Fall 2016

Risk Seminar Fall 2016 pic

SEM217: Alex Papanicolaou: Testing Local Volatility in Short Rate Models

Tuesday, August 30th @ 11:00-12:30 PM (639 Evans Hall)

Abstract: We provide a simple and easy to use goodness-of-fit test for the misspecification of the volatility function in diffusion models.

Risk Seminar Fall 2016 pic

SEM217: Baeho Kim, Korea University Business School: Stochastic Intensity Margin Modeling of Credit Default Swap Portfolios

Tuesday, September 6th @ 11:00-12:30 PM (639 Evans Hall) 

Abstract: We consider the problem of initial margin (IM) modeling for portfolios of credit default swaps (CDS) from the perspective of a derivatives Central Counterparty (CCP). The CCPs' IM models in practice are based on theoretically-unfounded direct statistical modeling of CDS spreads.

Risk Seminar Fall 2016 pic

SEM217: Bjorn Flesaker, Adjunct Professor at Courant Institute of Mathematical Sciences, NYU: Some Empirical Properties of a Bounded Interest Rate Model

Tuesday, October 11th @ 11:00-12:30 PM (639 Evans Hall)

We consider the two-factor version of a family of time-homogeneous interest rate models introduced by Cairns (Math Finance, 2004) in the Flesaker-Hughston positive interest framework. Specifically, we calibrate the model to cross-sectional USD swap and swaption market data, and we compare the corresponding model implied dynamics to that of the swap market rates via PCA.

Risk Seminar Fall 2016 pic

SEM217: Samim Ghamami, Office of Financial Research: Does OTC Derivatives Reform Incentivize Central Clearing?

Tuesday, October 4th @ 11:00-12:30 PM (639 Evans Hall)

Joint Work with Paul Glasserman Abstract: The reform program for the over-the-counter (OTC) derivatives market launched by the G-20 nations in 2009 seeks to reduce systemic risk from OTC derivatives. The reforms require that standardized OTC derivatives be cleared through central counterparties (CCPs), and they set higher capital and margin requirements for non-centrally cleared derivatives.

Risk Seminar Fall 2016 pic

SEM217: Mark Flood, Office of Financial Research: Measures of Financial Network Complexity: A Topological Approach

Tuesday, September 27th @ 11:00-12:30 PM (639 Evans Hall)

We present a general definition of complexity appropriate for financial counterparty networks and derive several topologically based implementations. These range from simple and obvious metrics to others that are more mathematically subtle.

Risk Seminar Fall 2016 pic

SEM217: Ram Akella, University of California, Berkeley School of Information and TIM/CITRIS/UCSC: Dynamic Multi-modal and Real-Time Causal Predictions and Risks

Tuesday, September 20th @ 11:00-12:30 PM (639 Evans Hall)

There are three major trends in prediction and risk analytics. We describe our research on two fronts and speculate on the third. We do this in the context of healthcare analytics and computational advertising at Silicon Valley firms. 

Risk Seminar Fall 2016 pic

SEM217: Daniel Mantilla-Garcia, Optimal Asset Management: Disentangling the Volatility Return: A Predictable Return Driver of Any Diversified Portfolio

Tuesday, September 13th @ 11:00-12:30 PM (639 Evans Hall)

Abstract: The long-term performance of any portfolio can be decomposed as the sum of the weighted average long-term return of its assets plus the volatility return of the portfolio. The volatility return represents a larger proportion of the total return of portfolios with more homogeneous assets, such as stock factor portfolios. 

Risk Seminar Fall 2016 pic

SEM217: Jim Hawley & Hendrik Bartel, TruValue Labs: Big Data Analytics and ‘Non-Financial’ Sustainability Information—uses of and initial findings from TruValue Labs’ first years

Tuesday, November 15th @ 11:00-12:30 PM (639 Evans Hall)

We present an overview of the current state of ESG (environmental, social, and corporate governance) data in the context of the value of so-called non-financial information. 

Risk Seminar Fall 2016 pic

SEM217: Farzad Pourbabaee, UC Berkeley: Portfolio selection: Capital at risk minimization under correlation constraint

Tuesday, November 8th @ 11:00-12:30 PM (639 Evans Hall) 

We studied the portfolio optimization problem in the Black-Scholes setup, subject to certain constraints. Capital at Risk (CaR) has turned out to resolve many of the shortcomings of the Value at Risk, hence is taken in this presentation as the objective of the optimization problem. 

Risk Seminar Fall 2016 pic

SEM217: Danny Ebanks, Federal Reserve: The Network of Large-Value Loans in the US: Concentration and Segregation

Tuesday, November 1st @ 11:00-12:30 PM (639 Evans Hall)

On this joint project with Anton Badev, we analyze the universe of large-value loans intermediated through Fedwire, the primary U.S. real-time, gross settlement service provided by the Federal Reserve System for the period from 2007 to 2015.

Risk Seminar Fall 2016 pic

SEM217: Ryan Copus and Hannah Laqueur, UC Berkeley: Machines Learning Justice: A New Approach to the Problems of Inconsistency and Bias in Adjudication

Tuesday, October 25th @ 11:00-12:30 PM (639 Evans Hall)

Abstract: We offer a two-step algorithmic approach to the problems of inconsistency and bias in legal decision making. First, we propose a new tool for reducing inconsistency: Judgmental Bootstrapping Models (“JBMs”) built with machine learning methods. 

Risk Seminar Fall 2016 pic

SEM217: Thomas Idzorek, CFA, Head of Investment Methodology and Economic Research at Morningstar: Popularity: A Unifying Asset Pricing Framework?

Tuesday, October 18th @ 11:00-12:30 PM (639 Evans Hall)

In a 2014 article, Thomas Idzorek and Roger Ibbotson introduced popularity as an asset pricing framework. 

Risk Seminar Fall 2016 pic

SEM217: Robert M. Anderson, CDAR Co-Director: PCA with Model Misspecification

Tuesday, November 29th @ 11:00-12:30 PM ( 639 Evans Hall)

In this project with UC Berkeley Ph.D. Candidate Farzad Pourbabaee, Principal Component Analysis (PCA) relies on the assumption that the data being analyzed is IID over the estimation window.  PCA is frequently applied to financial data, such as stock returns, despite the fact that these data exhibit obvious and substantial changes in volatility.