Testing Non-Linear Dependence in the Hedge Fund Industry

Autoregressive Conditional Skewness

Tail risk of hedge funds: an extreme value application

Diversification in the hedge fund industry

This chapter focuses on new trends in the hedge fund industry. The chapter begins by creating some historical context for the current perception and state of hedge funds. The remainder of this chapter focuses on the following trends and their potential impact on the industry: (1) growth in all areas of the industry, especially in terms of long-term capital flows from institutional investors; (2) uncertainty about growth in the short term; (3) ways hedge funds approach growth; (4) the need for more diversity among hedge fund managers, including more minorities and women; (5) diverging long-term objectives for larger and smaller hedge funds; (6) future cost savings to investors; (7) development of new investment options to address liquidity concerns for investors; (8) new regulations; and (9) the future role of technology in the hedge fund industry.

Hedge funds increasingly are becoming a focus of investors and U.S. regulatory agencies including the U.S. Securities and Exchange Commission (the “SEC”), the U.S. Treasury Department, the National Association of Securities Dealers, and the Commodity Futures Trading Commission as enhanced regulatory attention is forcing the hedge fund industry for the first time since the near‐collapse of Long‐Term Capital Management, L.P. in late 1998 to examine itself more closely. In light of the dismal performance of U.S. stock market indices over the past several years, investors are turning to “absolute” return vehicles, such as hedge funds, that are able to generate current positive returns in a market that has returned consistently laggard results. Investors’ ability to invest in hedge funds has been further enhanced by the increase of hedge funds‐of‐funds registered under the U.S. Investment Company Act of 1940, as amended (the “1940 Act”). Simultaneously, over the past 18 months, the SEC has become increasingly concerned with a multitude of industry occurrences including (i) the widening access of investors to hedge funds, (ii) the unregulated nature of hedge funds, potentially leading to fraud or conflicts of interest with their investors, and (iii) the impact of hedge funds on the markets, especially in relation to their use of short selling. As U.S. regulatory attention remains focused on the hedge fund industry and industry practice and the themes of potential new regulations become apparent, it has become a cautionary period for hedge fund sponsors and investment managers.

We present a pricing kernel that summarizes well the main features of the dynamics of interest rates and risk in postwar U.S. data and use it to uncover how the pricing kernel has moved with the short rate in this data. Our findings imply that standard monetary models miss an essential link between the central bank instrument and the economic activity that monetary policy is intended to affect, and thus we call for a new approach to monetary policy analysis. We sketch a new approach using an economic model based on our pricing kernel. The model incorporates the key relationships between policy and risk movements in an unconventional way: the central bank's policy changes are viewed as primarily intended to compensate for exogenous business cycle fluctuations in risk that threaten to push inflation off target. This model, while an improvement on standard models, is considered just a starting point for their revision. It leads to critical questions that researchers need to answer as they continue to revise their approach to monetary policy analysis. Copyright © 2009 by the National Bureau of Economic Research.

The purpose is to document the significance of non-linearity in the conditional mean and variance of the long-run annual UK GDP growth rate. In particular, an examination is made of asymmetry in conditional variance with respect to past shocks which implies that the conditional variance is greater, and the conditional variance function steeper, following negative shocks to output growth. Simulations of the estimated model also suggest the potential for complex dynamics including substantial amplitude changes.

Since 1997, the hedge fund industry has grown at a compounded annual growth rate of 16.07%, resulting in a 26-fold increase from its original value to its present value of $3.1 trillion Assets Under Management. This study researched the varying investment strategies used by hedge funds to determine the strategy that provides the highest returns for its investors. From the previous literature, the study identified Long/Short Equity, Global Macro, Arbitrage, Event Driven, and Cross-Asset Multi-Strategy as viable and relevant investment approaches. Using hedge fund index data from Bloomberg, Hedge Fund Research, Eureka Hedge, Barclay’s, and Credit Suisse, returns for each respective strategy were collected and compared against the Bloomberg Global Hedge Fund (BHEDGE) Index and the S&P 500 Index. Alpha adjusted returns for each strategy were later calculated and plotted against the average weighted returns of each individual strategy. The results of this study show that the L/S Equity strategy provided the highest returns for its investors. Specifically, only the L/S Equity strategy outperformed the BHEDGE Index by a narrow margin, while all other strategies provided negative alpha figures. All hedge fund strategies outperformed the overall equity market on a year-to-date basis, however, provided negative alpha returns when compared to the S&P 500 1-Year market gains. This deficit between hedge funds and the overall equity market can be attributed to the COVID-19 pandemic and its inflationary effects through low interest rates, market stimulus packs, and an increased money supply.

