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School of Mathematics

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College of Sciences

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Now showing 1 - 10 of 12

Topics on the length of the longest common subsequences, with blocks, in binary random words

(Georgia Institute of Technology, 2019-08-27) Zhang, Yuze

The study of LIn, the length of the longest increasing subsequences, and of LCIn, the length of the longest common and increasing subsequences in random words is classical in computer science and bioinformatics, and has been well explored over the last few decades. This dissertation studies a generalization of LCIn for two binary random words, namely, it analyzes the asymptotic behavior of LCbBn, the length of the longest common subsequences containing a fixed number, b, of blocks. We first prove that after proper centerings and scalings, LCbBn, for two sequences of i.i.d. Bernoulli random variables with possibly two different parameters, converges in law towards limits we identify. This dissertation also includes an alternative approach to the one-sequence LbBn problem, and Monte-Carlo simulations on the asymptotics of LCbBn and on the growth order of the limiting functional, as well as several extensions of the LCbBn problem to the Markov context and some connection with percolation theory.
Topics on the longest common subsequences: Simulations, computations, and variance

(Georgia Institute of Technology, 2018-11-07) Liu, Qingqing

The study of the longest common subsequences (LCSs) of two random words/strings is classical in computer science and bioinformatics. A problem of particular probabilistic interest is to determine the limiting behavior of the expectation and variance of the length of the LCSs, as the length of the random words grows without bound. This dissertation studies this problem using both Monte-Carlo simulation and theoretical analysis. The specific questions studied here include estimating the growth order of the variance, LCSs based hypothesis testing methods for sequences similarity, theoretical upper bounds on the Chvátal-Sankoff constant of multiple sequences, and theoretical growth order of the variance when the two random words have asymmetrical distributions.
Topics in percolation and sequence analysis

(Georgia Institute of Technology, 2018-07-02) Xu, Chen

This thesis studies three topics, two in percolation system and one in sequence analysis. In the first part, we prove that, for directed Bernoulli last passage percolation with i.i.d.~weights on vertices over a $n\times n$ grid and for $n$ large enough, the geodesics are shown to be concentrated in a cylinder, centered on the main diagonal and of width of order $n^{(2\kappa+2)/(2\kappa+3)}\sqrt{\ln n}$, where $1\le\kappa<\infty$ is the curvature power-index of the shape function at $(1,1)$. The methodology of proof is robust enough to also apply to directed Bernoulli first passage site percolation, and further to longest common subsequences in random words. In the second part, we prove that, in directed last passage site percolation over a $n\times\lfloor n^{\alpha}\rfloor$-grid and for i.i.d.~random weights having finite support, the order of the $r$-th central moment, $1\le r<+\infty$, of the last passage time is, for $n$ large enough, lower bounded by $n^{r(1-\alpha)/2}$, $0<\alpha<1/3$. In the last part, we address a question and a conjecture on the expected length of the longest common subsequences of two i.i.d.$\ $random permutations of $[n]:=\{1,2,...,n\}$. The question is resolved by showing that the minimal expectation is not attained in the uniform case. The conjecture asserts that $\sqrt{n}$ is a lower bound on this expectation, but we only obtain $\sqrt[3]{n}$ for it.
Small-time asymptotics of call prices and implied volatilities for exponential Lévy models

(Georgia Institute of Technology, 2015-01-08) Hoffmeyer, Allen Kyle

We derive at-the-money call-price and implied volatility asymptotic expansions in time to maturity for a selection of exponential Lévy models, restricting our attention to asset-price models whose log returns structure is a Lévy process. We consider two main problems. First, we consider very general Lévy models that are in the domain of attraction of a stable random variable. Under some relatively minor assumptions, we give first-order at-the-money call-price and implied volatility asymptotics. In the case where our Lévy process has Brownian component, we discover new orders of convergence by showing that the rate of convergence can be of the form t¹/ᵃℓ(t) where ℓ is a slowly varying function and $\alpha \in (1,2)$. We also give an example of a Lévy model which exhibits this new type of behavior where ℓ is not asymptotically constant. In the case of a Lévy process with Brownian component, we find that the order of convergence of the call price is √t. Second, we investigate the CGMY process whose call-price asymptotics are known to third order. Previously, measure transformation and technical estimation methods were the only tools available for proving the order of convergence. We give a new method that relies on the Lipton-Lewis formula, guaranteeing that we can estimate the call-price asymptotics using only the characteristic function of the Lévy process. While this method does not provide a less technical approach, it is novel and is promising for obtaining second-order call-price asymptotics for at-the-money options for a more general class of Lévy processes.
Topics in sequence analysis

(Georgia Institute of Technology, 2012-11-12) Ma, Jinyong

This thesis studies two topics in sequence analysis. In the first part, we investigate the large deviations of the shape of the random RSK Young diagrams, associated with a random word of size n whose letters are independently drawn from an alphabet of size m=m(n). When the letters are drawn uniformly and when both n and m converge together to infinity, m not growing too fast with respect to n, the large deviations of the shape of the Young diagrams are shown to be the same as that of the spectrum of the traceless GUE. Since the length of the top row of the Young diagrams is the length of the longest (weakly) increasing subsequence of the random word, the corresponding large deviations follow. When the letters are drawn with non-uniform probability, a control of both highest probabilities will ensure that the length of the top row of the diagrams satisfies a large deviation principle. In either case, both speeds and rate functions are identified. To complete our study, non-asymptotic concentration bounds for the length of the top row of the diagrams, are obtained for both models. In the second part, we investigate the order of the r-th, 1<= r < +∞, central moment of the length of the longest common subsequence of two independent random words of size n whose letters are identically distributed and independently drawn from a finite alphabet. When all but one of the letters are drawn with small probabilities, which depend on the size of the alphabet, the r-th central moment is shown to be of order n^{r/2}. In particular, when r=2, we get the order of the variance of the longest common subsequence.
Small-time asymptotics and expansions of option prices under Levy-based models

