scholarly journals Value at Risk (VaR) Using Volatility Forecasting Models: EWMA, GARCH and Stochastic Volatility

2007 ◽  
Vol 4 (1) ◽  
pp. 74-94 ◽  
Author(s):  
Fernando Caio Galdi ◽  
Leonel Molero Pereira
Keyword(s):  
At Risk ◽  
Stochastic Volatility ◽  
Value At Risk ◽  
Volatility Forecasting ◽  
Forecasting Models

scholarly journals A stochastic implementation of the APCI model for mortality projections

2019 ◽  
Vol 24 ◽  
Author(s):  
S. J. Richards ◽  
I. D. Currie ◽  
T. Kleinow ◽  
G. P. Ritchie
Keyword(s):  
At Risk ◽  
Stochastic Model ◽  
Value At Risk ◽  
Capital Requirements ◽  
Mortality Forecasting ◽  
Forecasting Models ◽  
Mortality Projections ◽  
Model Fits

AbstractThe Age-Period-Cohort-Improvement (APCI) model is a new addition to the canon of mortality forecasting models. It was introduced by Continuous Mortality Investigation as a means of parameterising a deterministic targeting model for forecasting, but this paper shows how it can be implemented as a fully stochastic model. We demonstrate a number of interesting features about the APCI model, including which parameters to smooth and how much better the model fits to the data compared to some other, related models. However, this better fit also sometimes results in higher value-at-risk (VaR)-style capital requirements for insurers, and we explore why this is by looking at the density of the VaR simulations.

scholarly journals Volatility forecasting and value-at-risk estimation in emerging markets: the case of the stock market index portfolio in South Africa

2011 ◽  
Vol 12 (4) ◽  
pp. 401-411 ◽  
Author(s):  
Lumengo Bonga-Bonga ◽  
George Mutema
Keyword(s):  
At Risk ◽  
Stock Market ◽  
Emerging Markets ◽  
Value At Risk ◽  
Risk Estimation ◽  
Volatility Forecasting ◽  
Stock Market Index ◽  
Market Index ◽  
Auto Regressive ◽  
Asymmetric Behaviour

Accurate modelling of volatility is important as it relates to the forecasting of Value-at-Risk (VaR). The RiskMetrics model to forecast volatility is the benchmark in the financial sector. In an important regulatory innovation, the Basel Committee has proposed the use of an internal method for modelling VaR instead of the strict use of the benchmark model. The aim of this paper is to evaluate the performance of RiskMetrics in comparison to other models of volatility forecasting, such as some family classes of the Generalised Auto Regressive Conditional Heteroscedasticity models, in forecasting the VaR in emerging markets. This paper makes use of the stock market index portfolio, the All-Share Index, as a case study to evaluate the market risk in emerging markets. The paper underlines the importance of asymmetric behaviour for VaR forecasting in emerging markets’ economies.

VALUE-AT-RISK COMPUTATIONS IN STOCHASTIC VOLATILITY MODELS USING SECOND-ORDER WEAK APPROXIMATION SCHEMES

2014 ◽  
Vol 17 (01) ◽  
pp. 1450004
Author(s):  
EVA LÜTKEBOHMERT ◽  
LYDIENNE MATCHIE
Keyword(s):  
At Risk ◽  
Stochastic Volatility ◽  
Value At Risk ◽  
Second Order ◽  
Stochastic Volatility Model ◽  
Approximation Schemes ◽  
Weak Approximation ◽  
Volatility Models ◽  
Volatility Model ◽  
Model Correlation

We explore the class of second-order weak approximation schemes (cubature methods) for the numerical simulation of joint default probabilities in credit portfolios where the firm's asset value processes are assumed to follow the multivariate Heston stochastic volatility model. Correlation between firms' asset processes is reflected by the dependence on a common set of underlying risk factors. In particular, we consider the Ninomiya–Victoir algorithm and we study the application of this method for the computation of value-at-risk and expected shortfall. Numerical simulations for these quantities for some exogenous portfolios demonstrate the numerical efficiency of the method.

scholarly journals ESTIMASI VALUE AT RISK MENGGUNAKAN VOLATILITAS DISPLACED DIFFUSION

2019 ◽  
Vol 8 (4) ◽  
pp. 298
Author(s):  
MIRANDA NOVI MARA DEWI ◽  
KOMANG DHARMAWAN ◽  
KARTIKA SARI
Keyword(s):  
At Risk ◽  
Stochastic Process ◽  
Stochastic Volatility ◽  
Diffusion Model ◽  
Stock Prices ◽  
Value At Risk ◽  
Implied Volatility ◽  
Stochastic Volatility Model ◽  
Financial Asset ◽  
Volatility Model

Value at Risk (VaR) is a measure of risk that is able to calculate the worst possible loss that can occurs to stock prices with a certain level of confidence and within a certain period of time. The purpose of this study was to determine the VaR estimate from PT. Indonesian Telecommunications by using Displaced Diffusion volatility. The Displaced Diffusion Model is a stochastic volatility model that describes changes in a financial asset assuming volatility is not constant, but follows a stochastic process. Displaced Diffusion model are capable of modelling skewed implied volatility structures and frequently applied by interest rate quants. Based on the estimation of Displaced Diffusion volatility, it is found that volatility for PT. Indonesian Telecommunications is 0.010168 and VaR estimation using Displaced Diffusion volatility with a confidence level of  95 percent of 1.63%.

VaR/CVaR ESTIMATION UNDER STOCHASTIC VOLATILITY MODELS

2014 ◽  
Vol 17 (02) ◽  
pp. 1450009 ◽  
Author(s):  
CHUAN-HSIANG HAN ◽  
WEI-HAN LIU ◽  
TZU-YING CHEN
Keyword(s):  
At Risk ◽  
Stochastic Volatility ◽  
Value At Risk ◽  
Extreme Event ◽  
Stochastic Volatility Model ◽  
Conditional Value At Risk ◽  
Transform Method ◽  
Historical Simulation ◽  
Volatility Models ◽  
Volatility Model

This paper proposes an improved procedure for stochastic volatility model estimation with an application to Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) estimation. This improved procedure is composed of the following instrumental components: Fourier transform method for volatility estimation, and importance sampling for extreme event probability estimation. The empirical analysis is based on several foreign exchange series and the S&P 500 index data. In comparison with empirical results by RiskMetrics, historical simulation, and the GARCH(1,1) model, our improved procedure outperforms on average.

scholarly journals PERHITUNGAN VALUE AT RISK DENGAN PENDUGA VOLATILITAS STOKASTIK HESTON

2018 ◽  
Vol 7 (4) ◽  
pp. 317
Author(s):  
DESAK PUTU DEVI DAMIYANTI ◽  
KOMANG DHARMAWAN ◽  
LUH PUTU IDA HARINI
Keyword(s):  
At Risk ◽  
Stochastic Process ◽  
Stochastic Volatility ◽  
Value At Risk ◽  
Financial Risk ◽  
Stochastic Volatility Model ◽  
Heston Model ◽  
Volatility Model ◽  
The Mean ◽  
Simulation Results

Value at risk is a method that measures financial risk of an security or portfolio. The aims of the research is to find out the value at risk of an exchange rate using the Heston stochastic volatility model. Heston model is a strochastic volatility model that assumes that volatility of the security follow stochastic process and consider the mean reversion. Based on simulation results, the value of volatility using Heston volatility estimastor is 0.2887, and the value of Heston VaR with 95 percent confident level is 0.0297. Based on result of backtesting,  there are 48 violations obtained VaR using Heston model, while historical VaR there are 2 violations. Thus, VaR using Heston model is more strict in estimating risk.

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