Indirect effects of flow-performance sensitivity on fund performance

Seok, Sangik; Cho, Hoon; Lee, Jennifer Eunkyeong; Ryu, Doojin

doi:10.1016/j.bir.2023.08.002

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…We implement the Wald statistic for the QLR test (Quandt 1960) as a modification of the Chow test (Chow 1960) on a dynamic linear model for the exogenous variable to locate the structural break date. The model is a common practice when the dates for the break are unknown (Éric Racicot and Théoret 2016;Seok et al 2023;Yang et al 2023). To reveal the unknown structural breaks, we exclude 15% of observations from each site in the time domain to avoid the short interval for the fitted models in the Chow test.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the resulting SB-GARCH-MIDAS model is useful in cases where the low-frequency variables exhibit a structural break. The models obtain the SB through the Quandt likelihood ratio (QLR) test (Quandt 1960), which is a modification of the Chow test (Chow 1960) and a common practice when the date of the break is not known (Éric Racicot and Théoret 2016;Seok et al 2023;Yang et al 2023). The monetary policy is assumed to follow an autoregressive process, as also done by Vidal-Tomás and Ibañez (2018).…”

Section: Introductionmentioning

confidence: 99%

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Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

Alam,

Amendola,

Candila

et al. 2024

Econometrics

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The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

Alam,

Amendola,

Candila

et al. 2024

Econometrics

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“…Managers may resort to herding behavior driven by career apprehensions; however, superior historical performance has the potential to attract new subscriptions, thereby diminishing the propensity for herding [67,68]. As elucidated earlier, managers ought to recognize that subsequent cash inflows may entail substantially higher costs for acquiring herded stocks due to short-term price escalations, particularly pronounced during periods of high market sentiment, especially for smaller and growing stocks [69].…”

Section: Herding Likelihoodmentioning

confidence: 99%

Does Herding and Anti-Herding Reflect Portfolio Managers’ Abilities in Emerging Markets?

Sheng,

Montgomery

2024

Mathematics

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This study investigates the relationship between herding behaviors and the abilities of Chinese mutual fund managers. Adapting existing methodologies to suit the low information disclosure environment of the Chinese market, we measure herding behaviors and managers’ abilities. Our analysis goes beyond traditional approaches by examining the contribution of herding outcomes to picking and timing abilities linked with mutual fund flows. Moreover, we extend this investigation to incorporate manager replacements and different market conditions. Our findings reveal that moderate herding is associated with enhanced picking abilities, particularly in bull markets. However, this effect is partly counteracted by positive mutual fund flows, suggesting that managers adjust their strategies in response to fund inflows. Excessive herding in bull markets is linked to reduced timing abilities, although this negative impact is mitigated by high turnover. Conversely, managers with anti-herding skills exhibit lower picking abilities. We observe that managerial replacements are driven by poor performance rather than considerations of current abilities. Nonetheless, under a new manager, herding behavior reflects improved picking abilities, indicating a potential shift in managerial strategies. Overall, our study provides valuable insights into the relationship between herding behaviors and managerial competencies in the Chinese mutual fund industry, highlighting the nuances of decision making in different market contexts.

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Effects of option incentive compensation on corporate innovation: The case of China

Cheng,

Frijns,

Kim

et al. 2024

Economic Systems

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Indirect effects of flow-performance sensitivity on fund performance

Cited by 3 publications

References 43 publications

Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach

Does Herding and Anti-Herding Reflect Portfolio Managers’ Abilities in Emerging Markets?

Effects of option incentive compensation on corporate innovation: The case of China

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