Risk quantification and validation for Bitcoin

Jiménez, Inés; Mora–Valencia, Andrés; Perote, Javier

doi:10.1016/j.orl.2020.06.004

Cited by 34 publications

(18 citation statements)

References 29 publications

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“…Given the weight of the return of every, the portfolio mean and variance can be straightforwardly obtained as in equations (28) and (29), respectively, where , +1 and , +1 are the conditional mean and standard deviation of asset i, and…”

Section: Risk Performance Modelmentioning

confidence: 99%

“…Regarding ES, we apply the ES Regression [32], ESR hereafter, a brand-new backtesting ES forecast and, to the best of our knowledge, barely used in cryptos [29]. We propose one of its specifications which consists in testing one-sided (such as most of the VaR backtest) beside two-sided alternatives: Intercept ESR which is the first test for ES stand-alone and consists of a regression framework for the forecast errors on an intercept term in the ES regression equation.…”

Section: Risk Performance Modelmentioning

confidence: 99%

“…Not only about their most differentiating characteristics [45][46][47][48] but also for the analysis of volatility models [43,[49][50][51][52][53] and risk forecasts [36]. Their behavior for hedging has been studied in portfolios with other kinds of assets [54,55], or with others cryptos [29,[56][57][58]. Our application fits in this latter framework, since GC models are applied to a portfolio with the three best known and most representative crypto assets: Bitcoin, Litecoin and Ripple.…”

Section: Cryptocurrenciesmentioning

confidence: 99%

“…Each of them has supporters and opponents, but VaR and ES have been the most used in the financial industry for the last years. Besides these traditional measures, we consider MS, which can be easily computed as a higher level of VaR and, to the best of our knowledge, is barely used related to cryptocurrencies [29], despite being a more robust measure in the presence of extreme events.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

Jiménez¹,

Mora–Valencia²,

Ñíguez³

et al. 2020

Preprint

Self Cite

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The semi-nonparametric (SNP) modeling of the return distribution has been proved to be a flexible and accurate methodology for portfolio risk management that allows two-step estimation of the dynamic conditional correlation (DCC) matrix. For this SNP-DCC model, we propose a stepwise procedure to compute pairwise conditional correlations under bivariate marginal SNP distributions, overcoming the curse of dimensionality. The procedure is compared to the assumption of Dynamic Equicorrelation (DECO), which is a parsimonious model when correlations among the assets are not significantly different but requires joint estimation of the multivariate SNP model. The risk assessment of both methodologies is tested for a portfolio on cryptocurrencies by implementing backtesting techniques and for different risk measures: Value-at-Risk, Expected Shortfall and Median Shortfall. The results support our proposal showing that the SNP-DCC model has better performance for a smaller confidence level than the SNP-DECO model, although both models perform similarly for higher confidence levels.

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Section: Risk Performance Modelmentioning

confidence: 99%

Section: Risk Performance Modelmentioning

confidence: 99%

Section: Cryptocurrenciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

Jiménez¹,

Mora–Valencia²,

Ñíguez³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are many papers on risk estimation for cryptocurrency data. Most notable papers include Acereda et al [37], Trucios et al [38] and Jimenez et al [39].…”

Section: Introductionmentioning

confidence: 99%

Compound distributions for financial returns

et al. 2020

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In this paper, we propose six Student's t based compound distributions where the scale parameter is randomized using functional forms of the half normal, Fré chet, Lomax, Burr III, inverse gamma and generalized gamma distributions. For each of the proposed distribution, we give expressions for the probability density function, cumulative distribution function, moments and characteristic function. GARCH models with innovations taken to follow the compound distributions are fitted to the data using the method of maximum likelihood. For the sample data considered, we see that all but two of the proposed distributions perform better than two popular distributions. Finally, we perform a simulation study to examine the accuracy of the best performing model.

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Trading commodity ETFs: Price behavior, investment insights, and performance analysis

Hadad,

Malhotra,

Nippani

2024

Journal of Futures Markets

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This study analyzes the risk‐adjusted performance of commodity exchange‐traded funds (ETFs) across diverse market conditions. Examining monthly returns from December 2004 to June 2022, our findings suggest that commodity ETFs underperformed US stocks, indicating limited diversification benefits. However, we document positive α during turbulent market periods like the COVID‐19 crisis, signifying potential resilience. Furthermore, our factor regressions reveal that shifts in the global commodity price index and disposable personal income significantly influence commodity ETFs' excess returns, pointing to broader economic and income‐related trends. Commodity ETFs exhibit lower Value‐at‐Risk and Expected Shortfall compared to stock market indices, indicating reduced downside risk exposure for investors. Given the increasing popularity of commodity ETFs, these insights hold substantial significance for market participants.

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Risk quantification and validation for Bitcoin

Cited by 34 publications

References 29 publications

Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

Portfolio Risk Assessment under Dynamic (Equi)Correlation and Semi-Nonparametric Estimation: an Application to Cryptocurrencies

Compound distributions for financial returns

Trading commodity ETFs: Price behavior, investment insights, and performance analysis

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