Abstract Recent advances in high-frequency financial trading have made light propagation delays between geographically separated exchanges relevant. Here we show that there exist optimal locations from which to coordinate the statistical arbitrage of pairs of spacelike separated securities, and calculate a representative map of such locations on Earth. Furthermore, trading local securities along chains of such intermediate locations results in a novel econophysical effect, in which the relativistic propagation of tradable information is effectively slowed or stopped by arbitrage. Discuss this paper
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by Attilio Meucci Abstract: We introduce the multivariate Ornstein-Uhlenbeck and discuss how it generalizes a vast class of continuous-time and discrete-time multivariate processes. Relying on the simple geometrical interpretation of the dynamics of the Ornstein-Uhlenbeck process we introduce cointegration and its relationship to statistical arbitrage. We illustrate an application to swap contract strategies. Fully documented code illustrating the theory and the applications is available at MATLAB Central.
Keywords: alpha, z-score, signal, half-life, vector-autoregression (VAR), moving average (MA), VARMA, stationary, unit-root, mean-reversion, Levy processes Discuss this paper
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by RAOUL PIETERSZ AND ANTOON PELSSER This article presents a new approach to calculating swap vega per bucket in a LIBOR model. It shows that for some forms of volatility an approach based on recalibration may make estimated swap vega very uncertain, as the instantaneous volatility structure may be distorted by recalibration. This does not happen in the case of constant swap rate volatility. Discuss this paper
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by R. Zvan,P.A. Forsyth,K. Vetzal We explore the pricing of Asian options by numerically solving the the associated partial dierential equations We demonstrate that numerical PDE techniques commonly used in nance for standard options are inaccurate in the case of Asian options and illustrate modications which alleviate this problem In particular the usual methods generally produce solutions containing spurious oscillations We adapt ux limiting techniques originally developed in the eld of computational uid dynamics in order to rapidly obtain accurate solutions We show that ux limiting methods are total variation diminishing and hence free of spurious oscillations for non conservative PDEs such as those typically encountered in nance for fully explicit and fully and partially implicit schemes We also modify the van Leer ux limiter so that the second order total variation diminishing property is preserved for non uniform grid spacing Discuss this paper
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by Navneet Arora, Jeffrey R. Bohn, Fanlin Zhu In this paper, we empirically compare two structural models (basic Merton and Vasicek- Kealhofer (VK)) and one reduced-form model (Hull-White (HW)) of credit risk. We propose here that two useful purposes for credit models are default discrimination and relative value analysis. We test the ability of the Merton and VK models to discriminate defaulters from non-defaulters based on default probabilities generated from information in the equity market. We test the ability of the HW model to discriminate defaulters from non-defaulters based on default probabilities generated from information in the bond market. We Discuss this paper
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by Francisco Santos This thesis empirically analyses credit default swaps. The model that we use is the Jarrow-Turnbull credit risk model with a constant recovery rate but assuming hazard rates that are function of time to maturity. The hazard rate is modelled as constant, linear and quadratic functions of time to maturity. For twenty five dates considered we estimate the hazard rate parameters based on fixed-coupon, bullet, senior unsecured bonds, denominated in euros. With the hazard functions we compute the predicted credit default swap premium. The results indicate that, globally, the linear specification produces results that are not biased, while the constant and quadratic specifications overestimate results. Analyzing by maturity of the instrument, we observe that the quadratic specification is better for short maturities; the linear specification is the best in medium maturities, while for long maturities a constant hazard function seems to be the best. We also show that pricing errors in these models are a function of the credit rating of the bond issuer; of the time to maturity and of the date of issuance of the credit default swap.
Traditional affine models of the term structure are eminently tractable, but suffer from empirical difficulties. Random field models offer great flexibility in fitting the data, but are widely considered non-implementable unless they are approximated by a low-dimensional system. I develop a state-space estimation framework where both random field and affine models can be estimated by MCMC using the same panel of forward rate data. I find that random field models are much better able to fit the patterns of volatility and correlation in a long historical sample of U.S. Treasury forward rates. Discuss this paper
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Andrew Angy Geert Bekaertz We examine the econometric performance of regime switching models for interest rate data from the US, Germany and the UK. Regime switching models forecast better out-of-sample than single regime models, including an affine multi-factor model, but do not always match moments very well. Regime switching models incorporating international short rate and term spread information forecast better, match sample moments better, and classify regimes better than univariate regime switching models. Finally, the regimes in interest rates correspond reasonably well with business cycles, at least in the US.
The aim of this review is to give a brief review of the statistical tools, models and fundamental concepts that are available for financial data analysis. The approach is set up as an index of basic concepts for the quantiatively minded. This review is inevitably very brief as both finance and statistics are large subjects. Finance is: Discuss this paper
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Yuriy Nevmyvaka Yi Feng fengyi Michael Kearns1 Abstract We present the first large-scale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. Our experiments are based on 1.5 years of millisecond time-scale limit order data from NASDAQ, and demonstrate the promise of reinforcement learning methods to market microstructure problems. Our learning algorithm introduces and exploits a natural "low-impact" factorization of the state space.
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Tomasz R. Bielecki Applied Mathematics Department, Illinois Institute of Technology Abstract This paper presents an application of risk sensitive control theory in financial decision making. The investor has an infinite horizon objective that can be interpreted as maximizing the portfolio Discuss this paper
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