Essays on Risk Modeling. Applications to Portfolio and Risk Management (summary section only)

In this thesis we deal with the concept of risk. The objective is to bring together and conclude on some normative information regarding quantitative portfolio management and risk assessment. The first essay concentrates on return dependency. We propose an algorithm for classifying markets into rising and falling. Given the algorithm, we derive a statistic: the Trend Switch Probability, for detection of long-term return dependency in the first moment. The empirical results suggest that the Trend Switch Probability is robust over various volatility specifications. The serial dependency in bear and bull markets behaves however differently. It is strongly positive in rising market whereas in bear markets it is closer to a random walk. Realized volatility, a technique for estimating volatility from high frequency data, is investigated in essays two and three. In the second essay we find, when measuring realized variance on a set of German stocks, that the second moment dependency structure is highly unstable and changes randomly. Results also suggest that volatility is non-stationary from time to time. In the third essay we examine the impact from market microstructure on the error between estimated realized volatility and the volatility of the underlying process. With simulation-based techniques we show that autocorrelation in returns leads to biased variance estimates and that lower sampling frequency and non-constant volatility increases the error variation between the estimated variance and the variance of the underlying process. From these essays we can conclude that volatility is not easily estimated, even from high frequency data. It is neither very well behaved in terms of stability nor dependency over time. Based on these observations, we would recommend the use of simple, transparent methods that are likely to be more robust over differing volatility regimes than models with a complex parameter universe. In analyzing long-term return dependency in the first moment we find that the Trend Switch Probability is a robust estimator. This is an interesting area for further research, with important implications for active asset allocation.

Modeling Nonlinearities and Asymmetries in Asset Pricing

Financial time series tend to behave in a manner that is not directly drawn from a normal distribution. Asymmetries and nonlinearities are usually seen and these characteristics need to be taken into account. To make forecasts and predictions of future return and risk is rather complicated. The existing models for predicting risk are of help to a certain degree, but the complexity in financial time series data makes it difficult. The introduction of nonlinearities and asymmetries for the purpose of better models and forecasts regarding both mean and variance is supported by the essays in this dissertation. Linear and nonlinear models are consequently introduced in this dissertation. The advantages of nonlinear models are that they can take into account asymmetries. Asymmetric patterns usually mean that large negative returns appear more often than positive returns of the same magnitude. This goes hand in hand with the fact that negative returns are associated with higher risk than in the case where positive returns of the same magnitude are observed. The reason why these models are of high importance lies in the ability to make the best possible estimations and predictions of future returns and for predicting risk.

Examining and Modeling the Dynamics of the Volatility Surface

The objective of this paper is to investigate and model the characteristics of the prevailing volatility smiles and surfaces on the DAX- and ESX-index options markets. Continuing on the trend of Implied Volatility Functions, the Standardized Log-Moneyness model is introduced and fitted to historical data. The model replaces the constant volatility parameter of the Black & Scholes pricing model with a matrix of volatilities with respect to moneyness and maturity and is tested out-of-sample. Considering the dynamics, the results show support for the hypotheses put forward in this study, implying that the smile increases in magnitude when maturity and ATM volatility decreases and that there is a negative/positive correlation between a change in the underlying asset/time to maturity and implied ATM volatility. Further, the Standardized Log-Moneyness model indicates an improvement to pricing accuracy compared to previous Implied Volatility Function models, however indicating that the parameters of the models are to be re-estimated continuously for the models to fully capture the changing dynamics of the volatility smiles.

Chemicals risk modeling : toxic effect risks imposed on aquatic organisms by industrial activity

Yhteenveto: Kemikaalien teollisesta käsittelystä vesieliöille aiheutuvien riskien arviointi mallin avulla.

Mercury in Finnish forest lakes and reservoirs : anthropogenic contribution to the load and accumulation in fish

Yhteenveto: Elohopea Suomen metsäjärvissä ja tekoaltaissa: ihmisen vaikutus kuormitukseen ja pitoisuuksiin kaloissa.

The roles of nitrification and nitrate reduction pathways in nitrogen cycling of Baltic Sea

The Baltic Sea is one of the largest brackish water bodies in the world. Primary production in the Baltic Sea is limited by nitrogen (N) availability with the exception of river outlets and the northernmost phosphorus limited basin. The excess human induced N load from the drainage basin has caused severe eutrophication of the sea. The excess N loads can be mitigated by microbe mediated natural N removal processes that are found in the oxic-anoxic interfaces in sediments and water column redoxclines. Such interfaces allow the close coupling between the oxic nitrification process, and anoxic denitrification and anaerobic ammonium oxidation (anammox) processes that lead to the formation of molecular nitrogen gas. These processes are governed by various environmental parameters. The effects of these parameters on N processes were investigated in the northern Baltic Sea sediments. During summer months when the sediment organic content is at its highest, nitrification and denitrification reach their maximum rates. However, nitrification had no excess potential, which was probably because of high competition for molecular oxygen (O2) between heterotrophic and nitrification microbes. Subsequently, the limited nitrate (NO3-) availability inhibited denitrification. In fall, winter and spring, nitrification was limited by ammonium availability and denitrification limited by the availability of organic carbon and occasionally by NO3-. Anaerobic ammonium oxidation (anammox) was not an important N removal process in the northern Baltic Sea. Modeling studies suggest that when hypoxia expands in the Baltic Sea, N removal intensifies. However, the results of this study suggest the opposite because bottom water hypoxia (O2< 2 ml l-1) decreased the denitrification rates in sediments. Moreover, N was recycled by the dissimilatory nitrate reduction to ammonium (DNRA) process instead of being removed from the water ecosystem. High N removal potentials were found in the anoxic water column in the deep basins of the Baltic Proper. However, the N removal in the water column appeared to be limited by low substrate availability, because the water at the depths at which the substrate producing nitrification process occurred, rarely mix with the water at the depths at which N removal processes were found. Overall, the natural N removal capacity of the northern Baltic Sea decreased compared to values measured in mid 1990s and early 2000. The reason for this appears to be increasing hypoxia.

Effect of drainage basin characteristics on the diffuse load of phosphorus and nitrogen.

Tiivistelmä: Valuma-alueen vaikutus fosforin ja typen hajakuormitukseen.