Joint QF-STAT PhD Webinars

Organizers: Prof. Stan Uryasev (), Prof. Wei Zhu (), Mr. Liting Chiang ()

Zoom Link and Password: Request by e-mailing to Mrs. Laurie Dalessio (laurie.dalessio@stonybrook.edu)

Spring 2021 Semester

Mr. Gabriel Mihalache | 12 February 2021 | Sustainable Debt with Long Run Risk

Speaker: Gabriel Michalache

Title: Sustainable Debt with Long Run Risk

Date/Time: 12 Feb. 2021, Friday, 10:00 AM - 11:00 AM

Abstract: We propose a parsimonious framework for the quantitative evaluation of sustainable debt in emerging markets, by embedding rich fiscal rules and a pricing kernel consistent with key stylized facts, as in Bansal and Yaron (JF, 2004), in the slow-moving debt crisis model of Lorenzoni and Werning (AER, 2019). The model can replicate the importance of a common global factor in spreads and the slow response of primary deficits to debt overhang. The framework supports the design of robust fiscal rules.

Speaker Bio: Gabriel Mihalache is an Assistant Professor of Economics at Stony Brook University since 2016. He was awarded a MSc in Applied Economics in Statistics by Clemson University in 2010 and a PhD in Economics in 2016 by the University of Rochester. His work addresses the causes and consequences of sovereign default risk for the maturity structure of sovereign debt, capital accumulation, and monetary policy.

Mr. Kevin Maritato | 26 February 2021 | An Introduction to Buffered Probability Distribution Functions

Speaker: Kevin Maritato

Title: An Introduction to Buffered Probability Distribution Functions

Date/Time: 26 Feb. 2021, Friday, 10:00AM - 11:00AM

Abstract: In this paper we introduce the concept of Buffered Probability Distribution Functions (bPDFs), as the derivative of the inverse of the Conditional Value at Risk (CVaR). We first explore the connection between this value and risk measures such as the Buffered Probability of Exceedance (bPoE). Then we describe three means of calculating the bPDF for a distribution, depending on what is known about the distribution. For functions with a closed-form formula for bPoE, we derive closed-form formulae for bPDF. We also examine using these formulae for parameter estimation using the maximum likelihood method. We then describe a method for deriving a formula for bPDF based on a numerical bPoE (calculated using Portfolio Safeguard software) when there is a closed-form formula for CVaR but no formula for bPoE. Finally, we give a method for numerically calculating bPDF for an empirical distribution, and compare the results of this method to those given by the other methods for known distributions.

Mr. Mitch Gao | 26 March 2021 | NIG Process and Its Application in Option Pricing and Interest Rate Models

Speaker: Mr. Mitch Gao

Title: NIG Process and Its Application in Option Pricing and Interest Rate Models

Date/Time: 26 Mar. 2021, Friday, TBD

Abstract: NIG type time-inhomogeneous Levy process, introduced by Barndorff-Nielsen as a Normal variance-mean mixture with an Inverse Gaussian mixing distribution, have greater flexibility and can model a variety of distributions, making this process one of great interest from a mathematical finance point of view. In this work, we study the NIG option pricing under Esscher transformation and explore its application in term structure of interest rate models.

TBD | 9 April 2021 | TBD

Speaker: TBD

Title: TBD`

Date/Time: 9 Apr. 2021, Friday, TBD

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TBD | 23 April 2021 | TBD

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Date/Time: 23 APR. 2021, Friday, TBD

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TBD | 7 May 2021 | TBD

Speaker: TBD

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Date/Time: 7 May 2021, Friday, TBD

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Fall 2021 Semester

TBD | 10 September 2021 | TBD

Speaker: TBD

Title: TBD`

Date/Time: 10 Sep. 2021, Friday, TBD

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TBD | 24 September 2021 | TBD

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Date/Time: 24 Sep. 2021, Friday, TBD

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TBD | 8 October 2021 | TBD

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Title: TBD`

Date/Time: 8 Oct. 2021, Friday, TBD

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TBD | 22 October 2021 | TBD

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Title: TBD`

Date/Time: 22 Oct. 2021, Friday, TBD

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TBD | 5 November 2021 | TBD

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Date/Time: 5 Nov. 2021, Friday, TBD

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TBD | 19 November 2021 | TBD

