On the Properties of Leveraged ETFs (original) (raw)

Understanding the risk of leveraged ETFs

Finance Research Letters, 2010

The purpose of this paper is to clarify the risks of leveraged ETFs. We do this by showing how to construct a k-times leveraged ETF as a dynamic portfolio in the ETF and a money market account. This construction characterizes the return distribution of the leveraged ETF over any investment horizon. As a corollary, we show that a ktimes leveraged ETF will not earn k times the return of the ETF. It differs due to a term involving the ETF's volatility and the interest paid on the borrowing over the investment horizon.

Same Leverage, Less Volatility: An Alternative Approach to the Construction of Leveraged Funds

The Journal of Index Investing, 2016

This article introduces the concept of variable leverage for exchange-traded funds while keeping in mind that the target value for the leverage value is still fixed. This is done to ensure that the daily percentage hedging demand is kept constant, which in turn provides some assurance to investors that excessive buying and selling will not take place. By using simulations, the authors show that this newly constructed leveraged fund is better behaved than the usual constant multiple leveraged fund in terms of standard deviations and volatility of compounded returns.

The pricing and performance of leveraged exchange-traded funds

Journal of Banking & Finance, 2011

Leveraged ETFs are a recent and very successful financial innovation. They provide daily returns that are in a multiple or a negative multiple of the daily returns on a market benchmark. In this paper, we examine the characteristics, trading statistics, pricing efficiency and tracking errors of a sample of leveraged ETFs. We find that these ETFs are traded mainly by retail traders with very short holding periods. Price deviations (from NAV) are small on average, but large premiums and discounts are prone to occur. More interestingly, the behavior of premiums is different between bull (i.e., those with a positive multiple) and bear ETFs (i.e., those with a negative multiple). Our findings are consistent with the argument that the end-of-day rebalancing of the funds' exposures increases market volatility at the close of a trading day. As for tracking errors, they are small for holding periods of up to a week, but become increasingly larger for longer horizons.

The Properties of Short Term Investing in Leveraged ETFs

The daily returns on leveraged and inverse-leveraged exchange-traded funds (LETFs) are a multiple of the daily returns of a reference index. Because LETFs rebalance their leverage daily, their holding period returns can deviate substantially from the returns of a leveraged investment. While about half of LETF investors hold their investments for less than a month, the standard analysis of these investments uses a continuous time framework that is not appropriate for analyzing short holding periods, so the true effect of this daily rebalancing has not been properly ascertained. In this paper, we model tracking errors of LETFs compared to a leveraged investment in discrete time. For a period lasting a month or less, the continuous time model predicts tracking errors to be small. However, we find that in a discrete time model, daily portfolio rebalancing introduces tracking errors that are not captured in the continuous time framework. On average, portfolio rebalancing accounts for app...

Empirical Insights on the Trading Behavior of the UK Leveraged ETFs

Journal of Financial Innovation, 2017

Objective. This paper focuses on UK leveraged Exchange Traded Funds (ETFs) and examines their ability to meet their daily targets, the impact of volatility on targets’ achievement, and their pricing efficiency.Methodology. Standard regression analysis is used to evaluate performance, tracking efficiency and persistence in tracking failures, and the relationship between tracking efficiency and market volatility. Moreover, the pricing efficiency is examined along with the persistence in premium and the influence of market factors on premium.Findings. Results reveal that ETFs achieve their targets but occasionally tracking error can be significant. Furthermore, increases in market volatility relate to higher and lower tracking errors for bull and bear ETFs respectively. Moreover, average premiums testify a sufficient fit between trading prices and net asset values whereas the premiums are eliminated sharply. Moreover, the pricing efficiency of bear ETFs is positively associated with be...

Analysis and Comparison of Leveraged ETFs and CPPI-type Leveraged Strategies

HAL (Le Centre pour la Communication Scientifique Directe), 2013

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The Study of the Spillover and Leverage Effects of Financial Exchange Traded Funds (ETFs)

Mutual Funds, 2014

This study adopts the Generalized Autoregressive Conditional Heteroskedasticity-in-Mean Autoregressive Moving Average (GARCH-M-ARMA) and Exponentially Generalized Autoregressive Conditional Heteroskedasticity-in-Mean Autoregressive Moving Average (EGARCH-M-ARMA) models to analyze the spillover, asymmetric volatility, and leverage effects of financial exchange-traded funds (ETFs). The results show that bilateral relationships exist between financial and non-financial ETFs. Both ETFs have negative asymmetric volatility, suggesting that the value of stock indices and ETFs reveal conditional heterokesdasticity. Financial and non-financial ETFs also have negative leverage effects on benchmark indexes. Bilateral relations in terms of the spillover effects of volatilities and leverage effects exist between financial and non-financial ETFs.

A Theoretical Study on Leverage and Spillover Effects in Indian Equity ETFs

ComFin Research

Exchange-Traded Funds (ETFs) are innovative financial instrument as it is well-diversified like a mutual fund and listed in a stock exchange. Since the launch of the first ETF (Nifty BeES ETF) in 2001, the Indian ETF market has seen growth in the number of ETF schemes and Asset Under Management (AUM). This study is an overview of previous studies on spillover and leverage effects in the Indian Equity ETF market and related works to tap the research gap in these twin areas. The study found a need for a rigorous evaluation of the strength and nature of leverage effect among different Broader Index ETFs, Sectoral/thematic ETFs and World ETFs in India. The study also identified a research gap for the conduct of a study on the spillover of mean, volatility, risk between Equity ETFs and its benchmark index, and the speed of spillover effect would be immensely useful for investors and other stakeholders in the Indian Equity ETF market.

A study about the existence of the leverage effect in stochastic volatility models

Physica A: Statistical Mechanics and its Applications, 2009

The empirical relationship between the return of an asset and the volatility of the asset has been well documented in the financial literature. Named the leverage effect or sometimes risk-premium effect, it is observed in real data that, when the return of the asset decreases, the volatility increases and vice-versa. Consequently, it is important to demonstrate that any formulated model for the asset price is capable to generate this effect observed in practice. Furthermore, we need to understand the conditions on the parameters present in the model that guarantee the apparition of the leverage effect. In this paper we analyze two general specifications of stochastic volatility models and their capability of generating the perceived leverage effect. We derive conditions for the apparition of leverage effect in both of these stochastic volatility models. We exemplify using stochastic volatility models used in practice and we explicitly state the conditions for the existence of the leverage effect in these examples.

Exchange traded funds: leverage and liquidity

Applied Economics, 2018

This paper examines the differences between leveraged and unleveraged Exchange Traded Funds (ETFs), particularly for liquidity and volatility characteristics. The impact of leverage on intraday liquidity (spread and depth) is analysed in two periodsone of normal volatility and the other of abnormal/high volatility. There is a significant difference in spread and depth of leveraged and unleveraged ETFs in periods of both normal volatility and high volatility; however, this difference is more pronounced in higher volatility periods. In high volatility periods, liquidity typically diminishes in all ETFs, and this is even more so for the leveraged ETFs. When leveraged ETFs are segregated into multiples based on their power to replicate the underlying benchmark (i.e. multiples of −3, −2, −1, 2, 3), the difference in spreads between normal and high volatility periods is typically larger. The double-leveraged ETF has the most significant difference between the positive and negative counter parts. However, the relationship in the progression of the multiples does not change linearly to correspond with the level of volatility. This may be due to the nonlinear relation between volume and volatility. We shed light on the magnification effect of financial leverage on microstructure of the ETFs.