Liquidity Risk in the Finnish Stock Market

This study explores the pricing of liquidity risk and its effect on stock returns in the Finnish stock market. In addition to that, it investigates whether there is a trend in liquidity risk. Finally, it analyzes whether the two chosen liquidity measures provide different results. The data consists of all the common shares listed in the Finnish stock market during the period of 1/1997–7/2015. To examine whether liquidity risk affects stock returns in the Finnish stock market, this study utilizes a conditional version of liquidity-adjusted capital asset pricing model (LCAPM) by Acharya and Pedersen (2005). Two recently proposed illiquidity measures – PQS and AdjILLIQ – are used in the empirical estimation to see whether there are differences in the results between the measures. The time-varying conditional liquidity risks are estimated by using a multivariate DCC-GARCH model, while the pricing of the liquidity risk is conducted by applying fixed effect panel regression. The results imply that investors in the Finnish stock market are willing to pay a premium to hedge from wealth shocks and having liquid assets during the declined market liquidity. However, investors are not willing to pay a premium for stocks with higher returns during illiquid markets. The total annualized illiquidity premiums found in the Finnish stock market are 1.77% and 1.04%, based on the PQS and AdjILLIQ measures, respectively. The study also shows that liquidity risk does not exhibit decreasing trend, and investors should consider liquidity risk in their portfolio diversification in the Finnish stock market.

Suomen taloussuhdanteiden kansainvälinen yhteisvaihtelu

Taloussuhdanteiden yhteisvaihtelun tutkimus on eräs taloustieteiden vanhimmista tutkimusaloista. Finanssikriisi ja euroalueen kohtaamat talousvaikeudet ovat kuitenkin nostaneet aiheen jälleen hyvin ajankohtaiseksi. Kuluneiden kahdenkymmenen vuoden aikana tutkimusalueesta on muodostunut erittäin laaja lukuisine näkökulmineen ja debatteineen. Tutkielman aiheena on Suomen taloussuhdanteiden kansainvälinen yhteisvaihtelu valittujen vertailumaiden kanssa. Vertailumaat ovat Ruotsi, Norja, Tanska, Saksa, Ranska, Iso-Britannia ja Yhdysvallat. Tutkielmaan valitut taloussuhdannetta kuvaavat muuttujat ovat reaalinen bruttokansantuote, yksityinen kokonaiskulutus ja teollisuustuotantoindeksi. Aineisto on kerätty Lappeenrannan tiedekirjaston Nelli-portaalin OECD iLibrary-tietokannasta ja se kattaa aikajakson 1960 Q1- 2014 Q4. Maakohtainen taloussuhdanne operationalisoidaan laskemalla ensimmäinen logaritminen differenssi, joka edustaa perinteistä reaalisuhdanneteoreettisen koulukunnan näkemystä taloussuhdanteesta. Tutkielman näkökulmaksi valitaan yhden maan näkökulma, joka on hieman harvinaisempi näkökulma verrattuna laajempiin alueellisiin näkökulmiin. Tutkimusmenetelminä käytetään Pearsonin korrelaatiokerrointa, Engle-Granger- sekä Johansenin yhteisintegroituvuustestejä ja VAR-GARCH-BEKK –mallilla laskettua dynaamista korrelaatiota, jotka lasketaan Suomen ja vertailumaiden välille maapareittain. Tuloksia tulkitaan suomalaisen vientiä vertailumaihin suunnittelevan yrityksen näkökulmasta. Tutkielman tulosten perusteella Engle-Grangerin menetelmällä laskettu samanaikainen yhteisintegroituvuus Suomen ja vertailumaiden välillä on epätodennäköistä. Kun yhteisintegroituvuuden annetaan riippua myös viiveistä, saadaan Johansenin menetelmällä yhteisintegroituvuus Suomen ja Yhdysvaltojen välille reaalisessa bruttokansantuotteessa, Suomen ja Saksan, Suomen ja Ranskan sekä Suomen ja Yhdysvaltojen välille yksityisessä kokonaiskulutuksessa sekä Suomen ja Norjan välille teollisuustuotantoindeksissä. Tulosten tulkintaa vaikeuttavat niiden malliriippuvuus ja informaatiokriteerien toisistaan poikkeavat mallisuositukset, joten yhteisintegroituvuus on mahdollinen myös muiden maaparien kohdalla. Dynaamisten korrelaatiokuvaajien perusteella maaparien välisen yhteisvaihtelun voimakkuus muuttuu ajan mukana. Finanssikriisin aikana kokonaistuotannossa on havaittavissa korkeampi korrelaatio, mutta korrelaatio palaa sen jälkeen perustasolleen. Kokonaiskulutuksen korrelaatio on kokonaistuotantoa alhaisempi ja pitemmissä aikajaksoissa vaihtelevaa.

Welding time models for cost calculations in the early stages of the design process

It is generally accepted that between 70 and 80% of manufacturing costs can be attributed to design. Nevertheless, it is difficult for the designer to estimate manufacturing costs accurately, especially when alternative constructions are compared at the conceptual design phase, because of the lack of cost information and appropriate tools. In general, previous reports concerning optimisation of a welded structure have used the mass of the product as the basis for the cost comparison. However, it can easily be shown using a simple example that the use of product mass as the sole manufacturing cost estimator is unsatisfactory. This study describes a method of formulating welding time models for cost calculation, and presents the results of the models for particular sections, based on typical costs in Finland. This was achieved by collecting information concerning welded products from different companies. The data included 71 different welded assemblies taken from the mechanical engineering and construction industries. The welded assemblies contained in total 1 589 welded parts, 4 257 separate welds, and a total welded length of 3 188 metres. The data were modelled for statistical calculations, and models of welding time were derived by using linear regression analysis. Themodels were tested by using appropriate statistical methods, and were found to be accurate. General welding time models have been developed, valid for welding in Finland, as well as specific, more accurate models for particular companies. The models are presented in such a form that they can be used easily by a designer, enabling the cost calculation to be automated.

Regime Switching Models for Electricity Time Series that Capture Fat Tailed Distributions

In the power market, electricity prices play an important role at the economic level. The behavior of a price trend usually known as a structural break may change over time in terms of its mean value, its volatility, or it may change for a period of time before reverting back to its original behavior or switching to another style of behavior, and the latter is typically termed a regime shift or regime switch. Our task in this thesis is to develop an electricity price time series model that captures fat tailed distributions which can explain this behavior and analyze it for better understanding. For NordPool data used, the obtained Markov Regime-Switching model operates on two regimes: regular and non-regular. Three criteria have been considered price difference criterion, capacity/flow difference criterion and spikes in Finland criterion. The suitability of GARCH modeling to simulate multi-regime modeling is also studied.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.