Stock Option Compensation in Finland: An Analysis of Economic Determinants, Contracting Frequency, and Design

This paper addresses several questions in the compensation literature by examining stock option compensation practices of Finnish firms. First, the results indicate that principal-agent theory succeeds quite well in predicting the use of stock options. Proxies for monitoring costs, growth opportunities, ownership structure, and risk are found to determine the use of incentives consistent with theory. Furthermore, the paper examines whether determinants of stock options targeted to top management differ from determinants of broad-based stock option plans. Some evidence is found that factors driving these two types of incentives differ. Second, the results reveal that systematic risk significantly increases the likelihood that firms adopt stock option plans, whereas total firm risk and unsystematic risk do not seem to affect this decision. Third, the results show that growth opportunities are related to time-dimensional contracting frequency, consistent with the argument that incentive levels deviate more rapidly from optimum in firms with high growth opportunities. Finally, the results suggest that vesting schedules are decreasing in financial leverage, and that contract maturity is decreasing in firm focus. In addition, both vesting schedules and contract maturity tend to be longer in firms involving state ownership.

Nonlinear mixture autoregressive model: economic applications

The aim of this dissertation is to model economic variables by a mixture autoregressive (MAR) model. The MAR model is a generalization of linear autoregressive (AR) model. The MAR -model consists of K linear autoregressive components. At any given point of time one of these autoregressive components is randomly selected to generate a new observation for the time series. The mixture probability can be constant over time or a direct function of a some observable variable. Many economic time series contain properties which cannot be described by linear and stationary time series models. A nonlinear autoregressive model such as MAR model can a plausible alternative in the case of these time series. In this dissertation the MAR model is used to model stock market bubbles and a relationship between inflation and the interest rate. In the case of the inflation rate we arrived at the MAR model where inflation process is less mean reverting in the case of high inflation than in the case of normal inflation. The interest rate move one-for-one with expected inflation. We use the data from the Livingston survey as a proxy for inflation expectations. We have found that survey inflation expectations are not perfectly rational. According to our results information stickiness play an important role in the expectation formation. We also found that survey participants have a tendency to underestimate inflation. A MAR model has also used to model stock market bubbles and crashes. This model has two regimes: the bubble regime and the error correction regime. In the error correction regime price depends on a fundamental factor, the price-dividend ratio, and in the bubble regime, price is independent of fundamentals. In this model a stock market crash is usually caused by a regime switch from a bubble regime to an error-correction regime. According to our empirical results bubbles are related to a low inflation. Our model also imply that bubbles have influences investment return distribution in both short and long run.

Mechanisms of variability and predictability of the tropical coupled ocean-atmosphere system

A conceptual model is proposed to explain the observed aperiodicity in the short term climate fluctuations of the tropical coupled ocean-atmosphere system. This is based on the evidence presented here that the tropical coupled ocean-atmosphere system sustains a low frequency inter-annual mode and a host of higher frequency intra-seasonal unstable modes. At long wavelengths, the low frequency mode is dominant while at short wavelengths, the high frequency modes are dominant resulting in the co-existence of a long wave low frequency mode with some short wave intra-seasonal modes in the tropical coupled system. It is argued that due to its long wavelength, the low frequency mode would behave like a linear oscillator while the higher frequency short wave modes would be nonlinear. The conceptual model envisages that an interaction between the low frequency linear oscillator and the high frequency nonlinear oscillations results in the observed aperiodicity of the tropical coupled system. This is illustrated by representing the higher frequency intra-seasonal oscillations by a nonlinear low order model which is then coupled to a linear oscillator with a periodicity of four years. The physical mechanism resulting in the aperiodicity in the low frequency oscillations and implications of these results on the predictability of the coupled system are discussed.

Lyapunov exponents and predictability of the tropical coupled ocean-atmosphere system

It has recently been proposed that the broad spectrum of interannual variability in the tropics with a peak around four years results from an interaction between the linear low-frequency oscillatory mode of the coupled system and the nonlinear higher-frequency modes of the system. In this study we determine the Lyapunov exponents of the conceptual model consisting of a nonlinear low-order model coupled to a linear oscillator for various values of the coupling constants.