Tiering Effects in Third-party Logistics: A First-tier Buyer Perspective

This doctoral dissertation takes a buy side perspective to third-party logistics (3PL) providers’ service tiering by applying a linear serial dyadic view to transactions. It takes its point of departure not only from the unalterable focus on the dyad levels as units of analysis and how to manage them, but also the characteristics both creating and determining purposeful conditions for a longer duration. A conceptual framework is proposed and evaluated on its ability to capture logistics service buyers’ perceptions of service tiering. The problem discussed is in the theoretical context of logistics and reflects value appropriation, power dependencies, visibility in linear serial dyads, a movement towards the more market governed modes of transactions (i.e. service tiering) and buyers’ risk perception of broader utilisation of the logistics services market. Service tiering, in a supply chain setting, with the lack of multilateral agreements between supply chain members, is new. The deductive research approach applied, in which theoretically based propositions are empirically tested with quantitative and qualitative data, provides new insight into (contractual) transactions in 3PL. The study findings imply that the understanding of power dependencies and supply chain dynamics in a 3PL context is still in its infancy. The issues found include separation of service responsibilities, supply chain visibility, price-making behaviour and supply chain strategies under changing circumstances or influence of non-immediate supply chain actors. Understanding (or failing to understand) these issues may mean remarkable implications for the industry. Thus, the contingencies may trigger more open-book policies, larger liability scope of 3PL service providers or insourcing of critical logistics activities from the first-tier buyer core business and customer service perspectives. In addition, a sufficient understanding of the issues surrounding service tiering enables proactive responses to devise appropriate supply chain strategies. The author concludes that qualitative research designs, facilitating data collection on multiple supply chain actors, may capture and increase understanding of the impact of broader supply chain strategies. This would enable pattern-matching through an examination of two or more sides of exchange transactions to measure relational symmetries across linear serial dyads. Indeed, the performance of the firm depends not only on how efficiently it cooperates with its partners, but also on how well exchange partners cooperate with an organisation’s own business.

Childhood otitis media - risk factors and surgical management

Background: Otitis media (OM) is one of the most common childhood diseases. Approximately every third child suffers from recurrent acute otitis media (RAOM), and 5% of all children have persistent middle ear effusion for months during their childhood. Despite numerous studies on the prevention and treatment of OM during the past decades, its management remains challenging and controversial. In this study, the effect of adenoidectomy on the risk for OM, the potential risk factors influencing the development of OM and the frequency of asthma among otitis-prone children were investigated. Subjects and methods: One prospective randomized trial and two retrospective studies were conducted. In the prospective trial, 217 children with RAOM or chronic otitis media with effusion (COME) were randomized to have tympanostomy with or without adenoidectomy. The age of the children at recruitment was between 1 and 4 years. RAOM was defined as having at least 3 episodes of AOM during the last 6 months or at least 5 episodes of AOM during the last 12 months. COME was defined as having persistent middle ear effusion for 2-3 months. The children were followed up for one year. In the first retrospective study, the frequency of childhood infections and allergy was evaluated by a questionnaire among 819 individuals. In the second retrospective study, data of asthma diagnosis were analysed from hospital discharge records of 1616 children who underwent adenoidectomy or had probing of the nasolacrimal duct. Results: In the prospective randomized study, adenoidectomy had no beneficial effect on the prevention of subsequent episodes of AOM. Parental smoking was found to be a significant risk factor for OM even after the insertion of tympanostomy tubes. The frequencies of exposure to tobacco smoke and day-care attendance at the time of randomization were similar among children with RAOM and COME. However, the frequencies of allergy to animal dust and pollen and parental asthma were lower among children with COME than those with RAOM. The questionnaire survey and the hospital discharge data revealed that children who had frequent episodes of OM had an increased risk for asthma. Conclusions: The first surgical intervention to treat an otitis-prone child younger than 4 years should not include adenoidectomy. Interventions to stop parental smoking could significantly reduce the risk for childhood RAOM. Whether an otitis-prone child develops COME or RAOM, seems to be influenced by genetic predisposition more strongly than by environmental risk factors. Children who suffer from repeated upper respiratory tract infections, like OM, may be at increased risk for developing asthma.

