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We examine whether the hedging effectiveness of crude oil futures is affected by asymmetry in the return distribution by applying tail-specific metrics to compare the hedging effectiveness of both short and long hedgers. The hedging effectiveness metrics we use are based on lower partial moments (LPM), value at risk (VaR) and conditional value at risk (CVaR). Comparisons are applied to a number of hedging strategies including ordinary least square (OLS), and both symmetric and asymmetric GARCH models. We find that OLS provides consistently better performance across different measures of hedging effectiveness as compared with GARCH models, irrespective of the characteristics of the underlying distribution.

Our study explores friction costs in terms of competition and market structure, considering factors such as market share, industry leverage levels, industry hedging levels, number of peers, and the geographic concentration that influences reinsurance purchase in the Property and Casualty insurance industry in China. Financial factors that influence the hedging level are also included. The data are hand collected from 2008 to 2015 from the Chinese Insurance Yearbook. Using panel data analysis techniques, the results are interesting. The capital structure shows a significant negative relationship with the hedging level. Group has a negative relationship with reinsurance purchases. Assets exhibit a negative relationship with hedging levels. The hedging level has a negative relation with the individual hedging level. Insurers have less incentive to hedge because it provides less resource than leverage. The study also robustly investigates the strategic risk management separately by the financial crises.

Midwest Independent Transmission System Operator, Inc. (MISO) is a nonprofit regional transmission organization (RTO) that oversees electricity production and transmission across 13 states and 1 Canadian province. MISO also operates an electronic exchange for buying and selling electricity for each of its five regional hubs.

MISO oversees two types of markets. The forward market, which is referred to as the day-ahead (DA) market, allows market participants to place demand bids and supply offers on electricity to be delivered at a specified hour the following day. The equilibrium price, known as the locational marginal price (LMP), is determined by MISO after receiving sale offers and purchase bids from market participants. MISO also coordinates a spot market, which is known as the real-time (RT) market. Traders in the RT market must submit bids and offers by 30minutes prior to the hour for which the trade will be executed. After receiving purchase and sale offers for a given hour in the RT market, MISO then determines the LMP for that particular hour.

The existence of the DA and RT markets allows producers and retailers to hedge against the large fluctuations that are common in electricity prices. Hedge ratios on the MISO exchange are estimated using various techniques. No hedge ratio technique examined consistently outperforms the unhedged portfolio in terms of variance reduction. Consequently, none of the hedge ratio methods in this study meet the general interpretation of FASB guidelines for a highly effective hedge.

In this chapter we concentrate at the most popular model for convertible bond (CB) valuation in a one-factor, stochastic underlying stock price setting. Through the last decade, the Tsiveriotis–Fernandes model (1998) has become a widely commented model that involves the state of default of the issuer of the CB. A routine approach to the solution of this model is the usage of methods of finite difference schemes (FDS). However, for many people trained in finance these methods are not very intuitive and they tend to ignore them and prefer to use binomial-tree approach as more intuitive technique. For that reason, our primary focus is to highlight the answer of the so far unanswered question: Does the binomial-tree approach to CBs provide accurate pricing, hedging, and risk assessment? We show on a set of representative examples that by using binomial-tree methodology one is unable to provide a consistent analysis of the pricing, hedging, and risk assessment. We start the chapter with the basics of CBs and CB market. We then explain the implementation of TF model within binary-tree approach. We conclude the chapter with performance valuation of binomial-tree approach showing unexpected behavior in practice areas such as pricing (profile of CB's price versus underlying stock price), hedging (performance of CB's delta, gamma, and convertible arbitrage strategy versus underlying stock), and risk assessment (Monte Carlo VaR with respect to the underlying).

Using a GED-GARCH model to estimate monthly data from January 1990 to February 2022, we test whether gold acts as a hedge or safe haven asset in 10 countries. With a downturn of the stock market, gold can be viewed as a hedge and safe haven asset in the G7 countries. In the case of inflation, gold acts as a hedge and safe haven asset in the United States, United Kingdom, Canada, China, and Indonesia. For currency depreciation, oil price shock, economic policy uncertainty, and US volatility spillover, evidence finds that gold acts as a hedge and safe haven for all countries.

Purpose – This research pinpoints the limitations of conventional models for evaluating the performance of hedge funds and attempts to provide a new framework for modeling the dynamics of risk structures of hedge funds.

Methodology/approach – This chapter aims to explore how the systematic risk exposures of hedge funds vary over time and depend on exogenous variables that managers are supposed to use in their dynamic investment strategies. To achieve this, we used a Bayesian time-varying CAPM-based beta model within a state space technology.

Findings – The results showed that the volatility, term spread rate, and shocks in liquidity influence significantly on the time variation of hedge funds. Besides, the dynamics of beta indicates that the transmission channels of systematic risk are mainly the leverage levels of hedge funds and liquidity shocks.

Originality/value of chapter – These results are original because they help to explain how expected and unexpected hedge fund returns are correlated with the systematic risk factors via the beta dynamics.

This chapter aims to determine whether diversification benefits accrue from adding emerging market hedge funds (EMHFs) to an emerging market bond/equity portfolio, and subsequently whether the type of exposure hedge funds provide is justified by their fees. We use multivariate cointegration analysis to show that the advantages of adding hedge funds to balanced portfolios are limited for the three regions of Asia, Eastern Europe, and Latin America, as well as for the entire global emerging market universe. In summary, we find that emerging market hedge funds are generally redundant for diversifying long-only emerging market investment portfolios with long-term investment horizons. This result also holds when we extend our sample by the global financial crisis in 2008 and 2009 and allow for structural breaks according to the Gregory-Hansen (1996) test. Hence, even during the global financial crisis in 2008 and 2009, when risk diversification was most needed, long-term comovements between hedge funds and traditional assets is, with the exception of the Eastern European region, not disrupted. Because EMHF returns are heavily influenced by the emerging market equity and bond markets, we conclude that the “alpha fees” charged by EMHFs may not always be appropriate for the three main regions under consideration. This also holds, however, to a lesser extent, for a global diversification among hedge funds and traditional assets in emerging markets.

The main purpose of this chapter is to estimate a model for hedge fund returns that will endogenously generate failure probabilities using panel data where sample attrition due to fund failures is a dominant feature. We use the Lipper (TASS) hedge fund database, which includes all live and defunct hedge funds over the period January 1994 through March 2009, to estimate failure probabilities for hedge funds. Our results show that hedge fund failure prediction can be substantially improved by accounting for selectivity bias caused by censoring in the sample. After controlling for failure risk, we find that capital flow, lockup period, redemption notice period, and fund age are significant factors in explaining hedge fund returns. We also show that for an average hedge fund, failure risk increases substantially with age. Surprisingly, a 5-year-old fund on average has only a 65% survival rate.

This study examines several aspects of active portfolio management by equity hedge funds between 1996 and 2013. Consistent with the idea that cross-sectional return dispersion is a proxy for the market’s available alpha, our results show that equity hedge funds achieve their strongest performance during periods of elevated dispersion. The performance advantage is robust to numerous risk adjustments. Portfolio managers may use the current month’s dispersion to plan the extent to which the following month’s investment approach will be active or passive. We also estimate the active share for equity hedge funds and find an average of 53%. We further document the average annual expense ratio for managing hedge funds’ active share to be about 7%. This figure is remarkably close to active expense ratios reported previously for equity mutual funds, which may be interpreted as evidence of uniform pricing for active portfolio management services.