Sequential Monte Carlo pricing of American-style options under stochastic volatility models

We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the optimal decision functions in the corresponding dynamic programming problem can be expressed as functions of conditional distributions of volatility, given observed data. By constructing statistics summarizing information about these conditional distributions, one can obtain high quality approximate solutions. Although the required conditional distributions are in general intractable, they can be arbitrarily precisely approximated using sequential Monte Carlo schemes. The drawback, as with many Monte Carlo schemes, is potentially heavy computational demand. We present two variants of the algorithm, one closely related to the well-known least-squares Monte Carlo algorithm of Longstaff and Schwartz [The Review of Financial Studies 14 (2001) 113-147], and the other solving the same problem using a "brute force" gridding approach. We estimate an illustrative SV model using Markov chain Monte Carlo (MCMC) methods for three equities. We also demonstrate the use of our algorithm by estimating the posterior distribution of the market price of volatility risk for each of the three equities.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS286 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Unfolding dynamics of proteins under applied force

Understanding the mechanisms of protein folding is a major challenge that is being addressed effectively by collaboration between researchers in the physical and life sciences. Recently, it has become possible to mechanically unfold proteins by pulling on their two termini using local force probes such as the atomic force microscope. Here, we present data from experiments in which synthetic protein polymers designed to mimic naturally occurring polyproteins have been mechanically unfolded. For many years protein folding dynamics have been studied using chemical denaturation, and we therefore firstly discuss our mechanical unfolding data in the context of such experiments and show that the two unfolding mechanisms are not the same, at least for the proteins studied here. We also report unexpected observations that indicate a history effect in the observed unfolding forces of polymeric proteins and explain this in terms of the changing number of domains remaining to unfold and the increasing compliance of the lengthening unstructured polypeptide chain produced each time a domain unfolds

Devising Face Authentication System and Performance Evaluation Based on Statistical Models

The modern world has seen a rapid evolution of the technology of biometric authentication, prompted by an increaing urgency to ensure a system's security. The need for efficient authentication systems has skyrocketed since 9/11, and the proposed inclusion of digitized photos in passports shows the importance of biometrics in homeland security today. Based on a person's essentially unique biological traits, these methods are potentially more reliable than traditional identifiers like PINs and ID cards. This paper focuses on demonstrating the use of statistical models in devising efficient authentication systems today that are capable of handling real-life applications. First, we propose a novel Gaussian Mixture Model-based face authentication approach in the frequency domain by exploiting the well-known significance of phase in face identification and illustrate that our method is superior to the non-model based state-of-the-art system called the Minimum Average Correlation Energy (MACE) filter in terms of performance on a database of 65 people under extreme illumination conditions. We then introduce a general statistical framework for assessing the predictive performance of a biometric system (including watch-list detection) and show that our model-based system outperforms the MACE system in this regard as well. Finally, we demonstrate how this framework can be used to study the watch-list performance of a biometric system.</p

The effect of core destabilization on the mechanical resistance of I27.

It is still unclear whether mechanical unfolding probes the same pathways as chemical denaturation. To address this point, we have constructed a concatamer of five mutant I27 domains (denoted (I27)(5)*) and used it for mechanical unfolding studies. This protein consists of four copies of the mutant C47S, C63S I27 and a single copy of C63S I27. These mutations severely destabilize I27 (DeltaDeltaG(UN) = 8.7 and 17.9 kJ mol(-1) for C63S I27 and C47S, C63S I27, respectively). Both mutations maintain the hydrogen bond network between the A' and G strands postulated to be the major region of mechanical resistance for I27. Measuring the speed dependence of the force required to unfold (I27)(5)* in triplicate using the atomic force microscope allowed a reliable assessment of the intrinsic unfolding rate constant of the protein to be obtained (2.0 x 10(-3) s(-1)). The rate constant of unfolding measured by chemical denaturation is over fivefold faster (1.1 x 10(-2) s(-1)), suggesting that these techniques probe different unfolding pathways. Also, by comparing the parameters obtained from the mechanical unfolding of a wild-type I27 concatamer with that of (I27)(5)*, we show that although the observed forces are considerably lower, core destabilization has little effect on determining the mechanical sensitivity of this domain