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You searched for subject:(Lower Prevision). Showing records 1 – 3 of 3 total matches.

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1. Achath, Sudhakar 1955-. Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability.

Degree: 2017, University of Saskatchewan

This study is about developing some further ideas in imprecise probability models of financial risk measures. A financial risk measure has been interpreted as an upper prevision of imprecise probability, which through the conjugacy relationship can be seen as a lower prevision. The risk measures selected in the study are value-at-risk (VaR) and conditional value-at-risk (CVaR). The notion of coherence of risk measures is explained. Stocks that are traded in the financial markets (the risky assets) are seen as the gambles. The study makes a determination through computation from actual assets data whether the risk measure assessments of gambles (assets) are coherent as an imprecise probability. It is observed that coherence of assessments depends on the asset's returns distribution characteristic. Advisors/Committee Members: Bickis, Mikelis, Samei, Ebrahim, Li, Longhai, Wilson, Craig.

Subjects/Keywords: Imprecise Probability; Lower Prevision; Risk Measure; Coherence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Achath, S. 1. (2017). Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/7888

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Achath, Sudhakar 1955-. “Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability.” 2017. Thesis, University of Saskatchewan. Accessed March 19, 2019. http://hdl.handle.net/10388/7888.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Achath, Sudhakar 1955-. “Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability.” 2017. Web. 19 Mar 2019.

Vancouver:

Achath S1. Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability. [Internet] [Thesis]. University of Saskatchewan; 2017. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/10388/7888.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Achath S1. Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability. [Thesis]. University of Saskatchewan; 2017. Available from: http://hdl.handle.net/10388/7888

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

2. Achath, Sudhakar 1955-. Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability.

Degree: 2017, University of Saskatchewan

This study is about developing some further ideas in imprecise probability models of financial risk measures. A financial risk measure has been interpreted as an upper prevision of imprecise probability, which through the conjugacy relationship can be seen as a lower prevision. The risk measures selected in the study are value-at-risk (VaR) and conditional value-at-risk (CVaR). The notion of coherence of risk measures is explained. Stocks that are traded in the financial markets (the risky assets) are seen as the gambles. The study makes a determination through computation from actual assets data whether the risk measure assessments of gambles (assets) are coherent as an imprecise probability. It is observed that coherence of assessments depends on the asset's returns distribution characteristic. Advisors/Committee Members: Bickis, Mikelis, Samei, Ebrahim, Li, Longhai, Wilson, Craig.

Subjects/Keywords: Imprecise Probability; Lower Prevision; Risk Measure; Coherence

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Achath, S. 1. (2017). Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/7889

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Achath, Sudhakar 1955-. “Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability.” 2017. Thesis, University of Saskatchewan. Accessed March 19, 2019. http://hdl.handle.net/10388/7889.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Achath, Sudhakar 1955-. “Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability.” 2017. Web. 19 Mar 2019.

Vancouver:

Achath S1. Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability. [Internet] [Thesis]. University of Saskatchewan; 2017. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/10388/7889.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Achath S1. Computational Determination of Coherence of Financial Risk Measure as a Lower Prevision of Imprecise Probability. [Thesis]. University of Saskatchewan; 2017. Available from: http://hdl.handle.net/10388/7889

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Quaeghebeur, Erik. Learning from samples using coherent lower previsions.

