσας εργασίας είναι η περιγραφή του τρόπου λειτουργίας της προσέγγισης της δυνητικής ζημίας (VAR). Για την αξιολόγηση του κινδύνου η δυνητική ζημία (VAR) χρησιμοποιεί τρεις μεθόδους προσομοίωσης, την Ιστορική, την Monte Carlo και την Variance−covariance προσομοίωση. Παρουσιάζονται οι μέθοδοι αυτοί, τα πλεονεκτήματα και τα μειονεκτήματά τους. Η εργασία καταλήγει σε μελέτη μιας εφαρμογής, με τη μέθοδο της Ιστορικής προσομοίωσης. / The aim of enterprises is to remedy situations, which may identify increased probability losses. In order to achieve this particular objective, it is necessary to determine the importance of imminent risks. These risks can be classified into operational, not operational and financial. The primary aim of Risk management is not to evade risk, but to minimize it. The risk must be located and we have to determine its importance. The objective is to quantify the risk and calculate one measure of total risk. One measure of total risk, is the Value at Risk (VAR) approach. In its most general form, the Value at Risk (VAR) measures the potential loss in value of a risky asset or portfolio, over a defined period for a given confidence interval. The aim of this essay is the description of Value at Risk (VAR) approach. For the evaluation of the risk the Value at Risk (VAR) approach uses three methods of simulation. The Historical, the Monte Carlo and the VarianceCovariance simulation. These three methods are presented along with their advantages and disadvantages. The essay is concluded with an application using the method of Historical simulation.