LTMS magistritööd -- Master's theses

Selle kollektsiooni püsiv URIhttps://hdl.handle.net/10062/50402

Sirvi

Nüüd näidatakse 1 - 9 9

Analysis of the links among FDI, GDP, oil and gas prices in developed, developing and resource-dependent countries
(2023) Hasanov, Ali; Raus, Toomas, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Matemaatika ja statistika instituut
This thesis studies the possible causal relationships among foreign direct investment (FDI), oil and gas prices, and gross domestic product (GDP) growth in 3 different groups of countries for the period of 1971-2021. Many unsuccessful countries in the world cannot efficiently attract their resources for economic growth. Sometimes resource-rich countries cannot maximize the benefits of the trade of natural sources. For example, many oil-dependent countries still fail to diversify their economy, and state incomes fluctuate as oil prices change. Moreover, in this thesis, some developed and successful developing countries are studied in order to compare their experience with resource-dependent countries. The effects of oil/gas prices on GDP and FDI, also the relationship between GDP and FDI are studied in selected 5 resourcedependent countries, 5 developed countries, and 5 developing countries using the Granger causality test and vector autoregressive (VAR) model. In general, this thesis asserts that an increase in commodity prices can negatively affect resource-dependent countries.
Äritsükli indeksi hindamine Kalmani filtriga
(2019) Riik, Ravel; Kangro, Raul, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Matemaatika ja statistika instituut
Vastava magistritöö eesmärk on rakendada Kalmani filtrit ning tuletada vastava meetodiga äritsükli indeks. Meetodi sobivust testitakse kahe erineva makromajanduse aegrea abil.
Clustering financial time series
(2020) Potikyan, Nshan; Kangro, Raul, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Matemaatika ja statistika instituut
Time series clustering is heavily based on choosing a proper dissimilarity measure between a pair of time series. We present several dissimilarity measures and use two synthetic datasets to evaluate their performance. Hierarchical clustering and network analysis methods are used to perform cluster analysis on stock price time series of 594 US-based companies in order to verify whether stock prices of companies operating within an industry have common uctuations. The results of the thesis show that some companies within the same industry do form clusters, while others are relatively scattered.
Forecasting time series with artificial neural networks
(2019) Peedosk, Hele-Liis; Raus, Toomas, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Matemaatika ja statistika instituut
Having accurate time series forecasts helps to be prepared for upcoming events. As many real world time series have nonlinear and irregular behavior, traditional approaches may be lacking performance. A suitable alternative method is artificial neural network models, that can achieve high accuracy in various difficult tasks. The objective of given thesis is to give theoretical and practical guidelines for applying neural networks in time series forecasting with packages h2o and neuralnet for statistical programming language R, and library Keras for programming language Python. An empirical study was conducted on five different datasets to compare multilayer perceptron model performance with long short-term memory model, and iterative, direct and multi-neural network modeling strategies with each other. The performance of neural network models were compared with liner baseline models to expose whether the results have any practical gain. When comparing the network structures, the results indicate the superiority of long short-term memory models. Furthermore, long short-term memory models offered improvement over linear baseline model almost in case of all datasets. Based on these results, neural networks proved to have great performance for time series forecasting, and should be considered as an alternative to linear models.
Fractional ARIMA processes and applications in modeling financial time series
(Tartu Ülikool, 2017) Guskova, Kseniia; Kangro, Raul, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Matemaatika ja statistika instituut
Time-series analysis is widely used in forecasting future trends on financial markets. There is a family of models which represent the property of long memory. In this thesis we aim at introducing fractionally differentiated ARIMA model in forecasting future returns of market index. In theoretical part the description of long-memory processes and statistical testing of given data are provided. In practical part we fit the models without differencing, with differencing and with fractional differencing to the market data and compare its forecast accuracy with observed values.
Haiglapatsientide arvu prognoosimine ARIMA tüüpi mudelitega
(2019) Soll, Hanna-Liisa; Fischer, Krista, juhendaja; Kangro, Raul, juhendaja; Tikk, Merje, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Matemaatika ja statistika instituut
Käesoleva magistritöö eesmärk oli prognoosida igakuist Tartu Ülikooli Kliinikumi haiglapatsientide arvu neljal ravierialal kuni üks aasta ette. Vaadeldi otorinolarüngoloogia, pediaatria, sisehaiguste ning üld- ja plastikakirurgia erialasid. Põhieesmärgiks oli leida sobivad ühemõõtmelised ARIMA tüüpi prognoosmudelid. Lisaks prooviti prognoose täpsustada, kasutades lineaarset regressiooni ARIMA tüüpi vigadega, võttes regressoriks Eesti Haigekassa poolt aastaks tellitavate ravijuhtude arvud vastavatel erialadel. Iga eriala jaoks valiti parim ühe- ja mitmemõõtmeline mudel ning võrreldi nende täpsust aastaste prognooside ruutkeskmiste vigade põhjal. Saadud mudelite abil leiti prognoosid valimiväliseks aastaks 2018 koos prognoosiintervallidega.
Mahuriski hinnastamine elektriturul
(2018) Kruusmann, Laura; Kangro, Raul, juhendaja; Pungas, Taavi, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Matemaatika ja statistika instituut
Magistritöö eesmärk on leida meetod mahuriskist tuleneva hinnalisa leidmiseks elektriturul. Töös on analüüsitud mahuriski olemust ning selle hinnastamiseks on tuletatud valem minimaalse fikseeritava hinna leidmiseks teatud kliendi korral. Saadud valemi rakendamist on demonstreeritud erinevatel eeldustel elektrituru kohta.
Modelling volume of savings deposits
(2022) Haviko, Enelin; Pärna, Kalev, juhendaja; Diamant, Jonathan, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Matemaatika ja statistika instituut
The objective of this master’s thesis is to find an appropriate time series model to forecast the volume of private customers’ savings deposits of one undisclosed Swedish financial institution. Models such as Holt, Holt-Winters, ARIMA and ARIMAX are fitted to the data under analysis. The best performing model is ARIMA showing approximately 20% lower error measures during the out-of-sample test period compared to the best ARIMAX models.
Nõudmiseni hoiuse intressimääradele ARIMAX tüüpi mudelite leidmine turumäärade abil
(2022) Tammesoo, Laura Anna; Kangro, Raul, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Matemaatika ja statistika instituut
Töö eesmärk on uurida nõudmiseni hoiusele pakutavate intresside sõltumist 6-kuulisest euriborist ja 6-kuulise euribori swap’i määrast. Selleks uuritakse ARIMAX tüüpi mudelite moodustamist ja kointegratsiooni hoiuse intresside ja turumäärade vahel. Lisaks moodustatakse mudelid naturaallogaritmi ja hüperboolse siinuse pöördfunktsiooni abil transformeeritud aegridadele. Töö on motiveeritud krediidiasutuste vajadusest intressiriski hinnata, kus intressirisk on ettevõtte risk saada kahju intressimäärade muutumisest. Hoiuse intresside prognoosimine turumäärade abil on oluline sisend intressiriski hindamise protsessi.

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Sirvi LTMS magistritööd -- Master's theses Märksõna "aegridade analüüs" järgi