<p>We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatility. We consider different methods of parametrization of returns and following the paper of Lindberg, [21] we</p><p>assume that the volatility is a linear function of the number of trades. In addition to the Lindberg’s paper, we suggest daily stock volumes and amounts as alternative measures of the volatility.</p><p>As an application of the models, we perform Value-at-Risk and Expected Tail Loss predictions by the Lindberg’s volatility model and by our own suggested model. These applications are new and not described in the</p><p>literature. For better understanding of our caluclations, programmes and simulations, basic informations and properties about the Normal Inverse Gaussian and Inverse Gaussian distributions are provided. Practical applications of the models are implemented on the Nasdaq-OMX, where we have calculated Value-at-Risk and Expected Tail Loss</p><p>for the Ericsson B stock data during the period 1999 to 2004.</p>

We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatility. We consider different methods of parametrization of returns and following the paper of Lindberg, [21] we assume that the volatility is a linear function of the number of trades. In addition to the Lindberg’s paper, we suggest daily stock volumes and amounts as alternative measures of the volatility. As an application of the models, we perform Value-at-Risk and Expected Tail Loss predictions by the Lindberg’s volatility model and by our own suggested model. These applications are new and not described in the literature. For better understanding of our caluclations, programmes and simulations, basic informations and properties about the Normal Inverse Gaussian and Inverse Gaussian distributions are provided. Practical applications of the models are implemented on the Nasdaq-OMX, where we have calculated Value-at-Risk and Expected Tail Loss for the Ericsson B stock data during the period 1999 to 2004.

We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatility. We consider different methods of parametrization of returns and following the paper of Lindberg, [21] we assume that the volatility is a linear function of the number of trades. In addition to the Lindberg’s paper, we suggest daily stock volumes and amounts as alternative measures of the volatility. As an application of the models, we perform Value-at-Risk and Expected Tail Loss predictions by the Lindberg’s volatility model and by our own suggested model. These applications are new and not described in the literature. For better understanding of our caluclations, programmes and simulations, basic informations and properties about the Normal Inverse Gaussian and Inverse Gaussian distributions are provided. Practical applications of the models are implemented on the Nasdaq-OMX, where we have calculated Value-at-Risk and Expected Tail Loss for the Ericsson B stock data during the period 1999 to 2004.

本文提出了一個新興風險衡量的工具的概念-期望尾端損失值(ETL)，其有別於風險值為百分位數且未考慮報酬分配的尾部風險(Tail Risk)，本研究期望能透過ETL的估計可以更完整表達投資組合所有可能面臨的風險，對於市場風險能更有效控管。 本文實證討論有關VaR與ETL穩定度的部分，VaR雖然在理論上證明無法滿足次可加性這個條件，但是在本研究實證中，即使在分配具厚尾狀況下，VaR仍滿足次加性的性質。這也表示，我們在現實生活中很難因VaR理論上缺乏次可加性，而捨棄VaR這個風險衡量工具，然ETL也有其貢獻性，其較VaR多考慮尾部資訊，可視為風險值外另一參考指標，此為本文貢獻一。 本文實證也探討移動窗口中歷史資料長度的不同，是否造成VaR與ETL估算準確性的差異，本文由實證結果發現：在歷史窗口的資料長度越長(1000日)下，並沒有正確預估VaR與ETL，而本研究中以移動窗口為500日下，使用內部模型較具正確性，故在使用風險值模型時，應謹慎選擇移動窗口之長度，此為本文貢獻二。

Asset allocation is the most influential factor driving investment performance. While researchers have made substantial progress in the field of asset allocation since the introduction of mean-variance framework by Markowitz, there is little agreement about appropriate portfolio choice for multi-period long horizon investors. Nowhere this is more evident than trustees of retirement plans choosing different asset allocation strategies as default investment options for their members. This doctoral dissertation consists of four essays each of which explores either a novel or an unresolved issue in the area of asset allocation for individual retirement plan participants. The goal of the thesis is to provide greater insight into the subject of portfolio choice in retirement plans and advance scholarship in this field. The first study evaluates different constant mix or fixed weight asset allocation strategies and comments on their relative appeal as default investment options. In contrast to past research which deals mostly with theoretical or hypothetical models of asset allocation, we investigate asset allocation strategies that are actually used as default investment options by superannuation funds in Australia. We find that strategies with moderate allocation to stocks are consistently outperformed in terms of upside potential of exceeding the participant’s wealth accumulation target as well as downside risk of falling below that target by very aggressive strategies whose allocation to stocks approach 100%. The risk of extremely adverse wealth outcomes for plan participants does not appear to be very sensitive to asset allocation. Drawing on the evidence of the previous study, the second essay explores possible solutions to the well known problem of gender inequality in retirement investment outcomes. Using non-parametric stochastic simulation, we simulate iv and compare the retirement wealth outcomes for a hypothetical female and male worker under different assumptions about breaks in employment, superannuation contribution rates, and asset allocation strategies. We argue that modest changes in contribution and asset allocation strategy for the female plan participant are necessary to ensure an equitable wealth outcome in retirement. The findings provide strong evidence against gender-neutral default contribution and asset allocation policy currently institutionalized in Australia and other countries. In the third study we examine the efficacy of lifecycle asset allocation models which allocate aggressively to risky asset classes when the employee participants are young and gradually switch to more conservative asset classes as they approach retirement. We show that the conventional lifecycle strategies make a costly mistake by ignoring the change in portfolio size over time as a critical input in the asset allocation decision. Due to this portfolio size effect, which has hitherto remained unexplored in literature, the terminal value of accumulation in retirement account is critically dependent on the asset allocation strategy adopted by the participant in later years relative to early years. The final essay extends the findings of the previous chapter by proposing an alternative approach to lifecycle asset allocation which incorporates performance feedback. We demonstrate that strategies that dynamically alter allocation between growth and conservative asset classes at different points on the investment horizon based on cumulative portfolio performance relative to a set target generally result in superior wealth outcomes compared to those of conventional lifecycle strategies. The dynamic allocation strategy exhibits clear second-degree stochastic dominance over conventional strategies which switch assets in a deterministic manner as well as balanced diversified strategies.