Mathematical Finance
Department of Mathematics and Statistics
Texas Tech University
This semester, the Financial Math seminar series is concentrating on alternative financial indices. The first seminar will consist of an overview from various members of this working group. The agenda for the first seminar is
1. Davide Lauria: index on financial returns from U.S. movies
2. Brent Lindquist: real estate analytics indices
3. Thilini Mahanama: index on financial assets due to natural disasters; introduction of new index on crime
4. Abootaleb Shirvani: U.S. citizenry “well-being” index
5. Zari Rachev: Vol-of-vol indices
6. Dimitri Volchenkov: political risk index
7. Jiho Park and/or Yuan Hu: near riskless rate portfolios
All presentations in the first seminar will be kept to 10 minutes maximum. Watch online at 2 PM this Friday the 4th via this Zoom link.
Continuing the Financial Math seminar series concentrating on alternative financial indices, the agenda for the second seminar is:
1. Thilini Mahanama: introduction of a new index on crime
2. Jiho Park: near riskless rate portfolios
3. Yuan Hu: crypto-currency portfolios and option valuation
4. Zari Rachev: Vol-of-vol indices
Watch online at 2 PM this Friday the 18th via this Zoom link.
The agenda for the Financial Math seminar is:
1. Yuan Hu: crypto-currency portfolios and option valuation
2. Zari Rachev: Vol-of-vol indices
3. Jason Bailey: Early warning signals for real estate bubbles
Watch online at 2 PM Friday the 6th via this Zoom link. Passcode 486269
In this study we suggest a portfolio selection framework based on time series of stock log-returns, option-implied information, and multivariate non-Gaussian processes. We empirically assess a multivariate extension of the normal tempered stable (NTS) model and of the generalized hyperbolic (GH) one by implementing an estimation method that simultaneously calibrates the multivariate time series of log-returns and, for each margin, the univariate observed one-month implied volatility smile. To extract option-implied information, the connection between the historical measure P and the risk-neutral measure Q, needed to price options, is provided by the multivariate Esscher transform. The method is applied to fit a 50-dimensional series of stock returns, to evaluate widely-known portfolio risk measures and to perform a forward-looking portfolio selection analysis. The proposed models are able to produce asymmetries, heavy tails, both linear and non-linear dependence and, to calibrate them, there is no need of liquid multivariate derivative quotes.
Joint work with Gian Luca Tassinari, University of Bologna