Mustafa Kamal, Sabir Ali Siddiqui, Nayabuddin, Afaf Alrashidi · 11 authors
The study and investigation of the behavior of monetary phenomena is an interesting subject for actuaries and practitioners. In the recent age and development in the monetary and financial phenomena, cryptocurrency has gained much attention from actuaries. Over the past decade, several research studies have emerged on modeling and forecasting cryptocurrency exchange rates. This paper also contributes to the modeling of cryptocurrency exchange rates using a new version of the Logistic distribution, namely, a new cotangent-Logistic distribution. The mathematical properties and estimators of the new cotangent-logistic distribution's parameters are obtained. We illustrate the new cotangent-Logistic distribution using two financial data sets representing the log-returns of the Bitcoin and Ethereum prices. We compare the new cotangent-Logistic distribution with the baseline Logistic distribution and its modified version. Using the p-value and three other statistical tests, we show that the new cotangent-Logistic distribution repeatedly provides the optimal fit to cryptocurrency exchange rates.
In this paper, we conducted an empirical investigation of the realized volatility of cryptocurrencies using an econometric approach. This work’s two main characteristics are: (i) the realized volatility to be forecast filters jumps, and (ii) the benefit of using various historical/implied volatility indices from brokers as exogenous variables was explicitly considered. We feature a jump-robust extension of the REGARCH-MIDAS-X model incorporating realized beta GARCH processes and MIDAS filters with monthly, daily, and hourly components. First, we estimated six jump-robust estimators of realized volatility for Bitcoin and Ethereum that were retained as the dependent variable. Second, we inserted ten Bitcoin and Ethereum volatility indices gathered from various exchanges as an exogenous variable, each at a time. Third, we explored their forecasting ability based on the MSE and QLIKE statistics. Our sample spanned the period from May 2018 to January 2023. The main result featured the best predictors among the volatility indices for Bitcoin and Ethereum derived from 30-day implied volatility. The significance of the findings could mostly be attributable to the ability of our new model to incorporate financial and technological variables directly into the specification of the Bitcoin and Ethereum volatility dynamics.
Cryptocurrencies have increasingly attracted the attention of several players interested in crypto assets. Their rapid growth and dynamic nature require robust methods for modeling their volatility. The Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) model is a well-known mathematical tool for predicting volatility. Nonetheless, the Realized-GARCH model has been particularly under-explored in the literature involving cryptocurrency volatility. This study emphasizes an investigation on the performance of the Realized-GARCH against a range of GARCH-based models to predict the volatility of five prominent cryptocurrency assets. Our analyses have been performed in both in-sample and out-of-sample cases. The results indicate that while distinct GARCH models can produce satisfactory in-sample fits, the Realized-GARCH model outperforms its counterparts in out of-sample forecasting. This paper contributes to the existing literature, since it better reveals the predictability performance of Realized-GARCH model when compared to other GARCH-types analyzed when an out-of-sample case is considered.
Pavlos I. Zitis, Shinji Kakinaka, Ken Umeno, Stavros G. Stavrinides · 6 authors
The COVID-19 pandemic has had an unprecedented impact on the global economy and financial markets. In this article, we explore the impact of the pandemic on the weak-form efficiency of the cryptocurrency and forex markets by conducting a comprehensive comparative analysis of the two markets. To estimate the weak-form of market efficiency, we utilize the asymmetric market deficiency measure (MDM) derived using the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach, along with fuzzy entropy, Tsallis entropy, and Fisher information. Initially, we analyze the temporal evolution of these four measures using overlapping sliding windows. Subsequently, we assess both the mean value and variance of the distribution for each measure and currency in two distinct time periods: before and during the pandemic. Our findings reveal distinct shifts in efficiency before and during the COVID-19 pandemic. Specifically, there was a clear increase in the weak-form inefficiency of traditional currencies during the pandemic. Among cryptocurrencies, BTC stands out for its behavior, which resembles that of traditional currencies. Moreover, our results underscore the significant impact of COVID-19 on weak-form market efficiency during both upward and downward market movements. These findings could be useful for investors, portfolio managers, and policy makers.
