2007 – 2008
This is joint work with Q. Yao, London School of Economics.
October 11, 2007
Aarhus School of Business, Denmark
Bipower Variation with Noisy Data (Paper)
October 19 and 20, 2007
November 2, 2007
National Science Foundation
University of Connecticut
Modeling and Analyzing High-Frequency Financial Data
November 9, 2007
Amir E. Khandani and Andrew W. Lo
Massachusetts Institute of Technology
What happened to the quants in August 2007
November 16, 2007
Technische Universität München, University of Marburg
University of Braunschweig, Germany
A continuous time GARCH process driven by a Levy process
November 30, 2007
London School of Economics
Centre for Economic Policy Research
The Price Impact of Institutional Herding
December 10, 2007
The University of Chicago
Signing and Nearly-Gamma Random Variables
December 12, 2007
London School of Economics
Subsamplig High Frequency Data
April 18, 2008
Universidad Politecnica de Cartagena, Spain
Exact filters for discretized diffusions
April 24, 2008
Homogeneous Groups and Multiscale Intensity Models for Multiname Credit Derivatives
1. realistic modeling of the firms’ default times and the correlation between them; and
2. efficient computational methods for computing the portfolio loss distribution from the firms’ marginal default time distributions.
We revisit intensity-based models and, with the aforementioned issues in mind, we propose improvements
1. via incorporating fast mean-reverting stochastic volatility in the default intensity processes; and
2. by considering a hybrid of a top-down and a bottom-up model with homogeneous groups within the original set of firms.
We present a calibration example from CDO data, and discuss the relative performance of the approach.
This is joint work with Evan Papageorgiou.
1. Whenever assets reach a barrier they are reduced by a fixed amount through a dividend payment, and whenever they reach 0 they are increased to another fixed amount by a reinvestment.
2. There is no optimal policy, but the value function is approximated by policies of the form described in Item 1 for increasing barriers. We provide criteria to decide whether an optimal solution exists, and when not, show how to calculate the value function. It is discussed how the problem can be solved numerically and numerial examples are given. The talk is based on a paper with the same title to appear in SIAM Journal of Control and Optimization.
Armed with these tools, there are two natural applications: one to finance and one to insurance. In the financial context, the Brownian motion may drive the value of a firm and through a structural modeling approach I will show how CDS spread curves can be matched. In the insurance context, suppose an individuals health reduces by one unit per annum with fluctuations induced by a Brownian motion and once their health hits zero the individual dies. I will show how life-table data can be nicely explained by this model and illustrate how to perturb the distribution for pricing purposes.
This is joint work with Alex Kreinin and Angelo Valov