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Версия для слабовидящихЛичный кабинет сотрудника ВШЭПоиск

Моделирование и прогнозирование одномерных и многомерных временных рядов с использованием данных Google и копулModelling and forecasting univariate and multivariate time series using Google data and copulae

Соискатель:Фантаццини Деан
Члены комитета: Пересецкий Анатолий Абрамович (НИУ ВШЭ, д.э.н., председатель комитета), Карминский Александр Маркович (НИУ ВШЭ, д.э.н., д.т.н., член комитета), Кузнецов Сергей Евгеньевич (Университет Колорадо, Боулдер (США), д.ф.-м.н., член комитета), Магнус Ян (Амстердамский свободный университет, PhD, член комитета), Шоорс Коэн Жан (НИУ ВШЭ, PhD, член комитета)
Диссертация принята к предварительному рассмотрению:12/30/2019
Диссертация принята к защите:2/4/2020
Дисс. совет:Совет по экономике
Дата защиты:5/29/2020
The quick development of the internet and information technology (IT) worldwide has given access to a large amount of data, which are usually known as “big data". One of the main tools that can be used to analyze big data is a search engine, which is often considered the first step in the consumer decision-making process, to understand social dynamics and to make better predictions. In this regard, Google is the search engine with the largest market share worldwide (90% in 2018) and the analysis of its search data has been one the most important and well-known examples of the use of big data. In 2006, it launched a tool named Google Trends which shows how frequently a particular keyword or a topic are searched online in a specific region, at a specific period of time, and also in different languages. The advent of these large datasets further stimulated the interest in developing multivariate models able to consider departures from the assumption of normality and to be computationally tractable: Copula models can deal with both these two issues. The theory of copulas dates back to Hoeffding (1940) and Sklar (1959), but its large-scale use in empirical applications is far more recent and dates back to the first decade of the 21st century. Copulas allow for a flexible modelling of the dependence structure between different variables, as well as for the possibility to have different marginal distributions. Moreover, the separation of the dependence structure from the marginals strongly decreases the computational burden of estimating a multivariate model. The 20 publications that constitute my dissertation investigated several cases where it is possible to model and forecast economic and financial time series using Google data and/or copula models.
Диссертация [*.pdf, 14.56 Мб] (дата размещения 3/11/2020)
Резюме [*.pdf, 2.10 Мб] (дата размещения 3/11/2020)
Summary [*.pdf, 873.84 Кб] (дата размещения 3/11/2020)

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Сведения о результатах защиты:Комитет по диссертации рекомендовал присудить ученую степень доктора наук (протокол № 2 от 29.05.2020). Решением диссертационного совета (протокол № 6 от 11.06.2020) присуждена ученая степень доктора экономических наук.
Ключевые слова:bubble, copula, credit risk, Cryptocurrencies, energy markets, forecasting, Google Trends, market risk, multivariate time series, sales, welfare, well-being