Finance and Stochastics (FAST) seminars are held on Tuesdays from 4pm-5pm. They are organised jointly between the Department of Business and Management and the Department of Mathematics. Please see individual seminar details below for venues.
- 17 February
Complex Financial Data: an Econophysics perspective
Tiziana Di Matteo (King's College) -
Venue
Pevensey III, 5C11 (4pm-5pm)
Abstract
In this talk I will briefly give a broad overview of the state of the art in Econophysics: a discipline that has a rich history and even controversial trends [1]. In particular I will focus on two main elements that define the complexity of financial time series: the first is multifractality [2], which is associated to the behavior of each single variable and the way it scales in time; the second is the structure of dependency between time series, associated with the collective behavior of the whole set of variables [3-6]. So far, these two manifestations of complexity have been investigated separately. In this talk I will discuss both and I will point out that they might be related [7]. [1] "Topical Issue: Trends in Econophysics" in EPJB, Vol. 55, No. 2 (2007). [2] T. Di Matteo, Quantitative Finance 7(1) (2007) 21 and T. Di Matteo et al. Journal of Banking & Finance 29/4 (2005) 827. [3] Won-Min Song, T. Di Matteo, T. Aste, PLoS One 7(3) (2012) e31929. [4] F. Pozzi, T. Di Matteo and T. Aste, Scientific Reports 3 (2013) 1665. [5] N. Musmeci, T. Aste, T. Di Matteo, Relation between financial market structure and the real economy: comparison between clustering methods, (2015) arXiv:1406.0496 [q-fin.ST], PLoS One, in press. [6] N. Musmeci, Tomaso Aste, T. Di Matteo, Risk diversification: a study of persistence with a filtered correlation-network approach, (2015) Journal of Network Theory in Finance arXiv:1410.5621 [q-fin.PM], in press. [7] R. Morales, T. Di Matteo, T. Aste, Scientific Reports 4 (2014) 4589.
- 10 March
Title TBC
Ding Chen (University of Sussex) -
Venue
Jubilee G36 (4pm-5pm)
- 17 March
Implied Volatility of Leveraged ETF Options: Consistency and Scaling
Tim Leung (Columbia University) -
Tim Leung presentation [PDF 1.33MB]
Venue
Jubilee G36 (4pm-5pm)
Abstract
The growth of the exchange-traded fund (ETF) industry has given rise to the trading of options written on ETFs and their leveraged counterparts (LETFs). Motivated by a number of empirical market observations, we study the relationship between the ETF and LETF implied volatility surfaces under general stochastic volatility models. Analytic approximations for prices and implied volatilities are derived for LETF options, along with rigorous error bounds. In these price and IV expressions, we identify their non-trivial dependence on the leverage ratio. Moreover, we introduce a "moneyness scaling" procedure for comparing implied volatilities across leverage ratios, and test it with empirical price data.
- 24 March
A Forward Equation of Barrier Options under the Brunick & Shreve Markovian Projection
Christoph Reisinger (University of Oxford) -
Venue
Jubilee G36 (4pm-5pm)
Abstract
We derive a forward equation for arbitrage-free barrier option prices, in terms of Markovian projections of the stochastic volatility process, in continuous semi-martingale models. This provides a Dupire-type formula for the coefficient derived by Brunick and Shreve for their mimicking diffusion and can be interpreted as the canonical extension of local volatility for barrier options. Alternatively, a forward partial-integro differential equation (PIDE) is introduced which provides up-and-out call prices, under a Brunick-Shreve model, for the complete set of strikes, barriers and maturities in one solution step. Similar to the vanilla forward PDE, the above-named forward PIDE can serve as a building block for an efficient calibration routine including barrier option quotes. We provide a discretisation scheme for the PIDE as well as a numerical validation.
- 31 March
Financial information filtering networks
Tomaso Aste (UCL) -
Venue
Pevensey III, 5C11 (4pm-5pm)
Abstract
We are witnessing interesting times rich of information, readily available for us all. Using, understanding and filtering such information has become a major activity across science, industry and society at large. I will show how networks build from similarity/distance measures can be used to process information while it is generated reducing complexity and dimensionality while keeping the integrity of the dataset. I’ll describe different algorithms to build these networks and I’ll discuss bounds on information retrieval. I’ll show how reliable models for risk assessment, forecasting and risk management can be constructed making use of financial information filtering network structure. References: M. Tumminello, T. Aste, T. Di Matteo, R.N. Mantegna, “A tool for filtering information in complex systems”, PNAS 102 (2005) 10421-10426. W.M. Song, T. Di Matteo and T. Aste, “Hierarchical information clustering by means of topologically embedded graphs”, PLoS ONE, 7 (2012) e31929. F. Pozzi, T. Di Matteo, and T. Aste, “Spread of risk across financial markets: better to invest in the peripheries”, Scientific Reports 3 (2013) 1665. G. Previde Massara, T. Di Matteo, and T. Aste, “Network Filtering for Big Data” (2015) submitted.
