Asymmetric jump beta estimation with implications for portfolio risk management

Alexeev, Vitali; Urga, Giovanni; Yao, Wenying

doi:10.1016/j.iref.2019.02.014

Cited by 10 publications

(7 citation statements)

References 70 publications

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“…Eraker et al (2003) and Bollerslev et al (2016) provide further evidence of signicant risk premia for the jump component (Bollerslev et al, 2016;Eraker et al, 2003). Bollerslev et al (2016), Alexeev et al (2017) and Alexeev et al (2019) report that individual stocks respond dierently to the market jumps than the continuous counterparts suggesting dierent systematic risk dynamics attributed to the market jumps. Gajurel et al (2020) further show that systemic risk and systematic jump risk are related.…”

Section: Introductionmentioning

confidence: 79%

Realized Volatility, Jump and Beta: evidence from Canadian Stock Market

Gajurel

Chowdhury

2021

Applied Economics

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Inclusion of jump component in the price process has been a long debate in nance literature. In this paper, we identify and characterize jump risks in the Canadian stock market using high-frequency data from the Toronto Stock Exchange. Our results provide a strong evidence of jump clustering -about 90% of jumps occur within rst 30 minutes of market opening for trade, and about 55% of jumps are due to the overnight returns. While average intraday jump is negative, jumps induced by overnight returns bring a cancellation eect yielding average size of the jumps to zero. We show that the economic signicance of jump component in volatility forecasting is very nominal. Our results further demonstrate that market jumps and overnight returns bring signicant changes in systematic risk (beta) of stocks. While the average eect of market jumps on beta is not signicantly dierent than zero, the eect of overnight returns on beta is signicant. Overall, our results suggest that jump risk is non-systematic in nature.

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Section: Introductionmentioning

confidence: 79%

Realized Volatility, Jump and Beta: evidence from Canadian Stock Market

Gajurel

Chowdhury

2021

Applied Economics

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“…This could lead to an increase in portfolio variance and ultimately reduce the benefits of diversification for investors. Moreover, ignoring the asymmetry of returns could lead to under-diversification of the portfolio and consequently increase the vulnerability to unexpected extreme negative market changes (Alexeev et al 2019). Hedging against these extreme events could be difficult for investors unless their portfolios are large enough to bear such risk.…”

Section: Discussionmentioning

confidence: 99%

“…They also emphasize that, when comparing periods with larger and smaller portfolio recommendations, market volatility and correlations are lower in periods where large portfolios are recommended. Moreover, Alexeev et al (2019) showed that there is a difference in recommended portfolio size during more extreme bounces, with the number of portfolio holdings during extreme market downturns being twice as high as the number of holdings during positive market shifts. As they point out, the asymmetry was found to be more pronounced during events that occur during periods of high market volatility.…”

Section: Impact Of Crises On Risk Diversificationmentioning

confidence: 99%

How Many Stocks Are Sufficient for Equity Portfolio Diversification? A Review of the Literature

Zaimović

Omanovic

Arnaut-Berilo

2021

JRFM

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Using extensive and comprehensive databases to select a subset of research papers, we aim to critically analyze previous empirical studies to identify certain patterns in determining the optimal number of stocks in well-diversified portfolios in different markets, and to compare how the optimal number of stocks has changed over different periods and how it has been affected by market turmoil such as the Global Financial Crisis (GFC) and the current COVID-19 pandemic. The main methods used are bibliometric analysis and systematic literature review. Evaluating the number of assets which lead to optimal diversification is not an easy task as it is impacted by a huge number of different factors: the way systematic risk is measured, the investment universe (size, asset classes and features of the asset classes), the investor’s characteristics, the change over time of the asset features, the model adopted to measure diversification (i.e., equally weighted versus optimal allocation), the frequency of the data that is being used, together with the time horizon, conditions in the market that the study refers to, etc. Our paper provides additional support for the fact that (1) a generalized optimal number of stocks that constitute a well-diversified portfolio does not exist for whichever market, period or investor. Recent studies further suggest that (2) the size of a well-diversified portfolio is larger today than in the past, (3) this number is lower in emerging markets compared to developed financial markets, (4) the higher the stock correlations with the market, the lower the number of stocks required for a well-diversified portfolio for individual investors, and (5) machine learning methods could potentially improve the investment decision process. Our results could be helpful to private and institutional investors in constructing and managing their portfolios and provide a framework for future research.

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“…Users can determine financial plans based on personal data [79]. This trend started from the discussions of insurance companies [78,85,86], who developed an integrated, knowledge-based model for AI (artificial intelligence), the robo-advisor [76,[81][82][83][84], to evaluate equity portfolios and study negative and positive reactions to portfolio risk management [77]. In addition, research on the adoption of mutual fund services has also been discussed [75].…”

Section: Research On Risk Management and Investmentmentioning

confidence: 99%

“…Regulators must secure and respect the conditions of the Portfolio risk management 34. The risk exposure of individual stocks with portfolios[77]…”

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confidence: 99%

Challenges and Trends of Financial Technology (Fintech): A Systematic Literature Review

2020

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Digital transformation creates challenges in all industries and business sectors. The development of digital transformation has also clearly triggered the emergence of fintech (financial technology) initiatives, which are recognized as some of the most important innovations in the financial industry. These initiatives are developing rapidly, driven in part by the sharing economy, regulations, and information technology. However, research in the field of fintech remains in its infancy. Fintech offers several services, such as funding, payment (including electronic wallets), e-aggregators, e-trading, and e-insurance, and cryptocurrencies such as Bitcoin. This provides an opportunity to more closely examine fintech’s research challenges and trends. This study aims to (1) determine the state of the art of financial technology research; (2) identify gaps in the financial technology research field; and (3) identify challenges and trends for future research potential. The novel proposal in this study includes theoretical contributions regarding financial technology. Using the systematic literature review approach of Kitchenham, in addition to thematic analysis, meta-analysis and observation to validate the quality of literature and analysis, the results of this study provide a theoretical basis fintech research from an information systems perspective, including the formulation of fintech technology concepts and their development.

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Asymmetric jump beta estimation with implications for portfolio risk management

Cited by 10 publications

References 70 publications

Realized Volatility, Jump and Beta: evidence from Canadian Stock Market

Realized Volatility, Jump and Beta: evidence from Canadian Stock Market

How Many Stocks Are Sufficient for Equity Portfolio Diversification? A Review of the Literature

Challenges and Trends of Financial Technology (Fintech): A Systematic Literature Review

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