Electricity industries worldwide have been restructured in order to introduce competition.As a result, decisionmakers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for ...

In this paper, we will explain how to perfectly hedge under Heston’s stochastic volatility 17 model with jump-to-default, which is in itself a generalization of the Merton jump-todefault model and a special case of the Heston model with jumps. The hedging instru19 ments we use to build the hedge will be as usual the stock and the bond, but also the Variance Swap (VS) and a Credit Default Swap (...

This paper utilises a new approach for determining minimum variance hedge ratio in stock index futures markets. More specifically, the performance of time-varying hedge ratios generated from Markov Regime Switching (MRS) models is investigated. The rational behind the use of these models stems from the fact that the dynamic relationship between spot and futures returns may be characterised by r...

Data envelopment analysis (DEA) is a nonparametric method from the area of operations research that measures the relationship of produced outputs to assigned inputs and determines an efficiency score. This efficiency score can be interpreted as a performance measure in investment analysis. Recent literature contains intensive discussion of using DEA to measure the performance of hedge funds, as...

In this paper, we examine the risk characteristics and capital adequacy of hedge funds using Value-at-Risk (VaR) as the criterion for measuring risk and estimating capital requirements. We find that a vast majority of hedge funds are adequately capitalized, with the level of under-capitalization being only 3.1% for live funds and 7.5% for dead funds. Using extreme value theory, we confirm that ...

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...