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A life distribution and its survival function F = 1 - F with finitemean y = /q F(x)dx are said to be HNBUE (HNWUE) if F(x)dx &lt; (&gt;)U exp(-t/y) for t &gt; 0. The major part of this thesis deals with the classof HNBUE (HNWUE) life distributions. We give different characterizationsof the HNBUE (HNWUE) property and present bounds on the moments and on thesurvival function F when this is HNBUE (HNWUE). We examine whether theHNBUE (HNWUE) property is preserved under some reliability operations andstudy some test statistics for testing exponentiality against the HNBUE(HNWUE) property.The HNBUE (HNWUE) property is studied in connection with shock models.Suppose that a device is subjected to shocks governed by a counting processN = {N(t): t &gt; 0}. The probability that the device survives beyond t isthen00H(t) = S P(N(t)=k)P, ,k=0where P^ is the probability of surviving k shocks. We prove that His HNBUE (HNWUE) under different conditions on N and * ^orinstance we study the situation when the interarrivai times between shocksare independent and HNBUE (HNWUE).We also study the Pure Birth Shock Model, introduced by A-Hameed andProschan (1975), and prove that H is IFRA and DMRL under conditions whichdiffer from those used by A-Hameed and Proschan.Further we discuss relationships between the total time on test transformHp^(t) = /q ^F(s)ds , where F \t) = inf { x: F(x) &gt; t}, and differentclasses of life distributions based on notions of aging. Guided by propertiesof we suggest test statistics for testing exponentiality agains t IFR,IFRA, NBUE, DMRL and heavy-tailedness. Different properties of these statisticsare studied.Finally, we discuss some bivariate extensions of the univariate properties NBU, NBUE, DMRL and HNBUE and study some of these in connection with bivariate shock models. / <p>There are some occurring misspellings in the formulas in the abstract on this webpage. Read the abstract in the full-text document for correct spelling in formulas, see the downloadable file.</p> / digitalisering@umu

The modelling of volatility has long been seminal to finance and risk management in general, as it provides information on the spread of portfolio returns. In order to reduce the overall volatility of a stock portfolio, modern portfolio theory (MPT), within an efficient market hypothesis (EMH) framework, dictates that a well-diversified portfolio should have a market beta of one (thereafter adjusted for risk preference), and thus move in sync with a benchmark market portfolio. Such a stock portfolio is highly correlated with the market, and considered to be entirely hedged against unsystematic risk. However, the risks within and between stocks present in a portfolio still impact on each other. In particular, risk present in a particular stock may spill over and affect the risk profile of another stock included within a portfolio - a phenomenon known as volatility spill-over effects. In developing economies such as South Africa, portfolio managers are limited in their choices of stocks. This increases the difficulty of fully diversifying a stock portfolio given the volatility spill-over effects that may be present between stocks listed on the same exchange. In addition, stock portfolios are not static, and therefore require constant rebalancing according to the mandate of the managing fund. The process of constant rebalancing of a stock portfolio (for instance, to follow the market) becomes more complex and difficult during times of financial distress. Considering all these conditions, portfolio managers need all the relevant information (more than MPT would provide) available to them in order to select and rebalance a portfolio of stocks that are as mean-variance efficient as possible. This study provides an additional measure to market beta in order to construct a more efficient portfolio. The additional measure analyse the volatility spill-over effects between stocks within the same portfolio. Using intraday stock returns and a residual based test (aggregate shock [AS] model), volatility spill-over effects are estimated between stocks. It is shown that when a particular stock attracts fewer spill-over effects from the other stocks in the portfolio, the overall portfolio volatility would decrease as well. In most cases market beta showcased similar results; this change is however not linear in the case of market beta. Therefore, in order to construct a more efficient portfolio, one requires both a portfolio that has a unit correlation with the market, but also includes stocks with the least amount of volatility spill-over effects among each other. / MCom (Risk Management), North-West University, Potchefstroom Campus, 2013

The modelling of volatility has long been seminal to finance and risk management in general, as it provides information on the spread of portfolio returns. In order to reduce the overall volatility of a stock portfolio, modern portfolio theory (MPT), within an efficient market hypothesis (EMH) framework, dictates that a well-diversified portfolio should have a market beta of one (thereafter adjusted for risk preference), and thus move in sync with a benchmark market portfolio. Such a stock portfolio is highly correlated with the market, and considered to be entirely hedged against unsystematic risk. However, the risks within and between stocks present in a portfolio still impact on each other. In particular, risk present in a particular stock may spill over and affect the risk profile of another stock included within a portfolio - a phenomenon known as volatility spill-over effects. In developing economies such as South Africa, portfolio managers are limited in their choices of stocks. This increases the difficulty of fully diversifying a stock portfolio given the volatility spill-over effects that may be present between stocks listed on the same exchange. In addition, stock portfolios are not static, and therefore require constant rebalancing according to the mandate of the managing fund. The process of constant rebalancing of a stock portfolio (for instance, to follow the market) becomes more complex and difficult during times of financial distress. Considering all these conditions, portfolio managers need all the relevant information (more than MPT would provide) available to them in order to select and rebalance a portfolio of stocks that are as mean-variance efficient as possible. This study provides an additional measure to market beta in order to construct a more efficient portfolio. The additional measure analyse the volatility spill-over effects between stocks within the same portfolio. Using intraday stock returns and a residual based test (aggregate shock [AS] model), volatility spill-over effects are estimated between stocks. It is shown that when a particular stock attracts fewer spill-over effects from the other stocks in the portfolio, the overall portfolio volatility would decrease as well. In most cases market beta showcased similar results; this change is however not linear in the case of market beta. Therefore, in order to construct a more efficient portfolio, one requires both a portfolio that has a unit correlation with the market, but also includes stocks with the least amount of volatility spill-over effects among each other. / MCom (Risk Management), North-West University, Potchefstroom Campus, 2013