Modèles de mélanges finis pour une distribution de loi BETA sous-jacente avec une application à des données sur la COVID-19

Scientific Conference (2022, September 14)

A new R package for Finite Mixture Models with an application to pension systems

Scientific Conference (2022, April 20)

trajeR, une nouvelle librairie R pour les modèles de mélange pour données longitundinales

Scientific Conference (2022, April 04)

Multiple Trajectory Analysis in Finite Mixture Modeling

Scientific Conference (2021, June 02)

Luxembourg Fund Data Repository

in Data (2020), 5(3), 1-15

In this paper, we introduce the Luxembourg Fund Data Repository, a novel database of investment funds available for academic research that was created at the Department of Finance of the University of ... [more ▼]

In this paper, we introduce the Luxembourg Fund Data Repository, a novel database of investment funds available for academic research that was created at the Department of Finance of the University of Luxembourg. The database contains the population of Undertakings for Collective Investment in Transferable Securities funds domiciled in Luxembourg from the starting month of their existence (March 1988) to October 2016. The fund characteristics are organized in a comprehensive database architecture encompassing static and dynamic data over the entire life of the funds. The characteristics include fund identifiers, official name, status information, management company and other service providers, daily and monthly performance time-series, portfolio holdings, classification of investment objective, fees, dividends, and cash flows. The database was constructed after collecting and assembling complementary historical information from three data providers. Importantly, funds no longer in existence due to liquidation or mergers are included in the database, preventing survivorship bias. The database has been constructed to serve as a research dataset of high accuracy due to the maximization of population coverage, the maximization of historical coverage, and validation by using information acquired from the supervisory authority of the financial sector of Luxembourg. License currently available to researchers of the Department of Finance of the University of Luxembourg. Future plans for extending accessibility to the global academic community. [less ▲]

TrajeR an R package for the clustering of longitudinal data

Scientific Conference (2020, June 04)

A performance evaluation of weight-constrained conditioned portfolio optimization

Scientific Conference (2019, December 20)

Financial markets dependence modeling using vine copulae - Vine copulae estimation of asset decomposition

Scientific Conference (2019, July 12)

Investment fund performance validation - Vine copulae estimation using a minimum spanning tree

Scientific Conference (2019, June 03)

Catching the dependencies between financial time series is a complex exer- cise with a lot of challenges, both of theoretical and practical nature. We develop a metho- dology to model portfolio dynamics ... [more ▼]

Catching the dependencies between financial time series is a complex exer- cise with a lot of challenges, both of theoretical and practical nature. We develop a metho- dology to model portfolio dynamics using the minimum spanning tree methods combined with econometric models which solves a good part of these challenges. We use a tracking error that is equivalent to the Euclidean distance, to cluster the closest market indices. Financial risk is difficult to manage, because risk is evolving constantly and depends on very different factors like volatility, liquidity, asset class etc. To capture this evolution we develop a recursive portfolio validation method that reveals the true nature of the evolution of the risk structure of financial portfolios. We investigate several portfolio strategies to understand the dynamics behind the holdings. We thus validate the results of our portfolio analysis. We use a vine copula construction, which allows us to separate the marginal estimation from the dependence estimation and calibrate the underlying dynamics very precisely. The minimum spanning tree method helps us to create a robust tree foundation to support the entire vine structure. Starting from a portfolio analysis, we are thus able to validate the portfolio valuation over a specific period of time. [less ▲]

Portfolio Optimisation with Conditioning Information

Scientific Conference (2017, April 04)