Gender-dependent progression of systemic metabolic states in early childhood

Little is known about the human intra-individual metabolic profile changes over an extended period of time. Here, we introduce a novel concept suggesting that children even at a very young age can be categorized in terms of metabolic state as they advance in development. The hidden Markov models were used as a method for discovering the underlying progression in the metabolic state. We applied the methodology to study metabolic trajectories in children between birth and 4 years of age, based on a series of samples selected from a large birth cohort study. We found multiple previously unknown age- and gender-related metabolome changes of potential medical significance. Specifically, we found that the major developmental state differences between girls and boys are attributed to sphingolipids. In addition, we demonstrated the feasibility of state-based alignment of personal metabolic trajectories. We show that children have different development rates at the level of metabolome and thus the state-based approach may be advantageous when applying metabolome profiling in search of markers for subtle (patho)physiological changes

Metabolic Regulation in Progression to Autoimmune Diabetes

Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede β-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their conservation in a murine model of type 1 diabetes. We performed a study in non-obese prediabetic (NOD) mice which recapitulated the design of the human study and derived the metabolic states from longitudinal lipidomics data. We show that female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, normoglycemia, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum group. Together, the findings indicate that autoimmune diabetes is preceded by a state of increased metabolic demands on the islets resulting in elevated insulin secretion and suggest alternative metabolic related pathways as therapeutic targets to prevent diabetes

Macroeconomic Volatility and Asset Prices

This dissertation investigates, both theoretically and empirically, how does the macroeconomic volatility, in particular, consumption growth, GDP growth and inflation volatility, affect asset prices in equity, bond and currency markets. In all three chapters of the dissertation I use the Bad Environment-Good Environment structure of Bekaert and Engstrom (2014) to model macroeconomic volatility. The key advantage of the approach is that it allows to model non-Gaussian features important in macroeconomic dynamics while yielding closed-form asset pricing solutions and being relatively efficient to estimate. In the first chapter of the dissertation I show that an external habit model augmented with a heteroskedastic consumption growth process reproduces well known domestic and international bond market puzzles, considered difficult to replicate simultaneously. Domestically, the model generates an upward sloping real yield curve and realistic violations of the expectation hypothesis. Depending on the parameters, the model can also generate a downward sloping real yield curve and predicts that the expectation hypothesis violations are stronger in countries with upward sloping real yield curves. Internationally, the model explains violations of the uncovered interest rate parity. Unlike a standard habit model, the model simultaneously features intertemporal smoothing to match domestic real yield curve slope and bond return predictability and precautionary savings to reproduce international predictability. The model also replicates the imperfect correlation between consumption and bond prices/exchange rates through positive and negative consumption shocks affecting habit differently. Empirical support for the model mechanisms is provided. In the second chapter, coauthored with my advisor Geert Bekaert and Eric Engstrom of Board of Governors of the Federal Reserve System, we extract aggregate supply and demand shocks for the US economy from data on inflation and real GDP growth. Imposing minimal theoretical restrictions, we obtain identification through exploiting non-Gaussian features in the data. The risks associated with these shocks together with expected inflation and expected economic activity are the key factors in a tractable no-arbitrage term structure model. Despite non-Gaussian dynamics in the fundamentals, we obtain closed-form solutions for yields as functions of the state variables. The time variation in the covariance between inflation and economic activity, coupled with their non-Gaussian dynamics leads to rich patterns in inflation risk premiums and the term structure. The macro variables account for over 70\% of the variation in the levels of yields, with the bulk attributed to expected GDP growth and inflation. In contrast, macro risks predominantly account for the predictive power of the macro variables for excess holding period returns. In the final chapter, I embed the macroeconomic dynamics from the second chapter into an external habit model to analyze the time-varying stock and bond return correlations. Despite featuring flexible non-Gaussian fundamental processes, the model can be solved in closed-form. The estimation identifies time-varying "demand-like" and "supply-like" macroeconomic shocks directly linked to the risk of nominal assets and matches standard properties of US stock and bond returns. I find that macroeconomic shocks generate sizeable positive and negative correlations, although negative correlations occur less frequently and are smaller than in data. Historically, macroeconomic shocks are most important in explaining high correlations from the late 70's until the early 90's and low correlations pre- and during the Great Recession

