Yes / This paper investigates the role of institutional investor sentiment in the mean–variance relation. We find market returns are negatively (positively) related to market’s conditional volatility over bullish (bearish) periods. The evidence indicates institutional investors to be sentiment traders as well.

Yes / This paper investigates the mean-variance relation during different time periods within trading days. We reveal that there is a positive mean-variance relation when the stock market is closed (i.e., overnight), but the positive relation is distorted when the market is open (i.e., intraday). The evidence offers a new explanation for the weak risk-return tradeoff in stock markets.

This thesis examines the exchange rate pass-through into consumer prices in Central and Eastern Europe. The study is based on quarterly data of 12 countries from 2003 to 2013. Estimations are conducted using heterogeneous panel cointegration methods, namely the mean group and the pooled mean group estimators. Fixed effects are used as a reference. The thesis provides short- run and long-run estimates of the exchange rate pass-through for the individual countries and for the region as a whole. Based on the results, we conclude that the exchange rate pass-through is highly variable across Central and Eastern Europe. We find that there is no clear distinction between the pass-through rates in euro area countries, EU countries not using the euro and non- EU countries. Further, we find that the generally accepted concept of higher exchange rate pass- though in developing countries does not hold in this region. JEL Classification C23, E31, E52, F31 Keywords exchange rate pass-through, pooled mean group, mean group, heterogeneous panel cointegration Author's e-mail bara.mirkova@centrum.cz Supervisor's e-mail roman.horvath@gmail.com

In view of the current situation of fluctuating high crude oil prices, it is now more important than ever for petroleum refineries to operate at an optimal level in the present dynamic global economy. Acknowledging the shortcomings of deterministic models, this work proposes a hybrid of stochastic programming formulations for an optimal midterm refinery planning that addresses three factors of uncertainties, namely price of crude oil and saleable products, product demand, and production yields. An explicit stochastic programming technique is utilized by employing compensating slack variables to account for violations of constraints in order to increase model tractability. Four approaches are considered to ensure both solution and model robustness: (1) the Markowitz???s mean???variance (MV) model to handle randomness in the objective coefficients of prices by minimizing variance of the expected value of the random coefficients; (2) the two-stage stochastic programming with fixed recourse approach via scenario analysis to model randomness in the right-hand side and left-hand side coefficients by minimizing the expected recourse penalty costs due to constraints??? violations; (3) incorporation of the MV model within the framework developed in Approach 2 to minimize both the expectation and variance of the recourse costs; and (4) reformulation of the model in Approach 3 by adopting mean-absolute deviation (MAD) as the risk metric imposed by the recourse costs for a novel application to the petroleum refining industry. A representative numerical example is illustrated with the resulting outcome of higher net profits and increased robustness in solutions proposed by the stochastic models.

This thesis is an implementation project of a portfolio optimization model, with the purpose of creating a decision support tool. It aims to provide quantitative input to the portfolio construction process at Handelsbanken Fonder, by applying Konno & Yamazaki’s Mean Absolute Deviation method, with a Feinstein & Thapa modification. Additionally, the Black-Litterman model is implemented to approximate the input of expected return. The linear optimization problem was then solved by the Simplex algorithm. The main deliverable is a model that can assist portfolio managers in making investment decisions. Back-testing of the model showed that it did not outperform the benchmark portfolios, which is likely a result of only allowing long positions in the model. Nevertheless, the model provides value by giving the user a second opinion on the efficient frontier, for any given investment decision. / Den här uppsatsen är ettimplementationsprojekt av enportföljoptimerings-modell, med syftet att skapaett beslutsstödjande verktyg. Den strävar efter att ge ett kvantitativt bidragtill portföljallokerings-processen på Handelsbanken Fonder, genom att användaKonno & Yamazaki’s Mean Absolute Deviation-metod med en Feinstein &Thapa-modifiering. Vidare har Black-Littermanmodellen implementerats för attapproximera den förväntade avkastningen. Det linjära optimeringsproblemetlöstes sedan med Simplex-algorithmen. Det huvudsakliga resultatet är en modellsom kan assisterafondförvaltare i investeringsbeslut. Utförda utfallstestvisade att modellen inte överträffade de använda benchmark-fonderna, vilketsannolikt är ett resultat av att modellen enbart tillåterlånga positioner.Likväl, kan modellen vara värdefull genom att erbjuda användaren ett alternativpå den effektiva fronten, för ett givet investeringsbeslut.

A dimensionally compatible system of equations for stand basal area, volume, and basal area and volume growth was derived using dimensional analysis. These equations are analytically and numerically consistent with dimensionally compatible individual tree volume and taper equations and share parameters with them. Parameters for the system can be estimated by fitting individual tree taper and volume equations or by fitting stand level basal area and volume equations. In either case the parameters are nearly identical. Therefore, parameters for the system can be estimated at the tree or stand level without changing the results. Data from a thinning study in loblolly pine (Pinus taeda L.) plantations established on cutover site-prepared lands were used to estimate the parameters. However, the developed system of equations is general and can be applied to other tree species in other locales. / Ph. D.

No description available.

