scholarly journals Volatility Modelling and VaR: The Case of Bitcoin, Ether and Ripple

2020 ◽  
Vol 11 (3) ◽  
pp. 253-269
Author(s):  
Jakub Ječmínek ◽  
Gabriela Kukalová ◽  
Lukáš Moravec
Keyword(s):  
Stochastic Process ◽  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Brownian Motion ◽  
Value At Risk ◽  
Risk Estimation ◽  
Geometric Brownian Motion ◽  
Empirical Literature ◽  
Digital Assets

Abstract Since Bitcoin introduction in 2008, the cryptocurrency market has grown into hundreds-of-billion-dollar market. The cryptocurrency market is well known as very volatile, mainly for the fact that the cryptocurrencies have not the price to fall back upon and that anybody can join the trading (no license or approval is required). Since empirical literature suggests that GARCH-type models dominate as VaR estimators the overall objective of this paper is to perform comprehensive volatility and VaR estimation for three major digital assets and conclude which method gives the best results in terms of risk management. The methods we used are parametric (GARCH and EWMA model), non-parametric (historical VaR) and Monte Carlo simulation (given by Geometric Brownian Motion). We conclude that the best method for value-at-risk estimation for cryptocurrencies is the Monte Carlo simulation due to the heavy diffusion (stochastic) process and robustness of the results.

scholarly journals Estimation of the Portfolio Risk from Conditional Value at Risk Using Monte Carlo Simulation

2021 ◽  
Vol 17 (3) ◽  
pp. 370-380
Author(s):  
Ervin Indarwati ◽  
Rosita Kusumawati
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Risk Estimation ◽  
Conditional Value At Risk ◽  
Portfolio Risk ◽  
Portfolio Returns

Portfolio risk shows the large deviations in portfolio returns from expected portfolio returns. Value at Risk (VaR) is one method for determining the maximum risk of loss of a portfolio or an asset based on a certain probability and time. There are three methods to estimate VaR, namely variance-covariance, historical, and Monte Carlo simulations. One disadvantage of VaR is that it is incoherent because it does not have sub-additive properties. Conditional Value at Risk (CVaR) is a coherent or related risk measure and has a sub-additive nature which indicates that the loss on the portfolio is smaller or equal to the amount of loss of each asset. CVaR can provide loss information above the maximum loss. Estimating portfolio risk from the CVaR value using Monte Carlo simulation and its application to PT. Bank Negara Indonesia (Persero) Tbk (BBNI.JK) and PT. Bank Tabungan Negara (Persero) Tbk (BBTN.JK) will be discussed in this study.  The  daily  closing  price  of  each  BBNI  and BBTN share from 6 January 2019 to 30 December 2019 is used to measure the CVaR of the two banks' stock portfolios with this Monte Carlo simulation. The steps taken are determining the return value of assets, testing the normality of return of assets, looking for risk measures of returning assets that form a normally distributed portfolio, simulate the return of assets with monte carlo, calculate portfolio weights, looking for returns portfolio, calculate the quartile of portfolio return as a VaR value, and calculate the average loss above the VaR value as a CVaR value. The results of portfolio risk estimation of the value of CVaR using Monte Carlo simulation on PT. Bank Negara Indonesia (Persero) Tbk and PT. Bank Tabungan Negara (Persero) Tbk at a confidence level of 90%, 95%, and 99% is 5.82%, 6.39%, and 7.1% with a standard error of 0.58%, 0.59%, and 0.59%. If the initial funds that will be invested in this portfolio are illustrated at Rp 100,000,000, it can be interpreted that the maximum possible risk that investors will receive in the future will not exceed Rp 5,820,000, Rp 6,390,000 and Rp 7,100,000 at the significant level 90%, 95%, and 99%

scholarly journals Value at Risk Dinâmico: um Estudo Comparativo entre os Modelos Heterocedásticos e a Simulação de Monte Carlo

2006 ◽  
Vol 4 (2) ◽  
pp. 181
Author(s):  
Marcos Roberto Gois de Oliveira ◽  
Charles Ulises de Montreuil Carmona ◽  
José Lamartine Távora Junior
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Dynamic Analysis ◽  
Value At Risk ◽  
Garch Models ◽  
Efficient Market