Analysis of differences between metabolic time series with hidden Markov models

In the thesis the method for finding and analyzing differences between sparse metabolic time series was developed. In metabolic time series the measurements contain concentrations of chemical compounds produced in reactions in a living organism. Analyzing sparse metabolic time series is an important task in medicine and biology, because the metabolome contains a lot of information about the organism, for example about diseases or pathologies, but at the same time it is usually difficult and expensive to make frequent measurements. The most important characteristics of the data used in the study are that time series are relatively short and sparse (that is time interval between subsequential observations is considerably longer than duration of the most biochemical reactions in an organism), measurements are confounded with heavy noise, and the number of time series available is considerably smaller than the dimension of the measurements. The developed approach was primarily designed for metabolomic data, but it can also be applied to the time series with the similar characteristics in other fields. The developed approach contains four stages: preprocessing, designing statistical model, finding differences and analyzing their statistical significance. Hidden Markov Models (HMM) are employed to find differences between metabolic time series. HMM is a statistical method where the modeled system is assumed to be a Markov chain with unknown ("hidden") states emitting visible observations. The properties of the underlying process can be analyzed based on the characteristics of the hidden states and their interrelationships. The developed method was succesfully applied to find and analyze differences between metabolic time series of males and females in growing age extracted from blood plasma. Several time-dependent between-gender differences were identified. Justified suggestions about where these differences come from and about their general structure were made. Compared to methods that ignore the time series structure, HMM-based approach gives superior results and provides some completely new insights to between-gender differences, for instance progression of the development can be investigated. HMMs also combine several advantages compared to other time series modelling methods: they are computationally relatively light, are able to produce relatively good results with the moderate amount of data and can be applied to sparse time series. It is relatively easy to extend and generalize the developed method

Market trends and trading in options versus stocks

Market Trends and Trading in Options versus Stocks PURPOSE OF THE STUDY Although there is a lot of evidence on impact of market states on investors’ behavior, the majority of studies in the area concentrates on equity markets, while options have received very limited attention. This thesis aims to partially fill this gap. The topic is of particular importance given the recent fast growth of derivatives markets. In this study, I theoretically and empirically address the question of how do past equity returns affect trading in options compared to stocks. I investigate both informed and uninformed trade. To the best of my knowledge, this thesis is the first academic study on the subject. DATA The sample consists of Chicago Board of Options Exchange (CBOE) traded individual stock options associated with New York Stock Exchange (NYSE) traded equity and covers the period from January 1996 to September 2008. The data includes option market’s transactions, stock market’s transactions, and analysts’ forecasts. Option market data is obtained from the OptionMetrics, which is currently the most comprehensive publically available options trading database. Stock market data comes from The Center for Research in Security Prices database (CRSP). Analysts’ forecasts were extracted from Thomson Reuters I/B/E/S database (IBES). RESULTS I develop a theoretical model predicting that both trading volume in options versus stocks and option market’s price informativeness increase following stock market gains. In the model, I incorporate the overconfidence mechanism of Gervais and Odean (2001) into the trading framework by Admati and Pfleiderer (1988). Uninformed (liquidity) investors on aggregate hold long positions in equity and, due to a self-attribution bias, become more (over)confident following bull markets. Overconfident liquidity investors prefer to trade in options underestimating risks of leverage and attracted by higher returns it provides. Increased liquidity reduces trading costs and consequently brings more informed traders to option market, which improves its price informativeness. I also find empirical evidence in support of my theory. In particular, by regressing option-tostock trading ratios on past market returns and controls, I document an increase in option trading volume relatively to stock trading volume following bull markets. The effect is also present in cross-section as there is relatively more trading in options versus stocks for companies which shares had been on their way up. Additionally, by regressing stock market returns on past options’ volatility skews and controls, I find that option market predicts stock market better after equity market gains, suggesting increased informed trading. However, this effect is insignificant in cross-section of returns

Ab Initio Simulation of the IR Spectrum of Hydrated Kaolinite

The hydration of the basal surfaces of kaolinite is studied by theoretical methods. The cluster method was used to simulate the positions of atoms. The positions of the atoms of the basal surfaces of dry and hydrated minerals are optimized by minimizing the total energy in the Hartree–Fock approximation. The adsorption energies of water molecules were calculated taking into account the fourth-order correlation corrections of Møller–Plesset perturbation theory. The formation of the IR spectrum of kaolinite in the range of wave numbers 2500–4500 cm−1 is studied. The experimentally observed effect of the change in relative intensity and position of the band with a change in the moisture content of the sample is interpreted