References of "Schiltz, Jang 50003012"
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See detailA new R package for Finite Mixture Models with an application to pension systems
Schiltz, Jang UL; Noel, Cédric

Scientific Conference (2022, April 20)

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See detailtrajeR, an R package for cluster analysis of time series
Noel, Cédric UL; Schiltz, Jang UL

E-print/Working paper (2022)

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See detailMultiple Trajectory Analysis in Finite Mixture Modeling
Noel, Cédric UL; Schiltz, Jang UL

Scientific Conference (2021, June 02)

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See detailtrajeR - une nouvelle librairie R pour les modèles de mélanges pour données longitudinales.
Noel, Cédric UL; Schiltz, Jang UL

in CNRIUT' 2021 - Recueil des Publications (2021, June)

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See detailLuxembourg Fund Data Repository
Skoura, Angeliki; Presber, Julian; Schiltz, Jang UL

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 ▲]

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See detailIdentifiability of Finite Mixture Models
Noel, Cédric; Schiltz, Jang UL

Scientific Conference (2020, June 04)

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See detailTrajeR an R package for the clustering of longitudinal data
Noel, Cédric; Schiltz, Jang UL

Scientific Conference (2020, June 04)

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See detailIdentifiability of Finite Mixture Models with underlying Normal Distribution
Noel, Cédric; Schiltz, Jang UL

E-print/Working paper (2020)

In this paper, we show under which conditions generalized finite mixture with underlying normal distribution are identifiable in the sense that a given dataset leads to a uniquely determined set of model ... [more ▼]

In this paper, we show under which conditions generalized finite mixture with underlying normal distribution are identifiable in the sense that a given dataset leads to a uniquely determined set of model parameter estimations up to a permuta-tion of the clusters. [less ▲]

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See detailA performance evaluation of weight-constrained conditioned portfolio optimization
Schiltz, Jang UL; Boissaux, Marc UL

Scientific Conference (2019, December 20)

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See detailA new model selection criterion for finite mixture models
Schiltz, Jang UL

in Proceedings of the 62nd ISI World Statistics Congress (2019, August 20)

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See detailInvestment fund performance validation - Vine copulae estimation using a minimum spanning tree
Petijean, Simon; Schiltz, Jang UL

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 ▲]

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See detailA performance evaluation of weight-constrained conditioned portfolio optimization
Schiltz, Jang UL; Boissaux, Marc UL

Scientific Conference (2017, May 25)

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See detailPortfolio Optimisation with Conditioning Information
Schiltz, Jang UL

Scientific Conference (2017, April 04)

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See detailCALCURIX: a "tailor-made" RM software
Schiltz, Jang UL; Fadiga, Isamel

Presentation (2017, March 15)

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See detailA performance evaluation of weight-constrained conditioned portfolio optimization
Schiltz, Jang UL; Boissaux, Marc

Scientific Conference (2016, December 15)

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See detailStable distributions for alternative UCITS
Fadiga, Ismael; Schiltz, Jang UL

Scientific Conference (2016, July 16)

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