Risk Everywhere: Modeling and Managing Volatility

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

doi:10.1093/rfs/hhy041

Cited by 276 publications

(117 citation statements)

References 60 publications

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“…Notice also that V ar(ζ t ) is actually below V ar(μ t ), so these are strong upper bounds. 23 See also related work by Bollerslev et al (2016) and Tang and Whitelaw (2011). The dashed line shows the point estimate in the historical sample.…”

Section: A Macrofinance Modelsmentioning

confidence: 99%

Volatility‐Managed Portfolios

Moreira¹,

Muir

2017

The Journal of Finance

413

190

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

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Section: A Macrofinance Modelsmentioning

confidence: 99%

Volatility‐Managed Portfolios

Moreira¹,

Muir

2017

The Journal of Finance

413

190

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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 (2018), 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, 2009).…”

Section: Stock Market Datamentioning

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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“…Given that basis and momentum strategies are already applied in practice and are not too different in stability and composition from basis-momentum, it is unlikely that transaction costs subsume the large basis-momentum returns. More rigorously, consider the estimated average effective half-spread for large commodity futures trades of 4.4 basis points in Marshall, Nguyen, and Visaltanochoti (2012) (the estimated half-spread is even lower in Bollerslev et al (2016) 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 × 2 × 2 × 0.75 × 4.4 = 158.4 basis points, which is well below average nearby returns of over 18%.…”

Section: B Composition and Stability Of Basis-momentum Portfoliosmentioning

confidence: 99%

“…2 These returns are robust to using the estimates of transaction costs reported in Marshall, Nguyen, and Visaltanochoti (2012) and Bollerslev et al (2016) as well as to limiting various subsamples. 3 For empirical evidence on the basis (the difference between the futures and spot price) and momentum, see, for example, Moskowitz, Ooi, and Pedersen (2012), Yang (2013), Szymanowska et al (2014), Bakshi, Gao, and Rossi (2017), and Koijen et al (2018).…”

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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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Risk Everywhere: Modeling and Managing Volatility

Cited by 276 publications

References 60 publications

Volatility‐Managed Portfolios

Volatility‐Managed Portfolios

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

Basis‐Momentum

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