Testing for granger-causality in the Finnish roundwood market

Summary

The Fed Model: International Analysis

The Fed model is a widely used market valuation model. It is often used only on market analysis of the S&P 500 index as a shorthand measure for the attractiveness of equity, and as a timing device for allocating funds between equity and bonds. The Fed model assumes a fixed relationship between bond yield and earnings yield. This relationship is often assumed to be true in market valuation. In this paper we test the Fed model from historical perspective on the European markets. The markets of the United States are also includedfor comparison. The purpose of the tests is to determine if the Fed model and the underlying assumptions come true on different markets. The various tests are made on time-series data ranging from the year 1973 to the end of the year 2008. The statistical methods used are regressions analysis, cointegration analysis and Granger causality. The empirical results do not give strong support for the Fed model. The underlying relationships assumed by the Fed model are statistically not valid in most of the markets examined and therefore the model is not valid in valuation purposes generally. The results vary between the different markets which gives reason to suspect the general use of the Fed model in different market conditions and in different markets.

Osakkeiden hintojen muutokset makrotalouden muuttujia ennakoivina tekijöinä 2001-2011: BRIC-maat , euroalue, Japani ja USA.

The aim of this thesis is to examine stock returns as predictive indicators to macroeconomic variables in BRIC-countries, Japan, USA and euro area. We picked to represent macroeconomic variables interest rate, inflation, currency, gross domestic product and industrial production. For the beginning we examined previous studies and theory about the subject. Hypothesis of this thesis were derived from the previous studies. To conduct the results we used tests such augmented Dickey-Fuller, Engle-Granger co-integration, Granger causality and lagged distribution model. According to results stock returns do predictive macroeconomic variables and specifically changes of GDP and industrial production. There were few evidences of stock returns predictive power of inflation.

The us subprime crisis and stock market integration: evidence from the BRICS

The aim of this study is to examine the level of stock market co-movement in the BRICS countries and three major industrialized countries (Japan, UK and USA). While analyzing the interdependence and integration of markets, two subsets are examined: before (2000 – 2007) and during the global financial crisis (2007-2011). Generally, interdependence across markets is likely to increase during a highly volatile period. This is problematic because if it were true, the main benefit of international diversification would be reduced at times when it is most needed. The results reveal the dominant role of the US financial markets over the examined time period. Empirical studies of this research paper indicate that cross-market linkages have become slightly stronger during the ongoing subprime crisis than before crisis. However, results also show that an investor may obtain some international diversification benefits by investing especially in the BRICS countries despite the fact of unstable economic condition and growing globalization.

Modelling Long-Run Relationship between Spot and Future Prices of Different Commodities

The purpose of this study is to investigate whether there exists any kind of relationship between the spot and future prices of the different commodities or not. Commodities like cocoa, coffee, crude oil, gold, natural gas and silver are considered from January 3, 2000 to December 31, 2012. For this purpose, ADF test and KPSS test are used in testing the stationarity whereas Johansen Cointegration test is used in testing the long-run relationship. Johansen co-integration test exhibits that there at least 5 co-integrating pairs out of 6 except crude oil. Moreover, the result of Granger Causality supports the fact that if two or more than two time series tend to be co-integrated there exists either uni-directional or bi-directional relationship. However, our results reveled that although there exists the co-integration between the variable, one might not granger causes another .VAR model is also used to measure the proportion of effects. These findings will help the derivative market and arbitragers in developing the strategies to gain the maximum profit in the financial market.

Dynamic linkages of real estate and stock markets in Finland

Traditionally real estate has been seen as a good diversification tool for a stock portfolio due to the lower return and volatility characteristics of real estate investments. However, the diversification benefits of a multi-asset portfolio depend on how the different asset classes co-move in the short- and long-run. As the asset classes are affected by the same macroeconomic factors, interrelationships limiting the diversification benefits could exist. This master’s thesis aims to identify such dynamic linkages in the Finnish real estate and stock markets. The results are beneficial for portfolio optimization tasks as well as for policy-making. The real estate industry can be divided into direct and securitized markets. In this thesis the direct market is depicted by the Finnish housing market index. The securitized market is proxied by the Finnish all-sectors securitized real estate index and by a European residential Real Estate Investment Trust index. The stock market is depicted by OMX Helsinki Cap index. Several macroeconomic variables are incorporated as well. The methodology of this thesis is based on the Vector Autoregressive (VAR) models. The long-run dynamic linkages are studied with Johansen’s cointegration tests and the short-run interrelationships are examined with Granger-causality tests. In addition, impulse response functions and forecast error variance decomposition analyses are used for robustness checks. The results show that long-run co-movement, or cointegration, did not exist between the housing and stock markets during the sample period. This indicates diversification benefits in the long-run. However, cointegration between the stock and securitized real estate markets was identified. This indicates limited diversification benefits and shows that the listed real estate market in Finland is not matured enough to be considered a separate market from the general stock market. Moreover, while securitized real estate was shown to cointegrate with the housing market in the long-run, the two markets are still too different in their characteristics to be used as substitutes in a multi-asset portfolio. This implies that the capital intensiveness of housing investments cannot be circumvented by investing in securitized real estate.