Systemic Risk and Hedge Funds

PurposeThe purpose of this paper is two fold: educate investors about hedge fund managers' activities prior to the fraud recognition by the authorities and to help investors and other stakeholders in the hedge fund industry identify red flags before fraud is actually committed.Design/methodology/approachThe paper investigates fraud committed by the Bayou Funds, Beacon Hill Asset Management, Lancer Management Group (LMG), Lipper & Company and Maricopa investment fund. The fraud activities took place during 2000 and 2005.FindingsThe five cases alone cost the hedge fund investors more than $1.5 billion. Investors may have had a good opportunity for avoiding the irrecoverable costs of the fraud had they carefully vetted the backgrounds of the hedge fund managers and/or continuously monitored the funds activities, especially during turbulent market environments.Originality/valueThis is the first research paper to identify and extensively investigate fraud committed by hedge funds. In spite of the size of the hedge fund industry and relatively substantial level and inevitably recurring fraud, academic journals are to yet address this issue. The paper is of great value to hedge funds and their individual and institutional investors, asset managers, financial advisers and regulators.

Does Size Matter in the Hedge Fund Industry?

The Complete Guide to Hedge Funds & Hedge Fund Strategies is a thorough and inclusive book that examines hedge funds both pre- and post crisis while addressing all aspects of this alternative asset class. The fundamentals of the hedge fund industry as well as a detailed examination of every strategy are covered with examples, figures, and tables. The author also addresses UCITS compliant hedge funds, regulation, corporate governance in a post crisis world and how the Dodd–Frank Act provisions have affected the hedge fund industry.

Hedge fund asset allocation can be a challenging endeavor given the dearth of tools available to deal with the unique statistical characteristics of long and short strategies. From a top-down perspective, the hedge fund industry is classified into several substyle categories including long/short equity, market neutral equity, convertible bond arbitrage, merger arbitrage, event driven, global macro, and managed futures. However, due to the non-correlated nature of rates of return in each style group, the problem of asset allocation appears overly simplistic. This article takes a different view of the hedge fund universe, classifying strategies as “convergent” or “divergent” in their orientation and thereby adding new meaning to the process of asset allocation. Convergent strategies tend to view the asset world as being mostly efficient, seeking to profit from small asset mispricings. Divergent strategies are based on the premise that from time to time, the market is inefficient, providing opportunities that can be exploited by using price series analysis and autocorrelations when pricing certain portfolio assets. Since convergent strategies tend to be “short volatility” and divergent strategies “long volatility,” using a top-down asset allocation policy that recognizes this asset dynamic can lead to a more efficiently allocated hedge fund portfolio. The results of this study show the time-varying validity of the divergent strategy and its potential benefits as a portfolio component. Since the divergent strategy experiences significantly higher performance during the periods of increasing market uncertainty, when it is combined with the convergent strategy, the portfolio experiences increased return and reduced risk with more favorable return distributions relative to the individual convergent strategies.

This article focuses on the hedge fund industry, its specifics and impact on the financial system and, in particular on the rate of return, risk and performance produced by the sector. The rate of return, risk and performance of the hedge funds and other asset classes were derived from monthly returns, standard deviation and Sharpe ratios. The time periods 1997-2016 and 2000-2016 were considered. The hedge fund industry, compared to the stock market, the gold market, and the overall commodity market, brought a significantly higher return at a lower risk in the considered period. In the past years, as the two-dimensionally most powerful hedge funds in the short and long term appeared to be smaller funds managing assets up to 100 million USD, and in the medium term the largest funds managing assets over 1 billion USD. From the point of view of geographic asset allocation, the highest two-dimensional performance was shown by funds allocating their assets to North American markets.

This study aims to determine whether daily television audience dynamics exhibit statistically significant conditional variance dependence that is systematically overlooked in conventional ARIMA-based broadcasting forecasts and to assess the incremental empirical value of integrating ARIMA with GARCH modeling. Using 1,096 consecutive daily observations (2022–2024) of viewers for a nationally broadcast program, we implement a diagnostic-first framework that jointly evaluates conditional mean and variance processes. Stationarity is confirmed through the Augmented Dickey–Fuller test (ADF = −3.4693, p = 0.0088), and an MA(1) specification is selected for the conditional mean (AIC = 1302.76). Residual diagnostics reveal pronounced ARCH effects (ARCH-LM = 78.4602, p < 0.001), justifying second-moment modeling. Among competing variance specifications, GARCH(2,2) yields the lowest information criterion (AIC = 1060.321) and indicates near-unit volatility persistence (Σα + Σβ = 0.9856), evidencing durable intertemporal uncertainty transmission. Out-of-sample forecast evaluation demonstrates low relative error (MAPE ≈ 1.0%), supporting empirical robustness. Unlike prior ARIMA-centered broadcasting studies that prioritize point accuracy under homoscedastic assumptions, this integration explicitly models volatility clustering as an object of inference, aligning media analytics with established volatility frameworks without overstating cross-domain novelty. The findings show that incorporating conditional variance dynamics provides measurable gains in risk-sensitive forecasting, offering a replicable approach for advertising allocation and scheduling decisions in competitive media environments.