The issue related to the quantification of the tail risk of cryptocurrencies is considered in this paper. The statistical methods used in the study are those concerning recent developments in Extreme Value Theory (EVT) for weakly dependent data. This research proposes an expectile-based approach for assessing the tail risk of dependent data. Expectile is a summary statistic that generalizes the concept of mean, as the quantile generalizes the concept of the median. We present the empirical findings for a dataset of cryptocurrencies. We propose a method for dynamically evaluating the level of the expectiles by estimating the level of the expectiles of the residuals of a heteroscedastic regression, such as a GARCH model. Finally, we introduce the Marginal Expected Shortfall (MES) as a tool for measuring the marginal impact of single assets on systemic shortfalls. In our case of interest, we are focused on the impact of a single cryptocurrency on the systemic risk of the whole cryptocurrency market. In particular, we present an expectile-based MES for dependent data.
We investigate the benefits of using intraday realized volatility (RV) commonality, and propose a novel non-parametric framework for forecasting one-day ahead intraday RV (1D-ahead intraday RV). Specifically, we train multiple models using machine learning (ML) techniques under various training settings (single-asset, cluster-driven, and cross-asset), where commonality gradually enters model dynamics as training schemes become more complex. We conclude that models that leverage the cryptocurrency commonality outperform models that do not explicitly account for it, regardless of the market regime considered. The source code of this project is available at: github.com/edjanga/crypto_volatility_commonality.
The effect of the Russia–Ukraine war has fluctuated in Europe and Asia's economic conjuncture by virtue of constant shifting balances. The portfolios of investors who made decisions in uncertain conditions have been affected by these fluctuations that have caused volatility in the stock market's indexes. The aim of this study is to examine the impact of the Fear Index (FI), the Dollar Index, and Bitcoin on the volatility of the Borsa Istanbul 100 Index (BIST). Autoregressive distributed lag (ARDL) time series analysis was used for the study, which revealed that the Dollar Index has no effect on volatility, while the FI was found to have an effect on volatility both in the short and long runs. In addition, Bitcoin was determined to have an effect on volatility only in the long run. When the period of the data used is examined, the outbreak of the Russia–Ukraine war in February 2022 is thought to be the reason for the increase in the FI. It can be assumed that the decisions of investors to invest in the BIST were adversely affected by the war as a natural consequence of this, and investors who ceased investing in the BIST index opted to invest elsewhere.
Kokulo K. Lawuobahsumo, Bernardina Algieri, Arturo Leccadito
Abstract This study aims to jointly predict conditional quantiles and tail expectations for the returns of the most popular cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin and Litecoin) using financial and macroeconomic indicators as explanatory variables. We adopt a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to make one- and five-steps-ahead predictions of Value-at-Risk (VaR) and Expected Shortfall (ES) based on a rolling window and compare the performance of our model against the Historical simulation and the standard ARMA(1,1)-GARCH(1,1) model used as benchmarks. The superior set of models is then chosen by backtesting VaR and ES using a Model Confidence Set procedure. Our results show that the MCQRNN performs better than both benchmark models for jointly predicting VaR and ES when considering daily data. Models with the implied volatility index, treasury yield spread and inflation expectations sharpen the extreme return predictions. The results are consistent for the two risk measures at the 1% and 5% level both, in the case of a long and short position and for all cryptocurrencies.
Paweł Szydło, Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
A non-fungible token (NFT) market is a new trading invention based on the blockchain technology, which parallels the cryptocurrency market. In the present work, we study capitalization, floor price, the number of transactions, the inter-transaction times, and the transaction volume value of a few selected popular token collections. The results show that the fluctuations of all these quantities are characterized by heavy-tailed probability distribution functions, in most cases well described by the stretched exponentials, with a trace of power-law scaling at times, long-range memory, persistence, and in several cases even the fractal organization of fluctuations, mostly restricted to the larger fluctuations, however. We conclude that the NFT market-even though young and governed by somewhat different mechanisms of trading-shares several statistical properties with the regular financial markets. However, some differences are visible in the specific quantitative indicators.
