Essays on the Modeling and Prediction of Volatility and Higher Moments of Stock Returns (summary section only)

One of the most fundamental and widely accepted ideas in finance is that investors are compensated through higher returns for taking on non-diversifiable risk. Hence the quantification, modeling and prediction of risk have been, and still are one of the most prolific research areas in financial economics. It was recognized early on that there are predictable patterns in the variance of speculative prices. Later research has shown that there may also be systematic variation in the skewness and kurtosis of financial returns. Lacking in the literature so far, is an out-of-sample forecast evaluation of the potential benefits of these new more complicated models with time-varying higher moments. Such an evaluation is the topic of this dissertation. Essay 1 investigates the forecast performance of the GARCH (1,1) model when estimated with 9 different error distributions on Standard and Poor’s 500 Index Future returns. By utilizing the theory of realized variance to construct an appropriate ex post measure of variance from intra-day data it is shown that allowing for a leptokurtic error distribution leads to significant improvements in variance forecasts compared to using the normal distribution. This result holds for daily, weekly as well as monthly forecast horizons. It is also found that allowing for skewness and time variation in the higher moments of the distribution does not further improve forecasts. In Essay 2, by using 20 years of daily Standard and Poor 500 index returns, it is found that density forecasts are much improved by allowing for constant excess kurtosis but not improved by allowing for skewness. By allowing the kurtosis and skewness to be time varying the density forecasts are not further improved but on the contrary made slightly worse. In Essay 3 a new model incorporating conditional variance, skewness and kurtosis based on the Normal Inverse Gaussian (NIG) distribution is proposed. The new model and two previously used NIG models are evaluated by their Value at Risk (VaR) forecasts on a long series of daily Standard and Poor’s 500 returns. The results show that only the new model produces satisfactory VaR forecasts for both 1% and 5% VaR Taken together the results of the thesis show that kurtosis appears not to exhibit predictable time variation, whereas there is found some predictability in the skewness. However, the dynamic properties of the skewness are not completely captured by any of the models.

High-density lipoprotein 17beta-estradiol fatty acyl esters and phytoestrogens : Impact on reverse cholesterol transport and women's cardiovascular health

Reverse cholesterol transport (RCT) is an important function of high-density lipoproteins (HDL) in the protection of atherosclerosis. RCT is the process by which HDL stimulates cholesterol removal from peripheral cells and transports it to the liver for excretion. Premenopausal women have a reduced risk for atherosclerosis compared to age-matched men and there exists a positive correlation for serum 17β-estradiol (E2) and HDL levels in premenopausal women supporting the role of E2 in atherosclerosis prevention. In premenopausal women, E2 associates with HDL as E2 fatty acyl esters. Discovery of the cellular targets, metabolism, and assessment of the macrophage cholesterol efflux potential of these HDL-associated E2 fatty acyl esters were the major objectives of this thesis (study I, III, and IV). Soy phytoestrogens, which are related to E2 in both structure and function, have been proposed to be protective against atherosclerosis but the evidence to support these claims is conflicting. Therefore, another objective of this thesis was to assess the ability of serum from postmenopausal women, treated with isoflavone supplements (compared to placebo), to promote macrophage cholesterol efflux (study II). The scope of this thesis was to cover the roles that HDL-associated E2 fatty acyl esters have in the cellular aspects of RCT and to determine if soy isoflavones can also influence RCT mechanisms. SR-BI was a pivotal cellular receptor, responsible for hepatic and macrophage uptake and macrophage cholesterol efflux potential of HDL-associated E2 fatty acyl esters. Functional SR-BI was also critical for proper LCAT esterification activity which could impact HDL-associated E2 fatty acyl ester assembly and its function. In hepatic cells, LDL receptors also contributed to HDL-associated E2 fatty acyl esters uptake and in macrophage cells, estrogen receptors (ERs) were necessary for both HDL-associated E2 ester-specific uptake and cholesterol efflux potential. HDL-containing E2 fatty acyl esters (E2-FAE) stimulated enhanced cholesterol efflux compared to male HDL (which are deficient in E2) demonstrating the importance of the E2 ester in this process. To support this, premenopausal female HDL, which naturally contains E2, showed greater macrophage cholesterol efflux compared to males. Additionally, hepatic and macrophage cells hydrolyzed the HDL-associated E2 fatty acyl ester into unesterified E2. This could have important biological ramifications because E2, not the esterified form, has potent cellular effects which may influence RCT mechanisms. Lastly, soy isoflavone supplementation in postmenopausal women did not modulate ABCA1-specific macrophage cholesterol efflux but did increase production of plasma pre-β HDL levels, a subclass of HDL. Therefore, the impact of isoflavones on RCT and cardiovascular health needs to be further investigated. Taken as a whole, HDL-associated E2 fatty acyl esters from premenopausal women and soy phytoestrogen treatment in postmenopausal women may be important factors that increase the efficiency of RCT through cellular lipoprotein-related processes and may have direct implications on the cardiovascular health of women.