Modeling and Forecasting Implied Volatility: Implications for Trading, Pricing, and Risk Management

Modeling and forecasting of implied volatility (IV) is important to both practitioners and academics, especially in trading, pricing, hedging, and risk management activities, all of which require an accurate volatility. However, it has become challenging since the 1987 stock market crash, as implied volatilities (IVs) recovered from stock index options present two patterns: volatility smirk(skew) and volatility term-structure, if the two are examined at the same time, presents a rich implied volatility surface (IVS). This implies that the assumptions behind the Black-Scholes (1973) model do not hold empirically, as asset prices are mostly influenced by many underlying risk factors. This thesis, consists of four essays, is modeling and forecasting implied volatility in the presence of options markets’ empirical regularities. The first essay is modeling the dynamics IVS, it extends the Dumas, Fleming and Whaley (DFW) (1998) framework; for instance, using moneyness in the implied forward price and OTM put-call options on the FTSE100 index, a nonlinear optimization is used to estimate different models and thereby produce rich, smooth IVSs. Here, the constant-volatility model fails to explain the variations in the rich IVS. Next, it is found that three factors can explain about 69-88% of the variance in the IVS. Of this, on average, 56% is explained by the level factor, 15% by the term-structure factor, and the additional 7% by the jump-fear factor. The second essay proposes a quantile regression model for modeling contemporaneous asymmetric return-volatility relationship, which is the generalization of Hibbert et al. (2008) model. The results show strong negative asymmetric return-volatility relationship at various quantiles of IV distributions, it is monotonically increasing when moving from the median quantile to the uppermost quantile (i.e., 95%); therefore, OLS underestimates this relationship at upper quantiles. Additionally, the asymmetric relationship is more pronounced with the smirk (skew) adjusted volatility index measure in comparison to the old volatility index measure. Nonetheless, the volatility indices are ranked in terms of asymmetric volatility as follows: VIX, VSTOXX, VDAX, and VXN. The third essay examines the information content of the new-VDAX volatility index to forecast daily Value-at-Risk (VaR) estimates and compares its VaR forecasts with the forecasts of the Filtered Historical Simulation and RiskMetrics. All daily VaR models are then backtested from 1992-2009 using unconditional, independence, conditional coverage, and quadratic-score tests. It is found that the VDAX subsumes almost all information required for the volatility of daily VaR forecasts for a portfolio of the DAX30 index; implied-VaR models outperform all other VaR models. The fourth essay models the risk factors driving the swaption IVs. It is found that three factors can explain 94-97% of the variation in each of the EUR, USD, and GBP swaption IVs. There are significant linkages across factors, and bi-directional causality is at work between the factors implied by EUR and USD swaption IVs. Furthermore, the factors implied by EUR and USD IVs respond to each others’ shocks; however, surprisingly, GBP does not affect them. Second, the string market model calibration results show it can efficiently reproduce (or forecast) the volatility surface for each of the swaptions markets.

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.

The potential of microbial ecological indicators to guide ecosophisticated management of hydrocarbon-contaminated soils

Soil is an unrenewable natural resource under increasing anthropogenic pressure. One of the main threats to soils, compromising their ability to provide us with the goods and ecosystem services we expect, is pollution. Oil hydrocarbons are the most common soil contaminants, and they disturb not just the biota but also the physicochemical properties of soils. Indigenous soil micro-organisms respond rapidly to changes in the soil ecosystem, and are chronically in direct contact with the hydrophobic pollutants on the soil surfaces. Soil microbial variables could thus serve as an intrinsically relevant indicator of soil quality, to be used in the ecological risk assessment of contaminated and remediated soils. Two contrasting studies were designed to investigate soil microbial ecological responses to hydrocarbons, together with parallel changes in soil physicochemical and ecotoxicological properties. The aim was to identify quantitative or qualitative microbiological variables that would be practicable and broadly applicable for the assessment of the quality and restoration of oil-polluted soil. Soil bacteria commonly react on hydrocarbons as a beneficial substrate, which lead to a positive response in the classical microbiological soil quality indicators; negative impacts were accurately reflected only after severe contamination. Hydrocarbon contaminants become less bioavailable due to weathering processes, and their potentially toxic effects decrease faster than the total concentration. Indigenous hydrocarbon degrader bacteria, naturally present in any terrestrial environment, use specific mechanisms to improve access to the hydrocarbon molecules adsorbed on soil surfaces. Thus when contaminants are unavailable even to the specialised degraders, they should pose no hazard to other biota either. Change in the ratio of hydrocarbon degrader numbers to total microbes was detected to predictably indicate pollutant effects and bioavailability. Also bacterial diversity, a qualitative community characteristic, decreased as a response to hydrocarbons. Stabilisation of community evenness, and community structure that reflected clean reference soil, indicated community recovery. If long-term temporal monitoring is difficult and appropriate clean reference soil unavailable, such comparison could possibly be based on DNA-based community analysis, reflecting past+present, and RNA-based community analysis, showing exclusively present conditions. Microbial ecological indicators cannot replace chemical oil analyses, but they are theoretically relevant and operationally practicable additional tools for ecological risk assessment. As such, they can guide ecologically informed and sustainable ecosophisticated management of oil-contaminated lands.