This paper proposes a new investment strategy in the cryptocurrency market based on a two-step procedure. The first step is the computation of the asset's levels of efficiency in an universe of cryptocurrencies. Price returns efficiency degrees are measured by their corresponding levels of multifractality, obtained by the multifractal detrended fluctuation analysis method. The higher the multifractality, the higher the inefficiency in terms of the weak form of market efficiency. Cryptocurrencies are then ranked in terms of efficiency. The second step is the construction of portfolios under the Markowitz framework composed of the most/least efficient digital coins. Minimum variance, maximum Sharpe ratio, equally weighted and (in)efficient-based portfolios were considered. The former strategy is also proposed, where the weights are computed proportionally to the assets levels of (in)efficiency. The main findings are: cryptocurrency price returns are multifractal and their levels of (in)efficiency change over time; returns exhibit left-sided asymmetry, which implies that subsets of large fluctuations contribute substantially to the multifractal spectrum; in bull markets portfolios with the least efficiency assets provided a better risk–return relation; in periods of high volatility and high price depreciation (bear market) a better performance is associated with the portfolios composed by the more efficient cryptocurrencies.
José Antonio Núñez Mora, Mario Iván Contreras-Valdez, Roberto J. Santillán‐Salgado
This paper reports our findings on the return dynamics of Bitcoin and Ethereum using high-frequency data (minute-by-minute observations) from 2015 to 2022 for Bitcoin and from 2016 to 2022 for Ethereum. The main objective of modeling these two series was to obtain a dynamic estimation of risk premium with the intention of characterizing its behavior. To this end, we estimated the Generalized Autoregressive Conditional Heteroskedasticity in Mean with Normal-Inverse Gaussian distribution (GARCH-M-NIG) model for the residuals. We also estimated the other parameters of the model and discussed their evolution over time, including the skewness and kurtosis of the Normal-Inverse Gaussian distribution. Similarly, we determined the parameters that define the evolution of the estimated variance, i.e., the parameters related to the fitted past variance, square error and long-term average value. We found that, despite the market uncertainty during the COVID-19 emergency period (2020 and 2021), the selected cryptocurrencies’ return volatility and kurtosis were even greater for several other subperiods within our sample’s time frame. Our model represents an analytical tool that estimates the risk premium that should be delivered by Bitcoin and Ethereum and is therefore of interest to risk managers, traders and investors.
Nghiên cứu này sử dụng các mô hình GARCH, bao gồm EGARCH(1,1), GJR-GARCH(1,1), TGARCH(1,1) và APARCH(1,1) để khảo sát sự bất đối xứng trong biến động tỷ suất sinh lợi của các loại tiền điện tử như Bitcoin, Ethereum, Ripple (XRP), Binance Coin (BNB) và DigiByte (DGB) trong khoảng thời gian từ ngày 01 tháng 01 năm 2018 đến ngày 31 tháng 5 năm 2023. Kết quả cho thấy mô hình EGARCH(1,1) là mô hình tốt nhất để mô tả hiệu ứng bất đối xứng trong biến động tỷ suất sinh lợi của các chuỗi tiền điện tử. Sự biến động tăng nhiều hơn trong phản ứng với cú sốc tích cực hơn là cú sốc tiêu cực, hàm ý một hiệu ứng bất đối xứng khác với hiệu ứng thường thấy trên thị trường chứng khoán. Kết quả nghiên cứu giúp nhà đầu tư và nhà quản lý rủi ro trong thị trường tiền điện tử hiểu rõ hơn về sự biến động giá, nhận biết, đánh giá rủi ro một cách chính xác hơn và đưa ra các chiến lược đầu tư phù hợp.
This study examines the role of cryptocurrencies as a hedging and safe-haven instrument against stock market risk. Employing five of the largest cryptocurrencies by market capitalization: BTC, ETH, BNB, ADA, and XRP, from 2017–2022 in a variance-optimal hedging framework we investigate and compare the hedging effectiveness of cryptocurrencies for the developed G7 and emerging BRICS stock markets. Based on EVT we introduced a new approach to the assessment of hedging effectiveness. We found that the probability of at least 10-percent hedging effectiveness of Bitcoin is approximately equal to zero. The conditional probability that Bitcoin can reduce at least 10% of volatility given that index returns fall below the 1st percentile is higher and ranges from 2% to 28.4% depending on the stock market. The probabilities estimated for other cryptocurrencies are lower. We provide new and valuable knowledge for investors, who consider cryptocurrencies as a shelter for their investment portfolios.