Foreign Direct Investment, Clean Development Mechanism, and Environmental Management: A case of Sub-Saharan Africa

This doctoral dissertation explores the contribution of environmental management practices, the so-called clean development mechanism (CDM) projects, and foreign direct investment (FDI) in achieving sustainable development in developing countries, particularly in Sub- Saharan Africa. Because the climate change caused by greenhouse gas emissions is one of the most serious global environmental challenges, the main focus is on the causal links between carbon dioxide (CO2) emissions, energy consumption, and economic development in Sub-Saharan Africa. In addition, the dissertation investigates the factors that have affected the distribution of CDM projects in developing countries and the relationships between FDI and other macroeconomic variables of interest. The main contribution of the dissertation is empirical. One of the publications uses crosssectional data and Tobit and Poisson regressions. Three of the studies use time-series data and vector autoregressive and vector error correction models, while two publications use panel data and panel data estimation methods. One of the publications uses thus both timeseries and panel data. The concept of Granger causality is utilized in four of the publications. The results indicate that there are significant differences in the Granger causality relationships between CO2 emissions, energy consumption, economic growth, and FDI in different countries. It appears also that the causality relationships change over time. Furthermore, the results support the environmental Kuznets curve hypothesis but only for some of the countries. As to CDM activities, past emission levels, institutional quality, and the size of the host country appear to be among the significant determinants of the distribution of CDM projects. FDI and exports are also found to be significant determinants of economic growth.

The Dynamic Relationship between Finnish Stock Market and Commodities

Since different stock markets have become more integrated during 2000s, investors need new asset classes in order to gain diversification benefits. Commodities have become popular to invest in and thus it is important to examine whether the investors should use commodities as a part for portfolio diversification. This master’s thesis examines the dynamic relationship between Finnish stock market and commodities. The methodology is based on Vector Autoregressive models (VAR). The long-run relationship between Finnish stock market and commodities is examined with Johansen cointegration while short-run relationship is examined with VAR models and Granger causality test. In addition, impulse response test and forecast error variance decomposition are employed to strengthen the results of short-run relationship. The dynamic relationships might change under different market conditions. Thus, the sample period is divided into two sub-samples in order to reveal whether the dynamic relationship varies under different market conditions. The results show that Finnish stock market has stable long-run relationship with industrial metals, indicating that there would not be diversification benefits among the industrial metals. The long-run relationship between Finnish stock market and energy commodities is not as stable as the long-run relationship between Finnish stock market and industrial metals. Long-run relationship was found in the full sample period and first sub-sample which indicate less room for diversification. However, the long-run relationship disappeared in the second sub-sample which indicates diversification benefits. Long-run relationship between Finnish stock market and agricultural commodities was not found in the full sample period which indicates diversification benefits between the variables. However, long-run relationship was found from both sub-samples. The best diversification benefits would be achieved if investor invested in precious metals. No long-run relationship was found from either sample. In the full sample period OMX Helsinki had short-run relationship with most of the energy commodities and industrial metals and the causality was mostly running from equities to commodities. During the first sub period the number of short-run relationships and causality shrunk but during the crisis period the number of short-run relationships and causality increased. The most notable result found was unidirectional causality from gold to OMX Helsinki during the crisis period.

Monte Carlo Methods in Parameter Estimation of Nonlinear Models

Yksi keskeisimmistä tehtävistä matemaattisten mallien tilastollisessa analyysissä on mallien tuntemattomien parametrien estimointi. Tässä diplomityössä ollaan kiinnostuneita tuntemattomien parametrien jakaumista ja niiden muodostamiseen sopivista numeerisista menetelmistä, etenkin tapauksissa, joissa malli on epälineaarinen parametrien suhteen. Erilaisten numeeristen menetelmien osalta pääpaino on Markovin ketju Monte Carlo -menetelmissä (MCMC). Nämä laskentaintensiiviset menetelmät ovat viime aikoina kasvattaneet suosiotaan lähinnä kasvaneen laskentatehon vuoksi. Sekä Markovin ketjujen että Monte Carlo -simuloinnin teoriaa on esitelty työssä siinä määrin, että menetelmien toimivuus saadaan perusteltua. Viime aikoina kehitetyistä menetelmistä tarkastellaan etenkin adaptiivisia MCMC menetelmiä. Työn lähestymistapa on käytännönläheinen ja erilaisia MCMC -menetelmien toteutukseen liittyviä asioita korostetaan. Työn empiirisessä osuudessa tarkastellaan viiden esimerkkimallin tuntemattomien parametrien jakaumaa käyttäen hyväksi teoriaosassa esitettyjä menetelmiä. Mallit kuvaavat kemiallisia reaktioita ja kuvataan tavallisina differentiaaliyhtälöryhminä. Mallit on kerätty kemisteiltä Lappeenrannan teknillisestä yliopistosta ja Åbo Akademista, Turusta.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Initialization of Continuous Nonlinear Models Using Extended Kalman Filter

The two main objectives of Bayesian inference are to estimate parameters and states. In this thesis, we are interested in how this can be done in the framework of state-space models when there is a complete or partial lack of knowledge of the initial state of a continuous nonlinear dynamical system. In literature, similar problems have been referred to as diffuse initialization problems. This is achieved first by extending the previously developed diffuse initialization Kalman filtering techniques for discrete systems to continuous systems. The second objective is to estimate parameters using MCMC methods with a likelihood function obtained from the diffuse filtering. These methods are tried on the data collected from the 1995 Ebola outbreak in Kikwit, DRC in order to estimate the parameters of the system.