Asymmetric Momentum Effects Under Uncertainty*

Kelsey, David; Kozhan, Roman; Pang, Wei

doi:10.1093/rof/rfq021

Cited by 18 publications

(5 citation statements)

References 22 publications

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“…Epstein and Schneider (2008) investigate the impact of information quality on investor behaviors and show that ambiguity-averse investors tend to react more to negative than positive information. Kelsey et al (2011) document that when momentum trading investors face ambiguity, they react differently to past winners and losers which creates an asymmetric momentum effect. Driouchi et al (2018) investigate the lead-lag relationship between option and stock markets during the 2006-2008 subprime crisis.…”

Section: Contextualisationmentioning

confidence: 99%

Investing during a Fintech Revolution: Ambiguity and return risk in cryptocurrencies

Luo

Mishra

Yarovaya

et al. 2021

Journal of International Financial Markets, Institutions and Mo

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Section: Contextualisationmentioning

confidence: 99%

Investing during a Fintech Revolution: Ambiguity and return risk in cryptocurrencies

Luo

Mishra

Yarovaya

et al. 2021

Journal of International Financial Markets, Institutions and Mo

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“…A similar shift in ambiguity preference is also documented by Ho, Keller and Keltyka () and Chakravarty and Roy (). Also, Kelsey, Kozhan and Pang () point out that under Knightian uncertainty or ambiguity investors react differently to past winners and losers and explain how momentum profitability relates to such an asymmetry. In line with this pattern of attitudes, we posit that the different impacts of positive and negative returns on investors’ ambiguity aversion and other sentimental attributes might explain the difficulties researchers face in modeling the interaction between market risk and ambiguity.…”

Section: Introductionmentioning

confidence: 99%

Improving Volatility Forecasts Using Market‐Elicited Ambiguity Aversion Information

Driouchi

2018

The Financial Review

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Distinguishing between risk and uncertainty, this paper proposes a volatility forecasting framework that incorporates asymmetric ambiguity shocks in the (exponential) generalized autoregressive conditional heteroskedasticity‐in‐mean conditional volatility process. Spanning 25 years of daily data and considering the differential role of investors' ambiguity attitudes in the gain and loss domains, our models capture a rich set of information and provide more accurate volatility forecasts both in‐sample and out‐of‐sample when compared to ambiguity‐free or risk‐based counterparts. Ambiguity‐based volatility‐timing trading strategies confirm the economic significance of our proposed framework and indicate that an annualized excess return of 3.2% over the benchmark could be earned from 1995 to 2014.

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“…In the "DOWN" states, the historical mean of returns of an equally weighted momentum strategy is -0.37% per month. Kelsey, Kozhan and Pang (2011) examine the asymmetric profitability of the momentum trading strategy. The different reactions of past winners IMPLIED PRICE RISK AND MOMENTUM STRATEGY 3 and past losers to market uncertainty create asymmetric patterns in price continuations.…”

Section: Introductionmentioning

confidence: 99%

“…The different reactions of past winners IMPLIED PRICE RISK AND MOMENTUM STRATEGY 3 and past losers to market uncertainty create asymmetric patterns in price continuations. Kelsey, Kozhan and Pang (2011) show that momentum is more likely to continue for downward trends in a highly uncertain market. Daniel and Moskowitz (2011) argue that the losses of momentum portfolio are due to the highly skewed returns of the momentum strategies.…”

Section: Introductionmentioning

confidence: 99%

Implied Price Risk and Momentum Strategy*

Chuang

2013

Review of Finance

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Examining the properties of stock returns has long been a central topic in finance. Most quantitative analyses conducted by academic researchers and practitioners focus only on the return distribution. However, the return distribution itself hardly helps to determine whether the price of a winner stock picked by using the momentum strategy reaches the level where the risk incurred from the falling of prices is imminent. Therefore, we construct an implied price risk index to quantify the downside risk of a stock and use it to manage the tail risk of the momentum strategy. The empirical results demonstrate that our modified strategy can not only achieve significant improvement on the overall performance, but also substantially reduce the drastic losses suffered from the 2008 global recession.We also establish the connection between the implied price risk index and the cross-sectional return differences based on the well-known three factors, the market beta, the firm size and the book-to-market ratio.

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Asymmetric Momentum Effects Under Uncertainty*

Cited by 18 publications

References 22 publications

Investing during a Fintech Revolution: Ambiguity and return risk in cryptocurrencies

Investing during a Fintech Revolution: Ambiguity and return risk in cryptocurrencies

Improving Volatility Forecasts Using Market‐Elicited Ambiguity Aversion Information

Implied Price Risk and Momentum Strategy*

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