MSTuE54
Computational Methods in Finance
Date: August 11
Time: 16:0018:30
Room: 12
(Note: Click title to show the abstract.)
Organizer:
Goncu, Ahmet (Xian Jiaotong Liverpool Univ.)
Abstract: Introduction
Applied and computational mathematics, in particular numerical PDEs, Monte Carlo methods, and numerical optimization, play a crucial role in solving problems from financial engineering. The talks in this minisymposia cover recent developments in computational methods used in financial problems, with an emphasis on Monte Carlo methods. Some covered topics are uncertainty and robustness of financial models, stratification and importance sampling in credit risk computations, and statistical arbitrage and Monte Carlo simulation.
Planned Speakers
Our initial list of talks in this minisymposia consists of five planned talks.
We believe that our proposed session will attract scholars from applied and financial mathematics, and make an important contribution to ICIAM 2015¡¯s anticipated success.
MSTuE541
16:0016:30
Existence of Statistical Arbitrage Portfolios in the BlackScholes
Framework
Akyildirim, Erdinc (akdeniz Univ.)
Abstract: In this study we consider diﬀerent statistical arbitrage strategies and prove the existence of
statistical arbitrage portfolios in the BlackScholes framework. We show that if there exists at
least one stock in an economy with a Sharpe ratio larger than half of the volatility of the stock,
a statistical arbitrage trading strategy can be designed. We derive analytical formulas for the
expected value and probability of loss of our statistical arbitrage portfolios.
MSTuE542
16:3017:00
Efficient Simulations for a Bernoulli Mixture Model of Portfolio Credit Risk
Sak, Halis (Xi'an Jiatong Liverpool Univ.)
Abstract: We consider the problem of calculating tail loss probability and conditional excess for the Bernoulli mixture model of credit risk. This is an important problem as all credit risk models proposed in literature can be represented as Bernoulli mixture models. The algorithm we propose is a combination of stratification, importance sampling based on crossentropy, and inner replications using the geometric shortcut method. We evaluate the efficiency of our general method on specific credit risk models.
MSTuE543
17:0017:30
Statistical Arbitrage Portfolios in the BlackScholes Framework
Goncu, Ahmet (Xian Jiaotong Liverpool Univ.)
Abstract: In this study we consider different statistical arbitrage strategies and prove the existence of statistical arbitrage portfolios in the BlackScholes framework. Statistical arbitrage profits can be generated if there exists at least one asset in the economy that satisfies the statistical arbitrage condition derived. We derive analytical formulas for the expected value and probability of loss of our statistical arbitrage portfolios. Furthermore, extensive Monte Carlo simulations are conducted to verify our theoretical results.
MSTuE544
17:3018:00
Sensitivity and Robustness of Financial Models
Okten, Giray (Florida State Univ.)
Mandel, David (Florida State Univ.)
Abstract: We wil discuss sensitivity and robustness of a mathematical model, in particular, models used in financial mathematics. In the literature, different models are usually compared by the model error, however, the sensitivity of a given model with respect to its input parameters is also very important for a modeler. Using Sobol' sensitivity indices, we will introduce a notion for robustness, and compare different models with respect to their robustness.
MSTuE545
18:0018:30
Disappointment Aversion Preferences in Continuous time
Pantelous, Athanasios (Univ. of Liverpool)
Karagiannis, Nikolaos (Univ. of Liverpool)
Abstract: In this paper, the portfolio choice problem for the Gul (1991)'s disappointment averse investors in continuous time framework is developed and considered. Assuming a complete market and general geometric Brownian motions for asset prices, and given the wealth $W$ of an investor and a standard utility function $u$, we define explicitly the certainty equivalent $\mu _W$ by the following relation $$u(\mu_W)= E[u(W)]$$.
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Footnote: Code: TypeDateTimeRoom No.
Type : IL=Invited Lecture, SL=Special Lectures, MS=Minisymposia, IM=Industrial Minisymposia, CP=Contributed Papers, PP=Posters
Date: Mo=Monday, Tu=Tuesday, We=Wednesday, Th=Thursday, Fr=Friday
Time : A=8:309:30, B=10:0011:00, C=11:1012:10, BC=10:0012:10, D=13:3015:30, E=16:0018:00, F=19:0020:00, G=12:1013:30, H=15:3016:00
Room No.: TBA