The S&P 500 index is considered the most popular trading instrument in financial markets. With the rise of cryptocurrencies over the past years, Bitcoin has also grown in popularity and adoption. The paper aims to analyze the daily return distribution of the Bitcoin and S&P 500 index and assess their tail probabilities through two financial risk measures. As a methodology, We use Bitcoin and S&P 500 Index daily return data to fit The seven-parameter General Tempered Stable (GTS) distribution using the advanced Fast Fractional Fourier transform (FRFT) scheme developed by combining the Fast Fractional Fourier (FRFT) algorithm and the 12-point rule Composite Newton-Cotes Quadrature. The findings show that peakedness is the main characteristic of the S&P 500 return distribution, whereas heavy-tailedness is the main characteristic of the Bitcoin return distribution. The GTS distribution shows that $80.05\%$ of S&P 500 returns are within $-1.06\%$ and $1.23\%$ against only $40.32\%$ of Bitcoin returns. At a risk level ($α$), the severity of the loss ($AVaR_α(X)$) on the left side of the distribution is larger than the severity of the profit ($AVaR_{1-α}(X)$) on the right side of the distribution. Compared to the S&P 500 index, Bitcoin has $39.73\%$ more prevalence to produce high daily returns (more than $1.23\%$ or less than $-1.06\%$). The severity analysis shows that at a risk level ($α$) the average value-at-risk ($AVaR(X)$) of the bitcoin returns at one significant figure is four times larger than that of the S&P 500 index returns at the same risk.
Wael Hemrit, Noureddine Benlagha, Racha Ben Arous, Mounira Ben Arab
Summary In this paper, we examine the connectedness between volatilities for various non‐fungible tokens (NFTs) and developed stock markets during the period from July 1, 2018, to June 15, 2022. With the use of the time‐varying connectedness methods to explore the volatility interdependences among these assets, we find that there is a significant volatility connectedness during Russia's invasion of Ukraine and COVID‐19 periods. Evidence emerging from this study advocates the inclusion of NFTs in developed stock markets for medium and long time periods only. The results also suggest that UK and Germany stock markets are the predominant market of spillover transmission, whereas the XTZ is the top net recipient/transmitter of volatility connectedness shocks. Moreover, Chinese stock market and ENJ offer more diversification gains than others, and the volatility connectedness from US stock market to NFTs is more pronounced in the long‐term than the short‐term. Our research provides some urgent and prominent insights to help investors and policymakers to be aware that NFTs are important hedge assets that should be added to stock portfolios during periods of geopolitical stability and in the post‐pandemic times.
Abstract The ınvestment decisions of institutional and individual investors in financial markets are largely influenced by market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of the prices and returns of the investment instruments becomes imperative for successful investment. In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. Using the opening data of weekly Bitcoin prices for the period of 11.24.2013–03.22.2020, their logarithmic returns were calculated. The stationarity properties of the Bitcoin return series was tested by applying the ADF unit root test and the series were found to be stationary. After reaching the average equation model as ARMA (2.2), it was tested whether there was an ARCH effect in the ARMA (2,2) model. As a result of the applied ARCH-LM test, it is reached that the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection of ARCH effect has been tried to predict with conditional variance models such as ARCH (1), ARCH (2), ARCH (3), GARCH (1,1), GARCH (1,2), GARCH (1,3), GARCH (2,1), GARCH (2,2), EGARCH (1,1) and EGARCH (1,2). While the obtained findings indicate that the best model is in the direction of GARCH (1,1) according to Akaike info criterion, it was found that GARCH (1,1) model does not have ARCH effect as a result of the applied ARCH-LM test. Thus, our empirical findings highlight an ample guide on appropriate modeling of price information in the Bitcoin market.