Silvicultural decisions based on simulation-optimization systems

Forest management is facing new challenges under climate change. By adjusting thinning regimes, conventional forest management can be adapted to various objectives of utilization of forest resources, such as wood quality, forest bioenergy, and carbon sequestration. This thesis aims to develop and apply a simulation-optimization system as a tool for an interdisciplinary understanding of the interactions between wood science, forest ecology, and forest economics. In this thesis, the OptiFor software was developed for forest resources management. The OptiFor simulation-optimization system integrated the process-based growth model PipeQual, wood quality models, biomass production and carbon emission models, as well as energy wood and commercial logging models into a single optimization model. Osyczka s direct and random search algorithm was employed to identify optimal values for a set of decision variables. The numerical studies in this thesis broadened our current knowledge and understanding of the relationships between wood science, forest ecology, and forest economics. The results for timber production show that optimal thinning regimes depend on site quality and initial stand characteristics. Taking wood properties into account, our results show that increasing the intensity of thinning resulted in lower wood density and shorter fibers. The addition of nutrients accelerated volume growth, but lowered wood quality for Norway spruce. Integrating energy wood harvesting into conventional forest management showed that conventional forest management without energy wood harvesting was still superior in sparse stands of Scots pine. Energy wood from pre-commercial thinning turned out to be optimal for dense stands. When carbon balance is taken into account, our results show that changing carbon assessment methods leads to very different optimal thinning regimes and average carbon stocks. Raising the carbon price resulted in longer rotations and a higher mean annual increment, as well as a significantly higher average carbon stock over the rotation.

Econometric models of Finnish non-industrial private forest owners' timber supply and timber stock

This dissertation examines the short- and long-run impacts of timber prices and other factors affecting NIPF owners' timber harvesting and timber stocking decisions. The utility-based Faustmann model provides testable hypotheses of the exogenous variables retained in the timber supply analysis. The timber stock function, derived from a two-period biomass harvesting model, is estimated using a two-step GMM estimator based on balanced panel data from 1983 to 1991. Timber supply functions are estimated using a Tobit model adjusted for heteroscedasticity and nonnormality of errors based on panel data from 1994 to 1998. Results show that if specification analysis of the Tobit model is ignored, inconsistency and biasedness can have a marked effect on parameter estimates. The empirical results show that owner's age is the single most important factor determining timber stock; timber price is the single most important factor in harvesting decision. The results of the timber supply estimations can be interpreted using utility-based Faustmann model of a forest owner who values a growing timber in situ.