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Date/Time: 19 Nov. 2021, Friday, TBD

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TBD | 3 December 2021 | TBD

Speaker: TBD

Title: TBD`

Date/Time: 3 Dec. 2021, Friday, TBD

Abstract: TBD

Fall 2020 Semester

Mr. Teng Chen | 11 September 2020 | Classification and Severity Progression Measure of COVID-19 Patients Using Proteomic and Metabolomic Sera

Speaker: Mr. Teng Chen (joint presentation with Prof. Pawel Polak and Prof. Stan Uryasev)

Title: Classification and Severity Progression Measure of COVID-19 Patients Using Proteomic and Metabolomic Sera

Date/Time: 11 Sep. 2020, Friday, 10:00 - 11:00 AM EST

Zoom Link: https://stonybrook.zoom.us/j/333315931?pwd=WndYa2thL2lOWjJobm9xd3djc0JXZz09

Abstract: Early detection and effective treatment of severe COVID-19 patients remain one of the major challenges during the current pandemic. Analysis of molecular changes in blood samples of severe patients is one of the promising approaches to this problem. From 75 most relevant proteomic and metabolomic factors selected by Shen et al. (2020), we identify several pairs of factors (some of them after additional nonlinear spline transformation) that are highly effective in classifying severe COVID-19 cases. The performance of these pairs is evaluated in-sample, in a cross-validation exercise, and in an out-of-sample analysis on an independent dataset. Our findings can help medical experts to identify small groups of biomarkers that can be used to construct a cost-effective, short-term test for patients screening and a measure of severity progression.

Mr. Pengzhan Guo | 9 October 2020 | Customizable Career Path Recommendation with Multi-Criteria Stochastic Optimization

Speaker: Mr. Pengzhan Guo

Title: Customizable Career Path Recommendation with Multi-Criteria Stochastic Optimization

Date/Time: 9 October 2020, Friday, 10:00 - 11:00 AM EST

Zoom Link: https://stonybrook.zoom.us/j/98544332907?pwd=UHNocExQeXB5blpLajdmZ0h2RVpBQT09

Abstract: Career mobility forecasting and recommendation are important research topics in talent management and people analytics. While existing models mainly focus on short-term one-period recommendation and prediction for job seekers, the long-term customizable career path recommendation (CPR) is a topic that has not been sufficiently investigated. In this project, we propose a generalized model of CPR and investigate related mathematical and economic characteristics from a long-term customizable perspective. The new objective function is capable of delivering personalized long-term career plans while upholding properties corresponding to three common facts among existing literature on job mobility. We then develop an efficient and effective searching algorithm to achieve the optimal solution of CPR based on simulated annealing and Markov chain techniques. We not only show the effectiveness of our method theoretically but also compare it to three different versions of the greedy method that includes the current state-of-the-art method empirically in addressing similar problems. From simulations based on a large real-world dataset, we show consistent results reaffirming the superior performance of our method in recommending optimal career paths based on user-defined criteria.

Mr. Yuanchen Huang | 23 October 2020 | Time Varying Parameter Selection with Spike and Slab Prior

Speaker: Mr. Yuanchen Huang

Title: Time Varying Parameter Selection with Spike and Slab Prior

Date/Time: 23 October 2020, Friday, 10:00 - 11:00 AM EST

Zoom Link: https://stonybrook.zoom.us/j/98544332907?pwd=UHNocExQeXB5blpLajdmZ0h2RVpBQT09

Mr. Ziheng Chen | 6 November 2020 | On Reinforcement Learning and Compressed Sensing

Speaker: Mr. Ziheng Chen

Title: On Reinforcement Learning and Compressed Sensing

Date/Time: 6 November 2020, Friday, 10:00 - 11:00 AM EST

Zoom Link: https://stonybrook.zoom.us/j/98544332907?pwd=UHNocExQeXB5blpLajdmZ0h2RVpBQT09

Xiaoqi Dong | 20 November 2020 | Weighted reduced rank analysis of time varying cointegration vector

Speaker: Xiaoqi Dong

Title: Weighted reduced rank analysis of time varying cointegration vector

Date/Time: 20 November 2020, Friday, 10:00 - 11:00 AM EST

Zoom Link: https://stonybrook.zoom.us/j/333315931?pwd=WndYa2thL2lOWjJobm9xd3djc0JXZz09