The phenomenon of financial bubbles is known to have impacted various markets since the seventeenth century. Such bubbles are known to form when the market drastically overvalues the price of an asset, causing its market value to increase hyperbolically, only to suddenly collapse once the untenable perceived future prospects of the asset are realized. Hence, it remains crucial for investors to be able to sell off assets residing within a bubble before they burst and their value is significantly diminished. Thus, portfolio optimization methods capable of accounting for financial bubbles in stock dynamics is a field of great value and interest for market participants. Portfolio optimization with respect to the mean-field is a relatively novel approach to accounting for the bubble-phenomenon. Hence, this paper investigates a previously unattempted method of portfolio optimization, providing a mean-field solution to the mean-variance trade-off problem, as well as providing new definitions of stock dynamics capable of diverting investors from bubbles. / Finansiella bubblor är ett fenomen som har påverkat marknader sedan 1600-talet. Bubblor tenderar att skapas när marknaden kraftigt övervärderar en tillgång vilket orsakar en hyperbolisk tillväxt i marknadspriset. Detta följs av en plötslig kollaps. Därför är det viktigt för investerare att kunna minska sin exponering mot aktier som befinner sig i en bubbla, så att risken för stora plötsliga förluster reduceras. Således är portföljoptimering där aktiedynamiken tar hänsyn till bubblor av högt intresse för marknadsdeltagare. Portföljoptimering med avseende på medelfältet är ett relativt nytt tillvägagångssätt för att behandla bubbelfenomen. Av denna anledning undersöks i detta arbete en hittills oprövad lösningsmetod som möjliggör en medelfältslösning till avvägningen mellan förväntad avkastning och risk. Där-utöver presenteras även ett antal nya modeller för aktier som kan bortleda investerare från bubblor.

<p dir="ltr">Statistical arbitrage is a prevalent trading strategy which takes advantage of mean reverse property of spreads constructed from pairs or portfolios of assets. Utilizing statistical models and algorithms, statistical arbitrage exploits and capitalizes on the pricing inefficiencies between securities or within asset portfolios. </p><p dir="ltr">In chapter 2, We propose a framework for constructing diversified portfolios with multiple pairs trading strategies. In our approach, several pairs of co-moving assets are traded simultaneously, and capital is dynamically allocated among different pairs based on the statistical characteristics of the historical spreads. This allows us to further consider various portfolio designs and rebalancing strategies. Working with empirical data, our experiments suggest the significant benefits of diversification within our proposed framework.</p><p dir="ltr">In chapter 3, we explore an optimal timing strategy for the trading of price spreads exhibiting mean-reverting characteristics. A sequential optimal stopping framework is formulated to analyze the optimal timings for both entering and subsequently liquidating positions, all while considering the impact of transaction costs. Then we leverages a refined signature optimal stopping method to resolve this sequential optimal stopping problem, thereby unveiling the precise entry and exit timings that maximize gains. Our framework operates without any predefined assumptions regarding the dynamics of the underlying mean-reverting spreads, offering adaptability to diverse scenarios. Numerical results are provided to demonstrate its superior performance when comparing with conventional mean reversion trading rules.</p><p dir="ltr">In chapter 4, we introduce an innovative model-free and reinforcement learning based framework for statistical arbitrage. For the construction of mean reversion spreads, we establish an empirical reversion time metric and optimize asset coefficients by minimizing this empirical mean reversion time. In the trading phase, we employ a reinforcement learning framework to identify the optimal mean reversion strategy. Diverging from traditional mean reversion strategies that primarily focus on price deviations from a long-term mean, our methodology creatively constructs the state space to encapsulate the recent trends in price movements. Additionally, the reward function is carefully tailored to reflect the unique characteristics of mean reversion trading.</p>

採用風險值取代標準差來衡量投資組合的下方風險，除了更符合投資人的對風險的態度，也更貼近目前金融機構多以風險值作為內部控管工具的情形。但除了風險的事後衡量，本篇論文希望能夠事前積極地控制投資組合風險值，求得最適投資組合的各資產配置權重。故本篇論文研究方法採用了Rockafellar and Uryasev.(2000)的極小條件風險值最適投資組合模型先建立Mean-CVaR效率前緣，並將此效率前緣上的投資組合風險以風險值衡量，再應用電腦上的探索方法進一步求得風險值更低的投資組合，逼近求得Mean-VaR效率前緣，最後利用Mean-VaR效率前緣採用Campbell，Huisman與Koedijk(2001)模型求得控制風險值下的最適投資組合。 在實證分析上，本篇論文採用國內三檔股票為標的，首先在實證標的資產報酬檢定為非常態分配下，使用歷史模擬法，以資產實際非常態報酬分配估計VaR，驗證了使用本篇論文研究方法極小CVaR投資組合與探索方法，可以適當逼近真實的Mean-VaR效率前緣。再者研究比較不同信賴水準、不同資產報酬分配假設與不同權重產生方式下的Mean-VaR效率前緣與Ｍean- 效率前緣效果差異，最後求得控制風險值下的最適投資組合。 / In contrast to the role of variance in the traditional Mean-Variance framework, in this thesis we introduce Value-at-Risk (VaR) as a shortfall-constraint into the portfolio selection decision. Doing so is much more in fitting with individual perception to risk and in line with the constraints which financial institutes currently face. However, mathematically VaR has some serious limitations making the portfolio selection problem difficult to attain optimal solution. In order to apply VaR to ex ante portfolio decision, we use the closely related tractable risk measure Conditional Value-at-Risk (CVaR) in this thesis as a proxy to find efficient portfolios. We utilize linear programming formulation developed by Rockafellar and Uryasev(2000) to construct a Mean-CVaR efficient frontier. Following which the VaR of resulting portfolios in the Mean-CVaR efficient frontier is reduced further by a simple heuristic procedure. After constructing an empirical Mean-VaR efficient frontier that can be proven an useful approximation to the true Mean-VaR efficient frontier, the Campbell, Huisman and Koedijk(2001) model is used to find the optimal portfolio. Three Taiwan listing stocks are used to build the Mean-VaR efficient frontier in the empirical study. And the Mean-VaR efficient frontier of different confident levels, under different asset return assumptions, and different optimal portfolio selection models are compared and results analyzed.