Most methods for decision-theoretic online learning are based on the Hedge algorithm, which takes a parameter called the learning rate. In most previous analyses the learning rate was carefully tuned to obtain optimal worst-case performance, leading to suboptimal performance on easy instances, for example when there exists an action that is significantly better than all others. We propose a new...

We measure dynamic risk exposure of hedge funds to various risk factors during different market volatility conditions using the regime-switching beta model. We find that in the high-volatility regime (when the market is rolling-down) most of the strategies are negatively and significantly exposed to the Large-Small and Credit Spread risk factors. This suggests that liquidity risk and credit ris...

In this paper, we present a machine learning approach that detects hedge cues and their scope in biomedical texts. Identifying hedged information in texts is a kind of semantic filtering of texts and it is important since it could extract speculative information from factual information. In order to deal with the semantic analysis problem, various evidential features are proposed and integrated...

Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hedging market participants. The resulting estimates are applied to derive expli...

We develop a new multivariate generalized ARCH (GARCH) parameterization suitable for testing the hypothesis that the optimal futures hedge ratio is constant over time, given that the joint distribution of cash and futures prices is characterized by autoregressive conditional heteroskedasticity (ARCH). The advantage of the new parameterization is that it allows for a flexible form of time-varyin...

We extend the regular model checking framework so that it can handle systems with arbitrary width tree-like structures. Configurations of a system are represented by trees of arbitrary arities, sets of configurations are represented by regular hedge automata, and the dynamics of a system is modeled by a regular hedge transducer. We consider the problem of computing the transitive closure T + of...

H edge funds often make headlines because of spectacular losses or spectacular gains. In September 2006, a large hedge fund, Amaranth, reported losses of more than $6 billion apparently incurred in only one month, representing a negative return over that month of roughly 66 percent. Earlier in the year, newspapers focused on the $1.4 billion compensation in 2005 of hedge fund manager Boone Pick...

The search for methodologies that accurately measure performance and performance persistence continues to evolve. This is especially true for investment strategies such as hedge funds, which have been shown, in several instances, to not be normally distributed. In this article, we evaluate performance of hedge funds using conditional approaches and GMM. Unlike the Sharpe ratio or Jensen’s alpha...

This paper compares the performances of the hedge ratios estimated from the OLS (ordinary least squares) method and the constant-correlation VGARCH (vector generalized autoregressive conditional heteroscedasticity) model. These methods are evaluated based on the out-of-sample optimal hedge ratio forecasts. A systematic comparison is provided by examining ten spot and futures markets covering cu...