Risk Everywhere: Modeling and Managing Volatility

Bollerslev, Tim; Hood, Benjamin; Huss, John; Pedersen, Lasse Heje

doi:10.2139/ssrn.2722591

Cited by 55 publications

(84 citation statements)

References 95 publications

(70 reference statements)

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“…For example, Bansal and Yaron (2004) generate alphas as high as in the data only in 0.2% of the simulated samples. The other three models do even worse in matching 20 See also related work by Bollerslev et al (2016) and Tang and Whitelaw (2011) our estimates. This highlights that our volatility managed portfolios pose a statistically sharper challenge to these models than the standard risk-return tradeoff literature.…”

Section: Macro-finance Modelsmentioning

confidence: 51%

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Volatility Managed Portfolios

Moreira¹,

Muir²

2016

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Managed portfolios that take less risk when volatility is high produce large alphas, substantially increase factor Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the market, value, momentum, profitability, return on equity, and investment factors in equities, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in factor volatilities are not offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions and crises yet still earns high average returns. This rules out typical risk-based explanations and is a challenge to structural models of time-varying expected returns.

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Section: Macro-finance Modelsmentioning

confidence: 51%

“…Section 2 documents our main empirical results. Sec-3 See also related work by Bollerslev et al (2016) and Tang andWhitelaw (2011). 4 Daniel et al (2015) also look at a related strategy to ours for currencies.…”

Section: Introductionmentioning

confidence: 99%

Volatility Managed Portfolios

Moreira¹,

Muir²

2016

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“…These returns are robust to using the estimates of transaction costs reported in Marshall, Nguyen, and Visaltanochoti () and Bollerslev et al. () as well as to limiting various subsamples.…”

mentioning

confidence: 73%

“…More rigorously, consider the estimated average effective half‐spread for large commodity futures trades of 4.4 basis points in Marshall, Nguyen, and Visaltanochoti () (the estimated half‐spread is even lower in Bollerslev et al. () at 3.5 basis points). If we conservatively assume that basis‐momentum requires the investor to turn over three of four commodities in the long and short positions 12 times per year, the total transaction costs would add up to

12 \times 2 \times 2 \times 0.75 \times 4.4 = 158.4

basis points, which is well below average nearby returns of over 18%.…”

Section: Extensions and Robustness Checksmentioning

confidence: 99%

Basis‐Momentum

Boons¹,

Prado²

2018

The Journal of Finance

111

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We introduce a return predictor related to the slope and curvature of the futures term structure: basis‐momentum. Basis‐momentum strongly outperforms benchmark characteristics in predicting commodity spot and term premiums in both the time series and the cross section. Exposure to basis‐momentum is priced among commodity‐sorted portfolios and individual commodities. We argue that basis‐momentum captures imbalances in the supply and demand of futures contracts that materialize when the market‐clearing ability of speculators and intermediaries is impaired, and that it represents compensation for priced risk. Our findings are inconsistent with alternative explanations based on storage, inventory, and hedging pressure.

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“…The latter is defined as the log of the open price on day t minus the log of the close price on day t −1. This approach follows Bollerslev, Hood, Huss, and Pedersen (), among others. The data for constructing RV i , t were obtained from the Realized Library of the Oxford‐Man Institute of Quantitative Finance and are available from the year 2000 onwards (see Heber, Lunde, Shephard, & Sheppard, ).…”

Section: Empirical Analysismentioning

confidence: 99%

Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models

Conrad

Kleen

2020

J of Applied Econometrics

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Summary We examine the properties and forecast performance of multiplicative volatility specifications that belong to the class of generalized autoregressive conditional heteroskedasticity–mixed‐data sampling (GARCH‐MIDAS) models suggested in Engle, Ghysels, and Sohn (Review of Economics and Statistics, 2013, 95, 776–797). In those models volatility is decomposed into a short‐term GARCH component and a long‐term component that is driven by an explanatory variable. We derive the kurtosis of returns, the autocorrelation function of squared returns, and the R2 of a Mincer–Zarnowitz regression and evaluate the QMLE and forecast performance of these models in a Monte Carlo simulation. For S&P 500 data, we compare the forecast performance of GARCH‐MIDAS models with a wide range of competitor models such as HAR (heterogeneous autoregression), realized GARCH, HEAVY (high‐frequency‐based volatility) and Markov‐switching GARCH. Our results show that the GARCH‐MIDAS based on housing starts as an explanatory variable significantly outperforms all competitor models at forecast horizons of 2 and 3 months ahead.

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Risk Everywhere: Modeling and Managing Volatility

Cited by 55 publications

References 95 publications

Volatility Managed Portfolios

Volatility Managed Portfolios

Basis‐Momentum

Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models

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