Leonardo H.S. Fernandes, JOSÉ W. L. SILVA, Aurelio F. Bariviera, Kleber E S Sobrinho · 5 authors
This paper sheds light on the changes suffered in cryptocurrencies due to the COVID-19 shock through a non-linear cross-correlations and similarity perspective. We have collected daily price and volume data for the seven largest cryptocurrencies considering trade volume and market capitalization. For both attributes (price and volume), we calculate their volatility and compute the Multifractal Detrended Cross-Correlations (MF-DCCA) to estimate the complexity parameters that describe the degree of multifractality of the underlying process. We detect (before and during COVID-19) a standard multifractal behaviour for these volatility time series pairs and an overall persistent long-term correlation. However, multifractality for price volatility time series pairs displays more persistent behaviour than the volume volatility time series pairs. From a financial perspective, it reveals that the volatility time series pairs for the price are marked by an increase in the non-linear cross-correlations excluding the pair Bitcoin vs Dogecoin (í µí»¼ í µí±¥í µí±¦ (0) = −1.14%). At the same time, all volatility time series pairs considering the volume attribute are marked by a decrease in the non-linear cross-correlations. The K-means technique indicates that these volatility time series for the price attribute were resilient to the shock of COVID-19. While for these volatility time series for the volume attribute, we find that the COVID-19 shock drove changes in cryptocurrency groups.
Lai Ting, M. M. Abd El‐Raouf, M. E. Bakr, Arwa M. Alsahangiti
Statistical modeling and forecasting are very important for decision-making in any field of life. This paper has two major objectives, namely, statistical modeling and forecasting of real phenomena. For covering the first aim (i.e., statistical modeling), we introduce a new probabilistic model. The new model is introduced by mixing the Dagum distribution with the weighted TX family approach. The proposed model is called the weighted TX Dagum distribution and possesses heavy-tailed characteristics. The new model is illustrated by analyzing real-life data related to Bitcoin prices. To cover the second aim (i.e., forecasting), we take into account six macroeconomic and financial indicators to investigate their impact on Bitcoin prices such as the Adaptive least absolute shrinkage and selection operator (Alasso), elastic net, and minimax concave penalty. After analyzing the data, it is found that Alasso and MCP have retained all the included predictors, except import, while Enet holds all the predictors. The root means square error and mean absolute error associated with MCP are lower than Alasso and Enet, which reveals that MCP fits the data very well as compared to rival methods.
With the rapid development of cryptocurrencies, the volatility characteristics of their yields have received more and more attention. At the same time, many empirical studies show that the GARCH family model is more effective in describing the volatility of financial time series. Firstly, this paper briefly introduces the research background of cryptocurrency and the research method using GARCH model. Next, the daily rate of return is calculated and descriptive statistical analysis is carried out on the collected closing price data of cryptocurrency, and on this basis, the GARCH model is constructed for empirical test to explore the volatility characteristics of its rate of return. Then the corresponding research conclusions and relevant policy recommendations are given.
Timothy Kayode Samson, Christian Elendu Onwukwe, Adedoyin Isola Lawal
With escalating public interest in the cryptocurrency market, largely driven by its perceived potential for rapid wealth accumulation and various advantages over traditional currencies, there is an imperative to understand its inherent volatility.This study addresses the dynamic behaviour of cryptocurrencies by utilizing skewed error innovation distributions to model the volatility of five key cryptocurrencies.Data was sourced from Yahoo Finance, encompassing daily closing prices from September 11, 2017, to April 8, 2022.The significance of the skewness parameter in all optimal volatility models (p<.05) substantiates the application of skewed error innovation distributions.Notably, the observed influence of past negative events on volatility was consistently greater than that of positive events across most examined cryptocurrencies.While Value at Risk (VaR) models are frequently used for risk measurement in this domain, this study's findings suggest that their reliability is not universal across all cryptocurrency cases.Consequently, caution is advised when employing VaR models for risk assessment associated with cryptocurrencies.
Abstract The growing attention on cryptocurrencies has led to increasing research on digital stock markets. Approaches and tools usually applied to characterize standard stocks have been applied to the digital ones. Among these tools is the identification of processes of market fluctuations. Being interesting stochastic processes, the usual statistical methods are appropriate tools for their reconstruction. There, besides chance, the description of a behavioural component shall be present whenever a deterministic pattern is ever found. Markov approaches are at the leading edge of this endeavour. In this paper, Markov chains of orders one to eight are considered as a way to forecast the dynamics of three major cryptocurrencies. It is accomplished using an empirical basis of intra-day returns. Besides forecasting, we investigate the existence of eventual long-memory components in each of those stochastic processes. Results show that predictions obtained from using the empirical probabilities are better than random choices.
