No description available.

No description available.

This paper focuses on how to improve strategic asset allocation in practice. Strategic asset allocation is perhaps the most fundamental issue in portfolio management and it has been thoroughly discussed in previous research. We take our starting point in the traditional work of Markowitz within portfolio optimization. We provide a new solution of how to perform portfolio optimization in practice, or more specifically how to estimate the covariance matrix, which is needed to perform conventional portfolio optimization. Many researchers within this field have noted that the return distribution of financial assets seems to vary over time, so called regime switching, which makes it dicult to estimate the covariance matrix. We solve this problem by using a Bayesian approach for developing a Markov chain Monte Carlo algorithm that detects break points in the return distribution of financial assets, thus enabling us to improve the estimation of the covariance matrix. We find that there are two break points during the time period studied and that the main difference between the periods are that the volatility was substantially higher for all assets during the period that corresponds to the financial crisis, whereas correlations were less affected. By evaluating the performance of the algorithm we find that the algorithm can increase the Sharpe ratio of a portfolio, thus that our algorithm can improve strategic asset allocation over time. / Detta examensarbete fokuserar på hur man kan förbättra tillämpningen av strategisk tillgångsslagsallokering i praktiken. Hur man allokerar kapital mellan tillgångsslag är kanske de mest fundamentala beslutet inom kapitalförvaltning och ämnet har diskuterats grundligt i litteraturen. Vårt arbete utgår från Markowitz traditionella teorier inom portföljoptimering och utifrån dessa tar vi fram ett nytt angreppssätt för att genomföra portföljoptimering i praktiken. Mer specifikt utvecklar vi ett nytt sätt att uppskatta kovar-iansmatrisen för avkastningsfördelningen för finansiella tillgångar, något som är essentiellt för att kunna beräkna de optimala portföljvikterna enligt Markowitz. Det påstås ofta att avkastningens fördelning förändras över tid; att det sker så kallade regimskiften, vilket försvårar uppskattningen av kovariansmatrisen. Vi löser detta problem genom att använda ett Bayesiansk angreppssätt där vi utvecklar en Markov chain Monte Carlo-algoritm som upptäcker brytpunkter i avkastningsfördelningen, vilket gör att uppskattningen av kovar-iansmatrisen kan förbättras. Vi finner två brytpunkter i fördelningen under den studerade tidsperioden och den huvudsakliga skillnaden mellan de olika tidsperioderna är att volatiliten var betydligt högre för samtliga tillgångar under den tidsperiod som motsvaras av finanskrisen, medan korrelationerna mellan tillgångsslagen inte påverkades lika mycket. Genom att utvärdera hur algoritmen presterar finner vi att den ökar en portföljs Sharpe ratio och således att den kan förbättra den strategiska allokeringen mellan tillgångsslagen över tid.

Geckos and other insects have fascinated scientists and casual observers with their ability to effortlessly climb up walls and across ceilings. This capability has inspired high capacity, easy release synthetic adhesives, which have focused on mimicking the fibrillar features found on the foot pads of these climbing organisms. However, without a fundamental framework that connects biological and synthetic adhesives from nanoscopic to macroscopic features, synthetic mimics have failed to perform favorably at large contact areas. In this thesis, we present a scaling approach which leads to an understanding of reversible adhesion in both synthetic and biological systems over multiple length scales. We identify, under various loading scenarios, how geometry and material properties control adhesion, and we apply this understanding to the development of high capacity, easy release synthetic adhesive materials at macroscopic size scales. Starting from basic fracture mechanics, our generalized scaling theory reveals that the ratio of contact area to compliance in the loading direction, A/C, is the governing scaling parameter for the force capacity of reversible adhesive interfaces. This scaling theory is verified experimentally in both synthetic and biological adhesive systems, over many orders of magnitude in size and adhesive force capacity (Chapter 2). This understanding is applied to the development of gecko-like adhesive pads, consisting of stiff, draping fabrics incorporated with thin elastomeric layers, which at macroscopic sizes (contact areas of 100 cm2) exhibit force capacities on the order of 3000 N. Significantly, this adhesive pad is non-patterned and completely smooth, demonstrating that fibrillar features are not necessary to achieve high capacity, easy release adhesion at macroscopic sizes and emphasizing the importance of subsurface anatomy in biological adhesive systems (Chapter 2, Chapter 3). We further extend the utility of the scaling theory under shear (Chapter 4) and normal (Chapter 5) loading conditions and develop simple expressions for patterned and non-patterned interfaces which describe experimental force capacity data as a function of geometric parameters such as contact area, aspect ratio, and contact radius. These studies provide guidance for the precise control of adhesion with enables the development of a simple transfer printing technique controlled by geometric confinement (Chapter 6). Force capacity data from each chapter, along with various literature data are collapsed onto a master plot described by the A/C scaling parameter, with agreement over 15 orders of magnitude in adhesive force capacity for synthetic and biological adhesives, demonstrating the generality and robustness of the scaling theory (Chapter 7).

No description available.

Elastin-like polypeptides (ELP) are artificially designed protein biopolymers that can be produced by living organisms. These proteins have the unique ability to undergo reversible inverse phase transition, in response to changes in temperature and/or addition of chaotropic salts. Below the transition temperature (T1) , ELP is soluble in water. Increasing the temperature above Ti, ELP coacervates into an aqeous ELP-rich phase. In this thesis, this unique feature of ELP was used in for recombinant protein purification and for the formation of aqueous multiple-phase systems. For protein purification, ELP was fused with an intein and a model protein (thioredoxin), to demonstrate a simple and inexpensive approach for recombinant protein purification. The ELP tags replace the chromatographic media and the intein replaces the use of the protease in conventional methods. Using ELP tags was found to be consistent with large -scale recombinant protein production/purification by purifying an ELP tagged protein using a stirred cell equipped with a microfiltration membrane. When the temperature and/or salt concer.tration is increased for mixtures containing free ELP and ELP tagged proteins, simultaneous phase transition takes place. This served as the basis for the development of a method suitable for selectively recovering molecules from complex mixtures with high specificity, full reversibility, and virtually unlimited affinity. The second parts of this thesis focus on the ability of ELP to form aqueous twophase systems (A TPS) in vitro and most importantly, in vivo- with the formation of aqueous microcompartments in living cells. These compartments exclude the protein making machinery of the cell, acting as depots for newly expressed protein. It is also shown (in vitro) that ELP bastd droplets exclude proteases, protecting proteins from degradation. These observations are important for high-level production of recombinant proteins. Also described, is the formation of protein based aqueous multiphasic systems, with tunable morphologies. / Thesis / Doctor of Philosophy (PhD)

No description available.

No description available.

No description available.

No description available.