Associations of gene polymorphisms and nutrition with calcium homeostasis and bone mineral density : Studies on skeletal nutrigenetics

Heredity explains a major part of the variation in calcium homeostasis and bone strength, and the susceptibility to osteoporosis is polygenetically regulated. Bone phenotype results from the interplay between lifestyle and genes, and several nutritional factors modulate bone health throughout life. Thus, nutrigenetics examining the genetic variation in nutrient intake and homeostatic control is an important research area in the etiology of osteoporosis. Despite continuing progress in the search for candidate genes for osteoporosis, the results thus far have been inconclusive. The main objective of this thesis was to investigate the associations of lactase, vitamin D receptor (VDR), calcium sensing receptor (CaSR) and parathyroid hormone (PTH) gene polymorphisms and lifestyle factors and their interactions with bone health in Finns at varying stages of the skeletal life span. Markers of calcium homeostasis and bone remodelling were measured from blood and urine samples. Bone strength was measured at peripheral and central bone sites. Lifestyle factors were assessed with questionnaires and interviews. Genetic lactase non-persistence (the C/C-13910 genotype) was associated with lower consumption of milk from childhood, predisposing females in particular to inadequate calcium intake. Consumption of low-lactose milk and milk products was shown to decrease the risk for inadequate calcium intake. In young adulthood, bone loss was more common in males than in females. Males with the lactase C/C-13910 genotype may be more susceptible to bone loss than males with the other lactase genotypes, although calcium intake predicts changes in bone mass more than the lactase genotype. The BsmI and FokI polymorphisms of the VDR gene were associated with bone mass in growing adolescents, but the associations weakened with age. In young adults, the A986S polymorphism of the calcium sensing receptor gene was associated with serum ionized calcium concentrations, and the BstBI polymorphism of the parathyroid gene was related to bone strength. The FokI polymorphism and sodium intake showed an interaction effect on urinary calcium excretion. A novel gene-gene interaction between the VDR FokI and PTH BstBI gene polymorphisms was found in the regulation of PTH secretion and urinary calcium excretion. Further research should be carried out with more number of Finns at varying stages of the skeletal life span and more detailed measurements of bone strength. Research should concern mechanisms by which genetic variants affect calcium homeostasis and bone strength, and the role of diet-gene and gene-gene interactions in the pathogenesis of osteoporosis.

Bayesian fisheries stock assessment: integrating and updating knowledge

In this thesis the use of the Bayesian approach to statistical inference in fisheries stock assessment is studied. The work was conducted in collaboration of the Finnish Game and Fisheries Research Institute by using the problem of monitoring and prediction of the juvenile salmon population in the River Tornionjoki as an example application. The River Tornionjoki is the largest salmon river flowing into the Baltic Sea. This thesis tackles the issues of model formulation and model checking as well as computational problems related to Bayesian modelling in the context of fisheries stock assessment. Each article of the thesis provides a novel method either for extracting information from data obtained via a particular type of sampling system or for integrating the information about the fish stock from multiple sources in terms of a population dynamics model. Mark-recapture and removal sampling schemes and a random catch sampling method are covered for the estimation of the population size. In addition, a method for estimating the stock composition of a salmon catch based on DNA samples is also presented. For most of the articles, Markov chain Monte Carlo (MCMC) simulation has been used as a tool to approximate the posterior distribution. Problems arising from the sampling method are also briefly discussed and potential solutions for these problems are proposed. Special emphasis in the discussion is given to the philosophical foundation of the Bayesian approach in the context of fisheries stock assessment. It is argued that the role of subjective prior knowledge needed in practically all parts of a Bayesian model should be recognized and consequently fully utilised in the process of model formulation.

Computationally Efficient Methods for MDL-Optimal Density Estimation and Data Clustering

The Minimum Description Length (MDL) principle is a general, well-founded theoretical formalization of statistical modeling. The most important notion of MDL is the stochastic complexity, which can be interpreted as the shortest description length of a given sample of data relative to a model class. The exact definition of the stochastic complexity has gone through several evolutionary steps. The latest instantation is based on the so-called Normalized Maximum Likelihood (NML) distribution which has been shown to possess several important theoretical properties. However, the applications of this modern version of the MDL have been quite rare because of computational complexity problems, i.e., for discrete data, the definition of NML involves an exponential sum, and in the case of continuous data, a multi-dimensional integral usually infeasible to evaluate or even approximate accurately. In this doctoral dissertation, we present mathematical techniques for computing NML efficiently for some model families involving discrete data. We also show how these techniques can be used to apply MDL in two practical applications: histogram density estimation and clustering of multi-dimensional data.