Do it with a smile: Forecasting volatility with currency options

Reus, Lorenzo; Carrasco, Francisco Javier Cárcel; Pincheira, Pablo

doi:10.1016/j.frl.2019.07.024

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…When the market is in chaos, the daily price restriction mechanism acts as a circuit breaker. However, when it comes to the link between PLH (Price Limit Hits) and market volatility (Reus et al 2020). The supporters claim that price limits are efficient for minimizing price volatility, as, after the limits hits, the system effectively interrupts order flow.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

Chinthapalli¹

2021

JRFM

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In recent years, the attention of investors, practitioners and academics has grown in cryptocurrency. Initially, the cryptocurrency was designed as a viable digital currency implementation, and subsequently, numerous derivatives were produced in a range of sectors, including nonmonetary activities, financial transactions, and even capital management. The high volatility of exchange rates is one of the main features of cryptocurrencies. The article presents an interesting way to estimate the probability of cryptocurrency volatility clusters. In this regard, the paper explores exponential hybrid methodologies GARCH (or EGARCH) and through its portrayal as a financial asset, ANN models will provide analytical insight into bitcoin. Meanwhile, more scalable modelling is needed to fit financial variable characteristics such as ANN models because of the dynamic, nonlinear association structure between financial variables. For financial forecasting, BP is contained in the most popular methods of neural network training. The backpropagation method is employed to train the two models to determine which one performs the best in terms of predicting. This architecture consists of one hidden layer and one input layer with N neurons. Recent theoretical work on crypto-asset return behavior and risk management is supported by this research. In comparison with other traditional asset classes, these results give appropriate data on the behavior, allowing them to adopt the suitable investment decision. The study conclusions are based on a comparison between the dynamic features of cryptocurrencies and FOREX Currency’s traditional mass financial asset. Thus, the result illustrates how well the probability clusters show the impact on cryptocurrency and currencies. This research covers the sample period between August 2017 and August 2020, as cryptocurrency became popular around that period. The following methodology was implemented and simulated using Eviews and SPSS software. The performance evaluation of the cryptocurrencies is compared with FOREX currencies for better comparative study respectively.

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Section: Introductionmentioning

confidence: 99%

A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

Chinthapalli¹

2021

JRFM

View full text Add to dashboard Cite

show abstract

Do it with a smile: Forecasting volatility with currency options

Cited by 1 publication

References 21 publications

A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

A Comparative Analysis on Probability of Volatility Clusters on Cryptocurrencies, and FOREX Currencies

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