Edosa Getachew Taera, Budi Setiawan, Adil Saleem, Andi Sri Wahyuni · 7 authors
This study investigates the volatility and external shock persistence within the financial and alternative assets markets during times of crises triggered by Covid-19 and the war in Ukraine. Univariate GARCH family models are used to capture the effect of financial turmoil caused by recent crises. Five different class of assets (which includes Islamic, ESG, Conventional, Crypto, FinTech, and commodities) have been chosen to represent a sample of the worldwide traditional financial market and alternative assets. The findings of this study revealed that almost all financial and alternative assets experienced an increase in volatility, except Bitcoin, across all observation periods. Islamic stock and ESG indexes exhibited high volatility before the Covid-19 outbreak. During the pandemic, all assets became more volatile. In addition, Islamic equities and ESG indexes showed relatively lower risk compared to conventional stocks and other alternative assets during the war. Multiple financial assets tend to be highly volatile during crises; however, global investors need to consider the advantages of incorporating Islamic stocks and ESG indexes as part of their investment portfolio innovation strategy, particularly in the presence of geopolitical risk.
Using the capital asset pricing model, this article critically assesses the relative importance of computing ‘realized’ betas from high-frequency returns for Bitcoin and Ethereum—the two major cryptocurrencies—against their classic counterparts using the 1-day and 5-day return-based betas. The sample includes intraday data from 15 May 2018 until 17 January 2023. The microstructure noise is present until 4 min in the BTC and ETH high-frequency data. Therefore, we opt for a conservative choice with a 60 min sampling frequency. Considering 250 trading days as a rolling-window size, we obtain rolling betas < 1 for Bitcoin and Ethereum with respect to the CRIX market index, which could enhance portfolio diversification (at the expense of maximizing returns). We flag the minimal tracking errors at the hourly and daily frequencies. The dispersion of rolling betas is higher for the weekly frequency and is concentrated towards values of β > 0.8 for BTC (β > 0.65 for ETH). The weekly frequency is thus revealed as being less precise for capturing the ‘pure’ systematic risk for Bitcoin and Ethereum. For Ethereum in particular, the availability of high-frequency data tends to produce, on average, a more reliable inference. In the age of financial data feed immediacy, our results strongly suggest to pension fund managers, hedge fund traders, and investment bankers to include ‘realized’ versions of CAPM betas in their dashboard of indicators for portfolio risk estimation. Sensitivity analyses cover jump detection in BTC/ETH high-frequency data (up to 25%). We also include several jump-robust estimators of realized volatility, where realized quadpower volatility prevails.
In this paper, a hybrid of a Wavelet Decomposition–Generalised Auto-Regressive Conditional Heteroscedasticity–Extreme Value Theory (WD-ARMA-GARCH-EVT) model is applied to estimate the Value at Risk (VaR) of BitCoin (BTC/USD) and the South African Rand (ZAR/USD). The aim is to measure and compare the riskiness of the two currencies. New and improved estimation techniques for VaR have been suggested in the last decade in the aftermath of the global financial crisis of 2008. This paper aims to provide an improved alternative to the already existing statistical tools in estimating a currency VaR empirically. Maximal Overlap Discrete Wavelet Transform (MODWT) and two mother wavelet filters on the returns series are considered in this paper, viz., the Haar and Daubechies (d4). The findings show that BitCoin/USD is riskier than ZAR/USD since it has a higher VaR per unit invested in each currency. At the 99% significance level, BitCoin/USD has average values of VaR of 2.71% and 4.98% for the WD-ARMA-GARCH-GPD and WD-ARMA-GARCH-GEVD models, respectively; and this is slightly higher than the respective 2.69% and 3.59% for the ZAR/USD. The average BitCoin/USD returns of 0.001990 are higher than ZAR/USD returns of −0.000125. These findings are consistent with the mean-variance portfolio theory, which suggests a higher yield for riskier assets. Based on the p-values of the Kupiec likelihood ratio test, the hybrid model adequacy is largely accepted, as p-values are greater than 0.05, except for the WD-ARMA-GARCH-GEVD models at a 99% significance level for both currencies. The findings are helpful to financial risk practitioners and forex traders in formulating their diversification and hedging strategies and ascertaining the risk-adjusted capital requirement to be set aside as a cushion in the event of the occurrence of an actual loss.