Fuzzy Value-at-Risk Forecasts Using a Novel Data-Driven Neuro Volatility Predictive Model

Thavaneswaran, A.; Thulasiram, Ruppa K.; Zhu, Zimo; Hoque, Mohammed Erfanul; Равишанкер, Налини

doi:10.1109/compsac.2019.10210

Cited by 22 publications

(4 citation statements)

References 12 publications

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“…As the value of a cryptocurrency is driven purely by the trust that is placed on them, and the transactions can happen at any point in time, volatility associated with cryptocurrencies is very high Baur and Dimpfl (2018); Peng et al (2018). Studies Thavaneswaran et al (2020Thavaneswaran et al ( , 2019; Liang et al (2020) focus on the estimation of an investment's volatility, along with other risk metrics like VaR. In 1952, Markowitz introduced modern portfolio theory with a framework to calculate optimal weights of assets in an investment portfolio Selection (1959).…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

Bowala

Singh

2022

JRFM

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Portfolio risk management plays an important role in successful investments. Portfolio standard deviation, value-at-risk, expected shortfall, and maximum absolute deviation are widely used portfolio risk measures. However, the existing portfolio risk measures are vulnerable to larger skewness and kurtosis of the asset returns. Moreover, the traditional assumption of normality of the portfolio returns leads to the underestimation of portfolio risk. Cryptocurrencies are a decentralized digital medium of exchange. In contrast to physical money, cryptocurrency payments exist purely as digital entries on an online ledger called blockchain that describe specific transactions. Due to the high volume and high frequency of cryptocurrency transactions, risk forecasting using daily data is not enough, and a high-frequency analysis is required. High-frequency data reveal a very high excess kurtosis and skewness for returns of cryptocurrencies. In order to incorporate larger skewness and kurtosis of the cryptocurrencies, a data-driven portfolio risk measure is minimized to obtain the optimal portfolio weights. A recently proposed data-driven volatility forecasting approach with daily data are used to study risk forecasting for cryptocurrencies with high-frequency (hourly) big data. The paper emphasizes the superiority of portfolio selection of cryptocurrencies by minimizing the recently proposed risk measure over the traditional minimum variance portfolio.

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Section: Literature Reviewmentioning

confidence: 99%

“…Recent studies in the area of computational finance have identified the negative effect of high kurtosis of the returns on traditional approaches Thavaneswaran et al (2019Thavaneswaran et al ( , 2020; Liang et al (2020). Furthermore, the normality assumption of returns leads to the underestimation of risks.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

Bowala

Singh

2022

JRFM

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“…A special version of the neural network model for time series which is called the neural network autoregression or NNAR model was introduced by [32]. In contrast to the GARCH model which computes volatility as the square root of the conditional variance, [33] directly models the volatility of a stock using a single hidden layer feed-forward NN volatility model which is based on the NNAR (p, P, k) model.…”

Section: The Nn Autoregressive (Nnar) Modelmentioning

confidence: 99%

“…In recent years, machine learning models especially the ANN model, have been widely implemented in financial risk forecasting due to their nature of modelling based on the historical data itself, giving higher forecast accuracy compared to existing time series models measuring volatility [28][29][30][31]. The neural network (NN) volatility predictive model framework based on the neural network autoregressive (NNAR) model by [32] to forecast financial risk measures was more effective than the GARCH (1,1) and the Heston-Nandi (HN)-GARCH (1,1) models were introduced by [33].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter

2022

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During the COVID-19 pandemic, cryptocurrency prices showed abnormal volatility that attracted the participation of many investors. Studying the behaviour of volatility for the prices of cryptocurrency is an interesting problem to be investigated. This research implements the state space model framework for volatility incorporating the Kalman filter. This method directly forecasts the conditional volatility of five cryptocurrency prices (Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC) and Bitcoin Cash (BCH)) for 10,000 consecutive hours, i.e., approximately 417 days during the COVID-19 pandemic from 26 February 2020, 00:00 h until 18 April 2021, 00:00 h. The performance of this model is compared to the GARCH (1,1) model and the neural network autoregressive (NNAR) based on root mean square error (RMSE), mean absolute error (MAE) and the volatility plot. The autocorrelation function plot, histogram and the residuals plot are used to examine the model adequacy. Among the three models, the state space model gives the best fit. The state space model gives the narrowest confidence interval of volatility and value-at-risk forecasts among the three models.

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Unveiling Portfolio Resilience: Harnessing Asymmetric Copulas for Dynamic Risk Assessment in the Knowledge Economy

2023

J Knowl Econ

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Fuzzy Value-at-Risk Forecasts Using a Novel Data-Driven Neuro Volatility Predictive Model

Cited by 22 publications

References 12 publications

Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

Optimizing Portfolio Risk of Cryptocurrencies Using Data-Driven Risk Measures

Forecasting the Volatility of Cryptocurrencies in the Presence of COVID-19 with the State Space Model and Kalman Filter

Unveiling Portfolio Resilience: Harnessing Asymmetric Copulas for Dynamic Risk Assessment in the Knowledge Economy

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