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Series:Occasional Papers  Bank:Federal Reserve Bank of Dallas 

Journal Article
Constructing Zero-Beta VIX Portfolios with Dynamic CAPM
This paper focuses on actively managed portfolios of VIX derivatives constructed to reduce portfolio correlation with the equity market. We find that the best results are obtained using Kalman filter-based dynamic CAPM. The portfolio construction method is capable of constructing zero-beta portfolios with positive alpha.
AUTHORS: Tindall, Michael; Chen, Jiaqi
DATE: 2014-06-01

Journal Article
Hedge Fund Return Prediction and Fund Selection: A Machine-Learning Approach
A machine-learning approach is employed to forecast hedge fund returns and perform individual hedge fund selection within major hedge fund style categories. Hedge fund selection is treated as a cross-sectional supervised learning process based on direct forecasts of future returns. The inputs to the machine-learning models are observed hedge fund characteristics. Various learning processes including the lasso, random forest methods, gradient boosting methods, and deep neural networks are applied to predict fund performance. They all outperform the corresponding style index as well as a benchmark model, which forecasts hedge fund returns using macroeconomic variables. The best results are obtained from machine-learning processes that utilize model averaging, model shrinkage, and nonlinear interactions among the factors.
AUTHORS: Chen, Jiaqi; Tindall, Michael; Wu, Wenbo
DATE: 2016-11-01

Journal Article
The Chen-Tindall system and the lasso operator: improving automatic model performance
Using U.S. monthly macroeconomic data, the automatic model system presented in Chen and Tindall [2016] outperforms the lasso automatic system, but the lasso is improved where Bayesian model averaging is employed to combine its forecasts with those from autoregressive schemes. The best performance is obtained using Bayesian model averaging to combine the Chen?Tindall system, the lasso, and autoregressive schemes. Performance is virtually the same using this combined approach where the elastic-net operator is substituted for the lasso. Similar overall outcomes are found for France and Germany treated as a single economic system and for Canada.
AUTHORS: Chen, Jiaqi; Tindall, Michael
DATE: 2016-05-31

Journal Article
Dynamic Methods for Analyzing Hedge-Fund Performance: A Note Using Texas Energy-Related Funds
We apply dynamic regression to Texas energy-related hedge funds to track changes in portfolio structure and manager performance in response to changing oil prices. We apply hidden Markov models to compute shifts in portfolio performance from boom to bust states. Using these dynamic methods, we find that, in the recent oil-price decline, these funds raised their exposure to high-grade energy-related bonds in a bet that the spread to low-grade energy bonds would widen. When the high-grade bonds eventually fell, the hedge funds entered into a bust state.
AUTHORS: Chen, Jiaqi; Tindall, Michael
DATE: 2016-07-01

Journal Article
Speculation in Commodity Futures Markets, Inventories and the Price of Crude Oil
This paper examines the role of inventories in re ners' gasoline production and develops a structural model of the relationship between crude oil prices and inventories. Using data on inventories and prices of oil futures, I show that convenience yields decrease at a diminishing rate as inventories increase, consistent with the theory of storage. In addition to exhibiting seasonal and procyclical behaviors, I show that the historical convenience yield averages about 18 percent of the oil price from March 1989 to November 2014. Although some have argued that a breakdown of the relationship between crude oil inventories and prices following increased nancial investors' participation after 2004 was evidence of an effect of speculation, I fi nd that the proposed price-inventory relationship is stable over time. The empirical evidence indicates that crude oil prices remained tied to oil-market fundamentals such as inventories, suggesting that the contribution of nancial investors' activities was weak.
AUTHORS: Byun, Sung Je
DATE: 2016-09-01

Journal Article
Understanding hedge fund alpha using improved replication methodologies
In this paper, we estimate alpha for major hedge fund indexes. To set the stage, we examine several alternative methods for replicating Hedge Fund Research Inc. hedge fund indexes. The replication methods include stepwise regression, variations of the lasso shrinkage method, principal component regression, partial least squares regression, and dynamic linear regression. We find that the lasso methods and dynamic regression are superior for generating hedge fund replications and that the performance of the replications corresponds closely to that of the respective actual indexes. Using these superior replications provides us with a solid platform for estimating alpha. We find that at the height of the financial crisis, the alphas computed with our methods were generally negative; in early 2009, when the stock market was rising, alphas were generally positive; and in the recent environment of low volatility and low interest rates, the alphas computed with our methods were generally close to zero and tended to exhibit low volatility.
AUTHORS: Tindall, Michael; Chen, Jiaqi
DATE: 2013

Journal Article
The structure of a machine-built forecasting system
This paper describes the structure of a rule-based econometric forecasting system designed to produce multi-equation econometric models. The paper describes the functioning of a working system which builds the econometric forecasting equation for each series submitted and produces forecasts of the series. The system employs information criteria and cross validation in the equation building process, and it uses Bayesian model averaging to combine forecasts of individual series. The system outperforms standard benchmarks for a variety of national economic datasets.
AUTHORS: Chen, Jiaqi; Tindall, Michael
DATE: 2013

Journal Article
Hedge fund dynamic market sensitivity
Many hedge funds attempt to achieve high returns by employing leverage. However, it is difficult to track the degree of leverage used by hedge funds over time because detailed timely information about their positions in asset markets is generally unavailable. This paper discusses how to combine shrinkage variable selection methods with dynamic regression to compute and track hedge fund leverage on a time-varying basis. We argue that our methodology measures leverage as well as hedge fund sensitivity to markets arising from other sources. Our approach employs the lasso variable selection method to select the independent variables in equations of hedge fund excess returns. With the independent variables selected by the lasso method, a state space model generates the parameter estimates dynamically. The hedge fund market sensitivity indicator is the average of the absolute values of the parameters in the excess return equations. Our indicator peaks at the time of the Long Term Capital Management meltdown in 1998 and again at a critical time in the 2008 financial crisis. In the absence of direct information from hedge fund balance sheets, our approach could serve as an important tool for monitoring market sensitivity and financial distress in the hedge fund industry.
AUTHORS: Tindall, Michael; Chen, Jiaqi
DATE: 2012

Journal Article
Risk measurement illiquidity distortions
We examine the effects of smoothed hedge fund returns on standard deviation, skewness, and kurtosis of return and on correlation of returns and cross-sectional volatility and covariance of returns using an MA(2)-GARCH(1,1)-skewed-t representation of returns instead of the traditional MA(2) model employed in the literature. We present evidence that our proposed representation is more consistent with the behavior of hedge fund returns and that the traditional method tends to overstate the degree of smoothing observed in hedge fund returns. We present methods for correcting for the distortive effects of smoothing using our representation.
AUTHORS: Chen, Jiaqi; Tindall, Michael
DATE: 2012

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