Submitted by Thiago Corrêa (thiago.strava.correa@gmail.com) on 2017-06-27T00:53:21Z No. of bitstreams: 1 Dissertacao Thiago Strava Correa.pdf: 2528557 bytes, checksum: 43d7258add4d6a8bd0ae8469e0948e7c (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2017-06-27T12:04:57Z (GMT) No. of bitstreams: 1 Dissertacao Thiago Strava Correa.pdf: 2528557 bytes, checksum: 43d7258add4d6a8bd0ae8469e0948e7c (MD5) / Made available in DSpace on 2017-06-27T12:56:20Z (GMT). No. of bitstreams: 1 Dissertacao Thiago Strava Correa.pdf: 2528557 bytes, checksum: 43d7258add4d6a8bd0ae8469e0948e7c (MD5) Previous issue date: 2017-05-29 / Esta dissertação propõe o uso de modelos de análise de estilo para a previsão da distribuição dos retornos de carteiras condicionais a cenários estressados de fatores de risco como uma alternativa aos tradicionais modelos de avaliação total. Dentre os seis modelos de análise de estilo cuja capacidade preditiva é testada, destacam-se os modelos quantílico composto e não-linear, que além de obterem os melhores resultados são ainda pouco explorados pela literatura de gestão de risco. / This dissertation suggests the use of style analysis models for the forecasting of portfolio returns’ distribution conditional to stressed scenarios of risk factors. Among the six style analysis models which had their forecasting capacity tested, the composite quantile and the non-linear quantile models stand out by their quality and lack of documentation in the risk management literature.

Die Quantilsregression untersucht die Quantilfunktion QY |X (τ ), sodass ∀τ ∈ (0, 1), FY |X [QY |X (τ )] = τ erfu ̈llt ist, wobei FY |X die bedingte Verteilungsfunktion von Y gegeben X ist. Die Quantilsregression ermo ̈glicht eine genauere Betrachtung der bedingten Verteilung u ̈ber die bedingten Momente hinaus. Diese Technik ist in vielerlei Hinsicht nu ̈tzlich: beispielsweise fu ̈r das Risikomaß Value-at-Risk (VaR), welches nach dem Basler Akkord (2011) von allen Banken angegeben werden muss, fu ̈r ”Quantil treatment-effects” und die ”bedingte stochastische Dominanz (CSD)”, welches wirtschaftliche Konzepte zur Messung der Effektivit ̈at einer Regierungspoli- tik oder einer medizinischen Behandlung sind. Die Entwicklung eines Verfahrens zur Quantilsregression stellt jedoch eine gro ̈ßere Herausforderung dar, als die Regression zur Mitte. Allgemeine Regressionsprobleme und M-Scha ̈tzer erfordern einen versierten Umgang und es muss sich mit nicht- glatten Verlustfunktionen besch ̈aftigt werden. Kapitel 2 behandelt den Einsatz der Quantilsregression im empirischen Risikomanagement w ̈ahrend einer Finanzkrise. Kapitel 3 und 4 befassen sich mit dem Problem der h ̈oheren Dimensionalit ̈at und nichtparametrischen Techniken der Quantilsregression. / Quantile regression studies the conditional quantile function QY|X(τ) on X at level τ which satisfies FY |X QY |X (τ ) = τ , where FY |X is the conditional CDF of Y given X, ∀τ ∈ (0,1). Quantile regression allows for a closer inspection of the conditional distribution beyond the conditional moments. This technique is par- ticularly useful in, for example, the Value-at-Risk (VaR) which the Basel accords (2011) require all banks to report, or the ”quantile treatment effect” and ”condi- tional stochastic dominance (CSD)” which are economic concepts in measuring the effectiveness of a government policy or a medical treatment. Given its value of applicability, to develop the technique of quantile regression is, however, more challenging than mean regression. It is necessary to be adept with general regression problems and M-estimators; additionally one needs to deal with non-smooth loss functions. In this dissertation, chapter 2 is devoted to empirical risk management during financial crises using quantile regression. Chapter 3 and 4 address the issue of high-dimensionality and the nonparametric technique of quantile regression.