Are Leveraged and Inverse ETFs the New Portfolio Insurers?

Tuzun, Tugkan

doi:10.2139/ssrn.2190708

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…To attract more assets under management, leveraged funds focus on finding the benchmark to which investors demand leveraged exposure and the cheapest way to achieve such leveraged exposure. 8 An incomplete list of the growing literature using leveraged fund data includes Avellaneda and Zhang (2010) and Lu, Wang, and Zhang (2009), who study the deviation between the long-term performance (in contrast to the one-day performance as promised in the prospectus) of leveraged funds and that of a levered buy-and-hold position in the underlying index, Charupat and Miu (2011), Tang and Xu (2013), and Jiang and Yan (2020), who study the market price discounts and premiums of leveraged funds, and Cheng and Madhavan (2009), Tuzun (2014), and Ivanov and Lenkey (2014), who study the implications of daily rebalancing of leveraged funds (in the same direction as benchmark returns) on market liquidity and volatility.…”

Section: Institutional Backgroundmentioning

confidence: 99%

“…Tang and Xu (2013) find that the interest rate is the most important determinant of the deviation between daily fund returns and target returns using 12 leveraged funds between 2006 and 2011. Tuzun (2014) and Jiang and Yan (2020) investigate a large panel of leveraged ETFs, collected from Morningstar Direct and Bloomberg. In comparison, our data set includes not only leveraged ETFs but also open-end mutual funds, resulting in two to four times as many funds.…”

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confidence: 99%

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Leveraged Funds and the Shadow Cost of Leverage Constraints

Lu¹,

Qin²

2021

The Journal of Finance

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Using the most comprehensive data set of leveraged funds known to the literature, we measure the market-wide shadow cost of leverage constraints and examine its pricing implications. The shadow cost averages 0.53% per annum from 2006 to 2016, spikes upon quarter-ends when banks face tighter capital requirements, positively predicts future betting-against-beta (BAB) returns, and negatively correlates with contemporaneous BAB returns. Stocks that experience lower returns when the shadow cost increases earn 0.85% more per month. Overall, our shadow cost measure fits the predictions of leverage-constraint-based theories better than the widely used TED spread.WE PROPOSE A NEW EMPIRICAL measure for the shadow cost of leverage constraints. The shadow cost measures the tightness of marginal investors' leverage constraints and is increasingly considered a key determinant of asset prices. 1 Examining the theoretical asset pricing implications of the shadow cost of leverage constraints requires an accurate empirical measure, which has remained elusive so far. As Garleanu and Pedersen (2011) demonstrate, the shadow cost is the difference between marginal investors' funding cost and the risk-free rate when the credit risk of the funding is zero (or fully hedged). Constructing an appropriate measure for the shadow cost therefore requires both

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Section: Institutional Backgroundmentioning

confidence: 99%

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confidence: 99%

Leveraged Funds and the Shadow Cost of Leverage Constraints

Lu¹,

Qin²

2021

The Journal of Finance

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“…The work by Dobi and Avellaneda [9] takes the discussion of LETFs a step further. They highlight the total return swap exposures that are rebalanced daily by LETF issuers to provide the leveraged return target as a cause of LETF underperformance.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Leveraged ETF Volatility Using Nonparametric Density Estimation

Ginley¹,

Scott²,

Ensor³

2015

JMF

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Leveraged Exchange Traded Funds (LETFs) are constructed to provide the indicated leverage multiple of the daily total return on an underlying index. LETFs may perform as expected on a daily basis; however, fund issuers state that there is no guarantee of achieving the multiple of the index return over longer time horizons. LETF returns are extremely volatile and funds frequently underperform their target for horizons greater than one month. In this paper, we contribute two nonparametric simulation methods for analyzing LETF return volatility and how this is related to the underlying index. First, to overcome the limited history of LETF returns data, we propose a method for simulating implied LETF tracking errors while still accounting for their dependence on underlying index returns. This allows for the incorporation of the complete history of index returns in an LETF returns model. Second, to isolate the effects of daily, leveraged compounding on LETF volatility, we propose an innovative method for simulating daily index returns with a chosen constraint on the multi-day period return. By controlling for the performance of the underlying index, the range of volatilities observed in a simulated sample can be attributed to compounding with leverage and the presence of tracking errors. Our nonparametric methods are flexible-easily incorporating any chosen number of days, leverage ratios, or period return constraints, and can be used in combination or separately to model any quantity of interest derived from daily LETF returns.

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“…3 The basis of the commentators' concerns seems to be a common perception that leveraged and inverse ETFs must rebalance their portfolios in the same direction as the contemporaneous return on their underlying assets in order to maintain a constant leverage ratio. Conventional thinking suggests that by purchasing assets following positive returns and selling assets following negative returns, these types of financial products exert additional upward price pressure on the underlying assets following positive returns and additional downward pressure following negative returns (see, e.g., Cheng and Madhavan (2009), Bai, Bond, and Hatch (2014), Tuzun (2014), and Shum et al (2014)). However, such reasoning is incomplete because it overlooks the effects of capital flows.…”

Section: Introductionmentioning

confidence: 99%

“…We are not the first to examine the potential for leveraged and inverse ETFs to exacerbate volatility, though, to our knowledge, we are the first to consider the effects of capital flows. A handful of researchers have attempted to assess whether leveraged and inverse ETFs amplify volatility in practice by examining the relation between intra-day returns and end-of-day volatility (see, e.g., Bai, Bond, and Hatch (2014), Trainor (2010), Tuzun (2014), andShum et al (2014)). None of these analyses, however, use actual data on ETF holdings or rebalancing activity to estimate the impact of ETFs on volatility.…”

Section: Introductionmentioning

confidence: 99%

Are Concerns About Leveraged ETFs Overblown?

Ivanov

Lenkey

2014

SSRN Journal

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Leveraged and inverse exchange-traded funds (ETFs) have been heavily criticized for exacerbating volatility in financial markets because it is thought that they mechanically rebalance their portfolios in the same direction as contemporaneous returns. We argue that these criticisms are likely exaggerated because they ignore the effects of capital flows on ETF rebalancing demand. Empirically, we find that capital flows substantially reduce the need for ETFs to rebalance when returns are large in magnitude and, therefore, mitigate the potential for these products to amplify volatility. We also show theoretically that flows can completely eliminate ETF rebalancing in the limit. * The views stated herein are those of the authors and are not necessarily the views of the Federal Reserve Board or the Federal Reserve System. For helpful comments, we thank Matt Gustafson, Jerry Hoberg, Jeremy Ko, and seminar participants at the Securities and Exchange Commission.

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Are Leveraged and Inverse ETFs the New Portfolio Insurers?

Cited by 8 publications

References 11 publications

Leveraged Funds and the Shadow Cost of Leverage Constraints

Leveraged Funds and the Shadow Cost of Leverage Constraints

Simulation of Leveraged ETF Volatility Using Nonparametric Density Estimation

Are Concerns About Leveraged ETFs Overblown?

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