Bear Stearns

About: Bear Stearns is a based out in . It is known for research contribution in the topics: Volatility (finance) & Financial market. The organization has 57 authors who have published 65 publications receiving 2228 citations.

Do Investor Sophistication and Trading Experience Eliminate Behavioral Biases in Financial Markets

Abstract: This paper provides an in depth analysis of an investor's reluctance to realize losses and his propensity to realize gains - a behavior known as the disposition effect. Together, sophistication (static differences across investors) and trading experience (evolving behavior of a single investor) eliminate the reluctance to realize losses. However, an asymmetry exists as sophistication and trading experience reduce the propensity to realize gains by 37% (but fail to eliminate this part of the behavior.) Our research design allows us to follow an individual's behavior from the start of his investing life/career. This ability makes it possible to track the evolution of the disposition effect as it is reduced and/or disappears. Our results are robust to alternative explanations including feedback trading, calendar effects, and frequency of observation.

Range-Based Estimation of Stochastic Volatility Models

Abstract: We propose using the price range in the estimation of stochastic volatility models. We show theoretically, numerically, and empirically that the range is not only a highly efficient volatility proxy, but also that it is approximately Gaussian and robust to microstructure noise. The good properties of the range imply that range-based Gaussian quasi-maximum likelihood estimation produces simple and highly efficient estimates of stochastic volatility models and extractions of latent volatility series. We use our method to examine the dynamics of daily exchange rate volatility and discover that traditional one-factor models are inadequate for describing simultaneously the high- and low-frequency dynamics of volatility. Instead, the evidence points strongly toward tw-factor models with one highly persistent factor and one quickly mean-reverting factor.

A Linear Programming Formulation and Approximation Algorithms for the Metric Labeling Problem

Abstract: We consider approximation algorithms for the metric labeling problem. This problem was introduced in a paper by Kleinberg and Tardos [J. ACM, 49 (2002), pp. 616--630] and captures many classification problems that arise in computer vision and related fields. They gave an O(log k log log k) approximation for the general case, where k is the number of labels, and a 2-approximation for the uniform metric case. (In fact, the bound for general metrics can be improved to O(log k) by the work of Fakcheroenphol, Rao, and Talwar [Proceedings of the 35th Annual ACM Symposium on Theory of Computing, 2003, pp. 448--455].) Subsequently, Gupta and Tardos [Proceedings of the 32nd Annual ACM Symposium on the Theory of Computing, 2000, pp. 652--658] gave a 4-approximation for the truncated linear metric, a metric motivated by practical applications to image restoration and visual correspondence. In this paper we introduce an integer programming formulation and show that the integrality gap of its linear relaxation either matches or improves the ratios known for several cases of the metric labeling problem studied until now, providing a unified approach to solving them. In particular, we show that the integrality gap of our linear programming (LP) formulation is bounded by O(log k) for a general k-point metric and 2 for the uniform metric, thus matching the known ratios. We also develop an algorithm based on our LP formulation that achieves a ratio of $2+\sqrt{2}\simeq 3.414$ for the truncated linear metric improving the earlier known ratio of 4. Our algorithm uses the fact that the integrality gap of the LP formulation is 1 on a linear metric.

Correlated Default Risk

Abstract: Fixed-Income portfolios are increasingly susceptible to correlated default risk. Defaults of individual firms will cluster if there are common factors that affect each firm9s default risk. Using a comprehensive dataset of firm-level default probabilities, we examine co-variation of default probabilities across U.S. public non-financial firms. We observe that systematic time-variation in default risk is driven more by an economy-wide volatility factor than by changing debt levels, and therefore is closely linked to the business cycle. Specifically, both default probabilities and default correlations vary over time resulting in substantial variation in joint default risk. For example, over the latter half of the 1990s, default probabilities across the economy doubled, and correlations increased by an even greater magnitude. We provide a reduced-form framework to jointly model time variation in both default probabilities and their correlations over the business cycle. Calibration of the model demonstrates the economic importance of modeling time-variation of joint default risk; for example, our model suggests that the ex-ante probability of observing the record defaults of 2001 doubled across regimes. We also document cross-sectional differences across rating classes—default probability correlations are higher amongst higher quality issuers.