Degree: 2009, Ghent University

Het hoofdonderwerp van dit werk is het afleiden, voorstellen en bestuderen van voorspellende en parametrische gevolgtrekkingsmodellen die gebaseerd zijn op de theorie van coherente onderprevisies. Een belangrijk nevenonderwerp is het vinden en bespreken van extreme onderwaarschijnlijkheden. In het hoofdstuk ‘Modeling uncertainty’ geef ik een inleidend overzicht van de theorie van coherente onderprevisies ─ ook wel theorie van imprecieze waarschijnlijkheden genoemd ─ en de ideeën waarop ze gestoeld is. Deze theorie stelt ons in staat onzekerheid expressiever ─ en voorzichtiger ─ te beschrijven. Dit overzicht is origineel in de zin dat ze meer dan andere inleidingen vertrekt van de intuitieve theorie van coherente verzamelingen van begeerlijke gokken. Ik toon in het hoofdstuk ‘Extreme lower probabilities’ hoe we de meest extreme vormen van onzekerheid kunnen vinden die gemodelleerd kunnen worden met onderwaarschijnlijkheden. Elke andere onzekerheidstoestand beschrijfbaar met onderwaarschijnlijkheden kan geformuleerd worden in termen van deze extreme modellen. Het belang van de door mij bekomen en uitgebreid besproken resultaten in dit domein is voorlopig voornamelijk theoretisch. Het hoofdstuk ‘Inference models’ behandelt leren uit monsters komende uit een eindige, categorische verzameling. De belangrijkste basisveronderstelling die ik maak is dat het bemonsteringsproces omwisselbaar is, waarvoor ik een nieuwe definitie geef in termen van begeerlijke gokken. Mijn onderzoek naar de gevolgen van deze veronderstelling leidt ons naar enkele belangrijke representatiestellingen: onzekerheid over (on)eindige rijen monsters kan gemodelleerd worden in termen van categorie-aantallen (-frequenties). Ik bouw hier op voort om voor twee populaire gevolgtrekkingsmodellen voor categorische data ─ het voorspellende imprecies Dirichlet-multinomiaalmodel en het parametrische imprecies Dirichletmodel ─ een verhelderende afleiding te geven, louter vertrekkende van enkele grondbeginselen; deze modellen pas ik toe op speltheorie en het leren van Markov-ketens. In het laatste hoofdstuk, ‘Inference models for exponential families’, verbreed ik de blik tot niet-categorische exponentiële-familie-bemonsteringsmodellen; voorbeelden zijn normale bemonstering en Poisson-bemonstering. Eerst onderwerp ik de exponentiële families en de aanverwante toegevoegde parametrische en voorspellende previsies aan een grondig onderzoek. Deze aanverwante previsies worden gebruikt in de klassieke Bayesiaanse gevolgtrekkingsmodellen gebaseerd op toegevoegd updaten. Ze dienen als grondslag voor de nieuwe, door mij voorgestelde imprecieze-waarschijnlijkheidsgevolgtrekkingsmodellen. In vergelijking met de klassieke Bayesiaanse aanpak, laat de mijne toe om voorzichtiger te zijn bij de beschrijving van onze kennis over het bemonsteringsmodel; deze voorzichtigheid wordt weerspiegeld door het op deze modellen gebaseerd gedrag (getrokken besluiten, gemaakte voorspellingen, genomen beslissingen). Ik toon ten slotte hoe de voorgestelde gevolgtrekkingsmodellen gebruikt kunnen… Advisors/Committee Members: De Cooman, Gert, Aeyels, Dirk.

Subjects/Keywords: Mathematics and Statistics; imprecise Dirichlet model; exponential family; inference; desirable gambles; extreme points; coherence; exchangeability; imprecise probability; sample; updating; lower prevision; representation insensitivity; learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Quaeghebeur, E. (2009). Learning from samples using coherent lower previsions. (Thesis). Ghent University. Retrieved from http://hdl.handle.net/1854/LU-495650

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Quaeghebeur, Erik. “Learning from samples using coherent lower previsions.” 2009. Thesis, Ghent University. Accessed March 19, 2019. http://hdl.handle.net/1854/LU-495650.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Quaeghebeur, Erik. “Learning from samples using coherent lower previsions.” 2009. Web. 19 Mar 2019.

Vancouver:

Quaeghebeur E. Learning from samples using coherent lower previsions. [Internet] [Thesis]. Ghent University; 2009. [cited 2019 Mar 19]. Available from: http://hdl.handle.net/1854/LU-495650.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Quaeghebeur E. Learning from samples using coherent lower previsions. [Thesis]. Ghent University; 2009. Available from: http://hdl.handle.net/1854/LU-495650

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.