J.P. Morgan & Co.

About: J.P. Morgan & Co. is a based out in . It is known for research contribution in the topics: Portfolio & Implied volatility. The organization has 328 authors who have published 436 publications receiving 14291 citations.

ESG in Global Corporate Bonds: The Analysis Behind the Hype

Abstract: Both large institutional and individual retail investors are increasingly demanding that the stewards of their savings demonstrate consideration of environmental, social, and governance (ESG) externalities in their decision making. This study asks how different ESG scores are from traditional agency credit ratings. Are E, S, and G scores correlated? Most importantly, can ESG scores enhance the investment process? Can an active, ESG-tilted corporate bond portfolio strategy generate superior performance versus a relevant benchmark that does not explicitly take ESG scores into account? The authors conclude that ESG scores can be used to enhance portfolio outcomes via lower drawdowns, reduced portfolio volatility, and, in some cases, even marginally increased risk-adjusted returns. Their backtesting suggests that E, S, and G scores are not related to one another and that ESG scores are additive to traditional credit ratings; the contingent liabilities related to ESG issues are not necessarily factored into rating agencies’ assigned credit ratings. TOPIC:ESG investing Key Findings • ESG scores can be used to enhance fixed-income portfolio outcomes via lower drawdowns, reduced portfolio volatility, and, in some cases, even marginally increased risk-adjusted returns. • Cross-sectional correlations show that E, S, and G scores are not related to one another. • ESG scores are additive to traditional credit ratings. In other words, the contingent liabilities related to ESG issues are not necessarily factored into the rating agencies’ assigned credit ratings.

A Gravity Model of Mortality Rates for Two Related Populations

Abstract: The mortality rate dynamics between two related but different-sized populations are modeled consistently using a new stochastic mortality model that we call the gravity model. The larger spreads (or deviations) relative to the evolution of the former, but the spreads in the period and cohort effects between the larger and smaller populations depend on gravity or spread reversion parameters for the two effects. The larger the two gravity parameters, the more strongly the smaller population’s mortality rates move in line with those of the larger population in the long run. This is important where it is believed that the mortality rates between related populations should not diverge over time on grounds of biological reasonableness. The model is illustrated using an extension of the Age-Period-Cohort model and mortality rate data for English and Welsh males representing a large population and the Continuous Mortality Investigation assured male lives representing a smaller related population.

Discrete sums of geometric Brownian motions, annuities and Asian options

Abstract: The discrete sum of geometric Brownian motions plays an important role in modeling stochastic annuities in insurance. It also plays a pivotal role in the pricing of Asian options in mathematical finance. In this paper, we study the probability distributions of the infinite sum of geometric Brownian motions, the sum of geometric Brownian motions with geometric stopping time, and the finite sum of the geometric Brownian motions. These results are extended to the discrete sum of the exponential Levy process. We derive tail asymptotics and compute numerically the asymptotic distribution function. We compare the results against the known results for the continuous time integral of the geometric Brownian motion up to an exponentially distributed time. The results are illustrated with numerical examples for life annuities with discrete payments, and Asian options.

Model uncertainty and asset return predictability: an application of Bayesian model averaging

Abstract: We investigate model uncertainty associated with predictive regressions employed in asset return forecasting research. We use simple combination and Bayesian model averaging (BMA) techniques to compare the performance of these forecasting approaches in short-vs. long-run horizons of S&P500 monthly excess returns. Simple averaging involves an equally-weighted averaging of the forecasts from alternative combinations of factors used in the predictive regressions, whereas BMA involves computing the predictive probability that each model is the true model and uses these predictive probabilities as weights in combing the forecasts from different models. From a given set of multiple factors, we evaluate all possible pricing models to the extent, which they describe the data as dictated by the posterior model probabilities. We find that, while simple averaging compares quite favorably to forecasts derived from a random walk model with drift (using a 10-year out-of-sample iterative period), BMA outperforms simple ...

Economic Restructuring in New York State

Abstract: When economic activity slows down, labor markets may undergo extensive structural change - the permanent reallocation of workers across industries. Job losses can be heavy, and creating new jobs and retraining displaced workers to fill them can take time. A high degree of restructuring may help to explain why New York State's most recent downturn persisted for well over two years.