The objective of this paper was to analyze the risk management of a portfolio composed by Petrobras PN, Telemar PN and Vale do Rio Doce PNA stocks. It was verified if the modeling of Value-at-Risk (VaR) through the place Monte Carlo simulation with volatility of GARCH family is supported by hypothesis of efficient market. The results have shown that the statistic evaluation in inferior to dynamics, evidencing that the dynamic analysis supplies support to the hypothesis of efficient market of the Brazilian share holding market, in opposition of some empirical evidences. Also, it was verified that the GARCH models of volatility is enough to accommodate the variations of the shareholding Brazilian market, since the model is capable to accommodate the great dynamic of the Brazilian market.

scholarly journals APPLICATION OF MONTE CARLO SIMULATION METHODS IN RISK MANAGEMENT

2007 ◽  
Vol 8 (3) ◽  
pp. 165-168 ◽  
Author(s):  
Alexander Suhobokov
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Value At Risk ◽  
Simulation Method ◽  
Monte Carlo Algorithm ◽  
Simulation Methods ◽  
Monte Carlo Simulation Method

The paper deals with Monte Carlo simulation method and its application in Risk Management. The author with the help of MATLAB 7.0 introduces new modification of Monte Carlo algorithm aimed at fast and effective calculation of financial organization's Value at Risk (VaR) by the example of Parex Bank's FOREX exposure.

Monte Carlo simulation of Brownian motion with viscous drag

1978 ◽  
Vol 46 (5) ◽  
pp. 543-545 ◽  
Author(s):  
L. Gunther ◽  
D. L. Weaver
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Brownian Motion ◽  
Viscous Drag

scholarly journals On the existence of an optimal estimation window for risk measures

2015 ◽  
Vol 4 (1and2) ◽  
pp. 28
Author(s):  
Marcelo Brutti Righi ◽  
Paulo Sergio Ceretta
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Financial Risk ◽  
Risk Measures ◽  
Optimal Estimation ◽  
Expected Shortfall ◽  
Estimation Bias ◽  
Optimal Length

We investigate whether there can exist an optimal estimation window for financial risk measures. Accordingly, we propose a procedure that achieves optimal estimation window by minimizing estimation bias. Using results from a Monte Carlo simulation for Value at Risk and Expected Shortfall in distinct scenarios, we conclude that the optimal length for the estimation window is not random but has very clear patterns. Our findings can contribute to the literature, as studies have typically neglected the estimation window choice or relied on arbitrary choices.

On Using Monte Carlo Simulations for Project Risk Management

2016 ◽  
pp. 150-173
Author(s):  
Cristiana Tudor ◽  
Maria Tudor
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Decision Makers ◽  
Project Schedule ◽  
Project Risk ◽  
Monte Carlo Simulation Technique ◽  
Basic Probability ◽  
The Cost

This chapter covers the essentials of using the Monte Carlo Simulation technique (MSC) for project schedule and cost risk analysis. It offers a description of the steps involved in performing a Monte Carlo simulation and provides the basic probability and statistical concepts that MSC is based on. Further, a simple practical spreadsheet example goes through the steps presented before to show how MCS can be used in practice to assess the cost and duration risk of a project and ultimately to enable decision makers to improve the quality of their judgments.

scholarly journals Some Results on Measures of Interaction between Paired Risks

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 88 ◽  
Author(s):  
Rui Fang ◽  
Xiaohu Li
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Risk Measures ◽  
Dependence Structure ◽  
Marginal Distributions ◽  
Numerical Examples ◽  
Risk Contribution ◽  
Actuarial Risk

Co-risk measures and risk contribution measures have been introduced to evaluate the degree of interaction between paired risks in actuarial risk management. This paper attempts to study the ordering behavior of measures on interaction between paired risks. For various co-risk measures and risk contribution measures, we investigate how the marginal distributions and the dependence structure impact on the level of interaction between paired risks. Also, several numerical examples based on Monte Carlo simulation are presented to illustrate the main findings.

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