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Hedge fund allocation: evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques

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Hedge fund allocation : evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques. / Subbiah, Mohan; Fabozzi, Frank J.

In: International Review of Financial Analysis, Vol. 45, 05.2016, p. 189-201.

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Subbiah, Mohan ; Fabozzi, Frank J. / Hedge fund allocation : evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques. In: International Review of Financial Analysis. 2016 ; Vol. 45. pp. 189-201.

Bibtex

@article{535e70f402944dbca3abeae83e09633b,
title = "Hedge fund allocation: evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques",
abstract = "We propose a model for constructing Asian funds of hedge funds. We compare the accuracy of forecasts of hedge fund returns using an ordinary least squares (OLS) regression model, a nonparametric regression model, and a nonlinear nonparametric model. We backtest to assess these forecasts using three different portfolio construction processes: an “optimized” portfolio, an equally-weighted portfolio, and the Kelly criterion-based portfolio. We find that the Kelly criterion is a reasonable method for constructing a fund of hedge funds, producing better results than a basic optimization or an equally-weighted portfolio construction method. Our backtests also indicate that the nonparametric forecasts and the OLS forecasts produce similar performance at the hedge fund index level. At the individual fund level, our analysis indicates that the OLS forecasts produce higher directional accuracy than the nonparametric methods but the nonparametric methods produce more accurate forecasts than OLS. In backtests, the highest information ratio to predict hedge fund returns is obtained from a combination of the OLS regression with the Fung–Hsieh eight-factor variables as predictors using the Kelly criterion portfolio construction method. Similarly, the highest information ratio using forecasts generated from a combination of the nonparametric regression using the Fung–Hsieh eight-factor model variables is achieved using the Kelly criterion portfolio construction method. Simulations using risk-adjusted total returns indicate that the nonparametric regression model generates superior information ratios than the analogous backtest results using the OLS. However, the benefits of diversification plateau with portfolios of more than 20 hedge funds. These results generally hold with portfolio implementation lags up to 12 months.",
keywords = "Hedge fund allocation, Hedge funds, Funds of hedge funds",
author = "Mohan Subbiah and Fabozzi, {Frank J.}",
year = "2016",
month = may,
doi = "10.1016/j.irfa.2016.03.003",
language = "English",
volume = "45",
pages = "189--201",
journal = "International Review of Financial Analysis",
issn = "1057-5219",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Hedge fund allocation

T2 - evaluating parametric and nonparametric forecasts using alternative portfolio construction techniques

AU - Subbiah, Mohan

AU - Fabozzi, Frank J.

PY - 2016/5

Y1 - 2016/5

N2 - We propose a model for constructing Asian funds of hedge funds. We compare the accuracy of forecasts of hedge fund returns using an ordinary least squares (OLS) regression model, a nonparametric regression model, and a nonlinear nonparametric model. We backtest to assess these forecasts using three different portfolio construction processes: an “optimized” portfolio, an equally-weighted portfolio, and the Kelly criterion-based portfolio. We find that the Kelly criterion is a reasonable method for constructing a fund of hedge funds, producing better results than a basic optimization or an equally-weighted portfolio construction method. Our backtests also indicate that the nonparametric forecasts and the OLS forecasts produce similar performance at the hedge fund index level. At the individual fund level, our analysis indicates that the OLS forecasts produce higher directional accuracy than the nonparametric methods but the nonparametric methods produce more accurate forecasts than OLS. In backtests, the highest information ratio to predict hedge fund returns is obtained from a combination of the OLS regression with the Fung–Hsieh eight-factor variables as predictors using the Kelly criterion portfolio construction method. Similarly, the highest information ratio using forecasts generated from a combination of the nonparametric regression using the Fung–Hsieh eight-factor model variables is achieved using the Kelly criterion portfolio construction method. Simulations using risk-adjusted total returns indicate that the nonparametric regression model generates superior information ratios than the analogous backtest results using the OLS. However, the benefits of diversification plateau with portfolios of more than 20 hedge funds. These results generally hold with portfolio implementation lags up to 12 months.

AB - We propose a model for constructing Asian funds of hedge funds. We compare the accuracy of forecasts of hedge fund returns using an ordinary least squares (OLS) regression model, a nonparametric regression model, and a nonlinear nonparametric model. We backtest to assess these forecasts using three different portfolio construction processes: an “optimized” portfolio, an equally-weighted portfolio, and the Kelly criterion-based portfolio. We find that the Kelly criterion is a reasonable method for constructing a fund of hedge funds, producing better results than a basic optimization or an equally-weighted portfolio construction method. Our backtests also indicate that the nonparametric forecasts and the OLS forecasts produce similar performance at the hedge fund index level. At the individual fund level, our analysis indicates that the OLS forecasts produce higher directional accuracy than the nonparametric methods but the nonparametric methods produce more accurate forecasts than OLS. In backtests, the highest information ratio to predict hedge fund returns is obtained from a combination of the OLS regression with the Fung–Hsieh eight-factor variables as predictors using the Kelly criterion portfolio construction method. Similarly, the highest information ratio using forecasts generated from a combination of the nonparametric regression using the Fung–Hsieh eight-factor model variables is achieved using the Kelly criterion portfolio construction method. Simulations using risk-adjusted total returns indicate that the nonparametric regression model generates superior information ratios than the analogous backtest results using the OLS. However, the benefits of diversification plateau with portfolios of more than 20 hedge funds. These results generally hold with portfolio implementation lags up to 12 months.

KW - Hedge fund allocation

KW - Hedge funds

KW - Funds of hedge funds

U2 - 10.1016/j.irfa.2016.03.003

DO - 10.1016/j.irfa.2016.03.003

M3 - Journal article

VL - 45

SP - 189

EP - 201

JO - International Review of Financial Analysis

JF - International Review of Financial Analysis

SN - 1057-5219

ER -