Analysis of capital asset pricing model on Deutsche bank energy commodity

Latunde, Tolulope; Akinola, Lukman Shina; Dare, Damilola Deborah

doi:10.3934/gf.2020002

Cited by 22 publications

(7 citation statements)

References 16 publications

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“…As a speculative asset, it is critical for trading strategies to understand the volume-return relationship and furthermore shed light on potential implications. Due to the volatility of price, speculators could get speculative profits by using Bitcoin (Yermack, 2013;Latunde, Akinola and Dare, 2020). Thus, in the VFAs trading platform, the determinant of speculation should be heterogeneous.…”

Section: Namelymentioning

confidence: 99%

Re-examining Bitcoin Volatility: A CAViaR-based Approach

Dong

Floros

et al. 2021

Emerging Markets Finance and Trade

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The paper aims to explore the heterogeneous feature in the determination of Bitcoin volatility using a Markov regime-switching model and test its forecasting ability. The forecasting methodology of the risk measurement of Bitcoin's returns is based on the Conditional Autoregressive Value at Risk models (CAViaR) approach. Our results show that Bitcoin's volatility is significantly related to the volatility of the crypto-asset's return and the main determinants of volatility are speculation, investor attention, market interoperability and the interaction between speculation and market interoperability. In addition, we present evidence that investors' attention is the main source of volatility. Speculation and the interaction term are related in a "U-shaped" form, whereas investor attention and market interoperability show a linear trend on the volatility of Bitcoin.

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Section: Namelymentioning

confidence: 99%

Re-examining Bitcoin Volatility: A CAViaR-based Approach

Dong

Floros

et al. 2021

Emerging Markets Finance and Trade

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“…Yet not so many researchers have attempted to use the CAPM in examining energy commodities. Latunde, Akinola, and Dare conducted a research article utilizing the CAPM to explain the connection between risk and return of four Deutsche Bank crude oil assets [9]. Yet, Latunde, Akinola, and Dare's research was limited to the study of crude oil only.…”

Section: Literature Reviewmentioning

confidence: 99%

The Practice and Analysis of CAPM on Energy Commodity ETFs

Cao¹

2023

Advances in Economics, Business and Management Research

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As one of the most dominant asset pricing models in the field of finance, the Capital Asset Pricing Model (CAPM) is frequently used to assess the risk and expected return in project finance. Yet, this mathematical model is still lacking in studies in the field of energy commodities. In this paper, the CAPM (expected return, beta, risk-free rate, and market rate of return) and some statistical tools (mean, standard deviation, variance, and covariance) will be used to study the investment risks on expected returns of four common energy commodity ETFs (USO Oil, BNO Brent Oil, UNG Natural Gas, UGA Gasoline) from 2017 to 2021. The research findings show that those assets which have positive beta values (USO, BNO, and UGA) result in positive expected returns, and the only energy commodity ETF (UNG) that has a negative value of beta ends with a negative expected return. In addition, risks and expected returns are positively correlated to each other. This means that the larger the beta value (higher risk), the higher the expected return in general, and vice versa. For example, in this study, UGA which has the largest beta value (2.34) generates the highest expected return (0.3732), and UNG which has the smallest value of beta (-0.28) gains the lowest expected return (-0.0222) or suffers a loss in this study.

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“…However, the S&P 500 index is not traded from the perspective of asset allocation, so we use the data of SPDR S&P 500 Trust ETF to study the stock return volatility. The SPDR S&P 500 Trust ETF, also known as the SPY ETF, is one of the most popular funds that aims to track the Standard & Poor's 500 Index (see, e.g., Corbet & Twomey, 2014; Latunde et al, 2020; Xu et al, 2016; Xu & Yin, 2017). And the Granger causality results of Xu and Yin (2017) show that there is a strong interaction and a close correlation between the volatility of S&P 500 index and SPY ETF.…”

Section: Datamentioning

confidence: 99%

Forecasting stock return volatility: The role of shrinkage approaches in a data‐rich environment

Dai

Yang

2022

Journal of Forecasting

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This paper employs the prevailing shrinkage approaches, the lasso, adaptive lasso, elastic net, and ridge regression to predict stock return volatility with a large set of variables. The out-of-sample results reveal that shrinkage approaches exhibit superior performance relative to the benchmark of the autoregressive model and a series of competing models in terms of the out-ofsample R-square and the model confidence set. By using shrinkage methods to allocate portfolio, a mean-variance investor can obtain significant economic gains. Overall, our findings confirm that shrinkage approaches can effectively improve stock return volatility forecasting in a data-rich environment.

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Analysis of capital asset pricing model on Deutsche bank energy commodity

Cited by 22 publications

References 16 publications

Re-examining Bitcoin Volatility: A CAViaR-based Approach

Re-examining Bitcoin Volatility: A CAViaR-based Approach

The Practice and Analysis of CAPM on Energy Commodity ETFs

Forecasting stock return volatility: The role of shrinkage approaches in a data‐rich environment

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