"Hedge Fund Replication"

This chapter provides a comprehensive explanation of hedge fund replication. This chapter first reviews the characteristics of hedge fund returns. Then, the emergence of hedge fund replication products is discussed. Hedge fund replication methods are classified into three categories: Rule-based, Factor-based, and Distribution replicating approaches. These approaches attempt to capture different aspects of hedge fund returns. This chapter explains the three methods.

Hedge Fund Replication ?Revised in November 2008, forthcoming in The Recent Trend of Hedge Fund Strategies)

This chapter provides a comprehensive explanation of hedge fund replication. This chapter first reviews the characteristics of hedge fund returns. Then, the emergence of hedge fund replication products is discussed. Hedge fund replication methods are classified into three categories: Rule-based, Factor-based, and Distribution replicating approaches. These approaches attempt to capture dierent aspects of hedge fund returns. This chapter explains the three methods.

Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations

We review recent progress in modeling credit risk for correlated assets. We start from the Merton model which default events and losses are derived from the asset values at maturity. To estimate the time development of the asset values, the stock prices are used whose correlations have a strong impact on the loss distribution, particularly on its tails. These correlations are non-stationary which also influences the tails. We account for the asset fluctuations by averaging over an ensemble of random matrices that models the truly existing set of measured correlation matrices. As a most welcome side effect, this approach drastically reduces the parameter dependence of the loss distribution, allowing us to obtain very explicit results which show quantitatively that the heavy tails prevail over diversification benefits even for small correlations. We calibrate our random matrix model with market data and show how it is capable of grasping different market situations. Furthermore, we present numerical simulations for concurrent portfolio risks, i.e., for the joint probability densities of losses for two portfolios. For the convenience of the reader, we give an introduction to the Wishart random matrix model.Comment: Review of a new random matrix approach to credit ris

Concurrent Credit Portfolio Losses

We consider the problem of concurrent portfolio losses in two non-overlapping credit portfolios. In order to explore the full statistical dependence structure of such portfolio losses, we estimate their empirical pairwise copulas. Instead of a Gaussian dependence, we typically find a strong asymmetry in the copulas. Concurrent large portfolio losses are much more likely than small ones. Studying the dependences of these losses as a function of portfolio size, we moreover reveal that not only large portfolios of thousands of contracts, but also medium-sized and small ones with only a few dozens of contracts exhibit notable portfolio loss correlations. Anticipated idiosyncratic effects turn out to be negligible. These are troublesome insights not only for investors in structured fixed-income products, but particularly for the stability of the financial sector

Measurement of Farm Credit Risk: SUR Model and Simulation Approach

The study addresses problems in measuring credit risk under the structure model, and then proposes a seemingly unrelated regression model (SUR) to predict farms’ ability in meeting their current and anticipated obligations in the next 12 months. The empirical model accounts for both the dependence structure and the dynamic feature of the structure model, and is used for estimating asset correlation using FBFM data for 1995-2004. Farm credit risk is then predicted by copula based simulation process with historical default rates as benchmark. Results are reported and compared to previous studies on farm default.Credit Risk Measurement, Seemingly Unrelated Regression Model, Simulation, Agribusiness, Agricultural Finance, Farm Management, Research Methods/ Statistical Methods, Risk and Uncertainty,

Measuring reproducibility of high-throughput experiments

Reproducibility is essential to reliable scientific discovery in high-throughput experiments. In this work we propose a unified approach to measure the reproducibility of findings identified from replicate experiments and identify putative discoveries using reproducibility. Unlike the usual scalar measures of reproducibility, our approach creates a curve, which quantitatively assesses when the findings are no longer consistent across replicates. Our curve is fitted by a copula mixture model, from which we derive a quantitative reproducibility score, which we call the "irreproducible discovery rate" (IDR) analogous to the FDR. This score can be computed at each set of paired replicate ranks and permits the principled setting of thresholds both for assessing reproducibility and combining replicates. Since our approach permits an arbitrary scale for each replicate, it provides useful descriptive measures in a wide variety of situations to be explored. We study the performance of the algorithm using simulations and give a heuristic analysis of its theoretical properties. We demonstrate the effectiveness of our method in a ChIP-seq experiment.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS466 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Modeling Financial Return Dynamics by Decomposition

While the predictability of excess stock returns is statistically small, their sign and volatility exhibit a substantially larger degree of dependence over time. We capitalize on this observation and consider prediction of excess stock returns by decomposing the equity premium into a product of sign and absolute value components and carefully modeling the marginal predictive densities of the two parts. Then we construct the joint density of a positively valued (absolute returns) random variable and a discrete binary (sign) random variable by copula methods and discuss computation of the conditional mean predictor. Our empirical analysis of US stock return data shows among other interesting ndings that despite the large unconditional correlation between the two multiplicative components they are conditionally very weakly dependent.Stock returns predictability; Directional forecasting; Absolute returns; Joint predictive distribution; Copulas.

Forecasting and prequential validation for time varying meta-elliptical distributions

We consider forecasting and prequential (predictive sequential) validation of meta-elliptical distributions with time varying parameters. Using the weak prequential principle of Dawid, we conduct model validation avoiding nuisance parameter problems. Results rely on the structure of meta-elliptical distributions and we allow for discontinuities in the marginals and time varying parameters. We illustrate the ideas of the paper using a large data set of 16 commodity prices