(Georgia Institute of Technology, 2012-06-12) Gong, Ruoting

This thesis is concerned with the small-time asymptotics and expansions of call option prices, when the log-return processes of the underlying stock prices follow several Levy-based models. To be speciﬁc, we derive the time-to-maturity asymptotic behavior for both at-the-money (ATM), out-of-the-money (OTM) and in-the-money (ITM) call-option prices under several jump-diﬀusion models and stochastic volatility models with Levy jumps. In the OTM and ITM cases, we consider a general stochastic volatility model with independent Levy jumps, while in the ATM case, we consider the pure-jump CGMY model with or without an independent Brownian component. An accurate modeling of the option market and asset prices requires a mixture of a continuous diﬀusive component and a jump component. In this thesis, we ﬁrst model the log-return process of a risk asset with a jump diﬀusion model by combining a stochastic volatility model with an independent pure-jump Levy process. By assuming smoothness conditions on the Levy density away from the origin and a small-time large deviation principle on the stochastic volatility model, we derive the small-time expansions, of arbitrary polynomial order, in time-t, for the tail distribution of the log-return process, and for the call-option price which is not at-the-money. Moreover, our approach allows for a uniﬁed treatment of more general payoﬀ functions. As a consequence of our tail expansions, the polynomial expansion in t of the transition density is also obtained under mild conditions. The asymptotic behavior of the ATM call-option prices is more complicated to obtain, and, in general, is given by fractional powers of t, which depends on diﬀerent choices of the underlying log-return models. Here, we focus on the CGMY model, one of the most popular tempered stable models used in ﬁnancial modeling. A novel second-order approximation for ATM option prices under the pure-jump CGMY Levy model is derived, and then extended to a model with an additional independent Brownian component. The third-order asymptotic behavior of the ATM option prices as well as the asymptotic behavior of the corresponding Black-Scholes implied volatilities are also addressed.
On the limiting shape of random young tableaux for Markovian words

(Georgia Institute of Technology, 2008-11-17) Litherland, Trevis J.

The limiting law of the length of the longest increasing subsequence, LI_n, for sequences (words) of length n arising from iid letters drawn from finite, ordered alphabets is studied using a straightforward Brownian functional approach. Building on the insights gained in both the uniform and non-uniform iid cases, this approach is then applied to iid countable alphabets. Some partial results associated with the extension to independent, growing alphabets are also given. Returning again to the finite setting, and keeping with the same Brownian formalism, a generalization is then made to words arising from irreducible, aperiodic, time-homogeneous Markov chains on a finite, ordered alphabet. At the same time, the probabilistic object, LI_n, is simultaneously generalized to the shape of the associated Young tableau given by the well-known RSK-correspondence. Our results on this limiting shape describe, in detail, precisely when the limiting shape of the Young tableau is (up to scaling) that of the iid case, thereby answering a conjecture of Kuperberg. These results are based heavily on an analysis of the covariance structure of an m-dimensional Brownian motion and the precise form of the Brownian functionals. Finally, in both the iid and more general Markovian cases, connections to the limiting laws of the spectrum of certain random matrices associated with the Gaussian Unitary Ensemble (GUE) are explored.
Aspects of random matrix theory: concentration and subsequence problems

(Georgia Institute of Technology, 2008-11-17) Xu, Hua

The present work studies some aspects of random matrix theory. Its first part is devoted to the asymptotics of random matrices with infinitely divisible, in particular heavy-tailed, entries. Its second part focuses on relations between limiting law in subsequence problems and spectra of random matrices.
Nonparametric estimation of Levy processes with a view towards mathematical finance

(Georgia Institute of Technology, 2004-04-08) Figueroa-Lopez, Jose Enrique

Model selection methods and nonparametric estimation of Levy densities are presented. The estimation relies on the properties of Levy processes for small time spans, on the nature of the jumps of the process, and on methods of estimation for spatial Poisson processes. Given a linear space S of possible Levy densities, an asymptotically unbiased estimator for the orthogonal projection of the Levy density onto S is found. It is proved that the expected standard error of the proposed estimator realizes the smallest possible distance between the true Levy density and the linear space S as the frequency of the data increases and as the sampling time period gets longer. Also, we develop data-driven methods to select a model among a collection of models. The method is designed to approximately realize the best trade-off between the error of estimation within the model and the distance between the model and the unknown Levy density. As a result of this approach and of concentration inequalities for Poisson functionals, we obtain Oracles inequalities that guarantee us to reach the best expected error (using projection estimators) up to a constant. Numerical results are presented for the case of histogram estimators and variance Gamma processes. To calibrate parametric models,a nonparametric estimation method with least-squares errors is studied. Comparison with maximum likelihood estimation is provided. On a separate problem, we review the theoretical properties of temepered stable processes, a class of processes with potential great use in Mathematical Finance.
Curvature, isoperimetry, and discrete spin systems

(Georgia Institute of Technology, 2001-12) Murali, Shobhana