The Nature of Alpha

We suggest an empirical model of investment strategy returns which elucidates the importance of non-Gaussian features, such as time-varying volatility, asymmetry and fat tails, in explaining the level of expected returns. Estimating the model on the (former) Lehman Brothers Hedge Fund Index data, we demonstrate that the volatility compensation is a significant component of the expected returns for most strategy styles, suggesting that many of these strategies should be thought of as being `short vol'. We present some fundamental and technical reasons why this should indeed be the case, and suggest explanation for exception cases exhibiting `long vol' characteristics. We conclude by drawing some lessons for hedge fund portfolio construction.Comment: 22 pages, 5 figures, 3 table

A Guide to Modeling Credit Term Structures

We give a comprehensive review of credit term structure modeling methodologies. The conventional approach to modeling credit term structure is summarized and shown to be equivalent to a particular type of the reduced form credit risk model, the fractional recovery of market value approach. We argue that the corporate practice and market observations do not support this approach. The more appropriate assumption is the fractional recovery of par, which explicitly violates the strippable cash flow valuation assumption that is necessary for the conventional credit term structure definitions to hold. We formulate the survival-based valuation methodology and give alternative specifications for various credit term structures that are consistent with market observations, and show how they can be empirically estimated from the observable prices. We rederive the credit triangle relationship by considering the replication of recovery swaps. We complete the exposition by presenting a consistent measure of CDS-Bond basis and demonstrate its relation to a static hedging strategy, which remains valid for non-par bonds and non-flat term structures of interest rates and credit risk.Comment: 54 pages, 13 figures (references fixed

Spatial Model of COVID 19 Distribution Based on Differences an Climate Characteristics and Environment of According to the Earth Latitude

The purpose of this research is to analyze the spread of COVID-19 on the earth's surface. The method used is a spatial method with the Geographic Information System (GIS) analysis technique. The results of this study explain that the spread of COVID-19 very much occurs in the region of moderate latitude. Besides, tropical climate conditions can also make the virus more quickly become unstable. The Coronavirus spreads faster in countries with high latitudes and humid climates. The spread and concentration of the COVID-19 virus increasingly to the poles are easier to develop, current conditions at high latitudes or poles are not significantly affected due to a small population concentration and population activity, so the virus cannot spread, because the body's virus does not find a host (human) to live and develop

The Underlying Dynamics of Credit Correlations

We propose a hybrid model of portfolio credit risk where the dynamics of the underlying latent variables is governed by a one factor GARCH process. The distinctive feature of such processes is that the long-term aggregate return distributions can substantially deviate from the asymptotic Gaussian limit for very long horizons. We introduce the notion of correlation spectrum as a convenient tool for comparing portfolio credit loss generating models and pricing synthetic CDO tranches. Analyzing alternative specifications of the underlying dynamics, we conclude that the asymmetric models with TARCH volatility specification are the preferred choice for generating significant and persistent credit correlation skews