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Publications
Publications (18)
In this paper the generalized autoregressive conditional heteroscedastic models are applied in modeling exchange rate volatility of the USD/KES exchange rate using daily observations over the period starting 3 rd January 2003 to 31 st December 2015. The paper applies both symmetric and asymmetric models that capture most of the stylized facts about...
This paper presents a comparative evaluation of the predictive performance of conventional univariate
VaR models including unconditional normal distribution model, exponentially weighted moving average
(EWMA/RiskMetrics), Historical Simulation, Filtered Historical Simulation, GARCH-normal and GARCH
Students t models in terms of their forecasting ac...
This paper implements different approaches used to compute the one-day
Value-at-Risk (VaR) forecast for a portfolio of four currency exchange rates.
The concepts and techniques of the conventional methods considered in the
study are first reviewed. These approaches have shortcomings and therefore
fail to capture the stylized characteristics of fina...
Modelling high-dimensional dependence structures for financial assets in a portfolio framework require flexible dependence models. In this paper, a regular vine-copula based model is employed to analyze financial dependencies and co-movements of a six-dimensional portfolio of currency exchange rates starting from January 2001 to April 2018. The reg...
In Kenya, social media platforms are the primary medium for cyberhate, and it predominantly affects university students who have extensive social media usage. Although cyberhate is considered as a criminal offence in Kenya, victims, particularly young people, often do not report victimisation to the police. Despite the well-documented harmful effec...
The Black–Scholes–Merton option pricing model is a classical approach that assumes that the underlying asset prices follow a normal distribution with constant volatility. However, this assumption is often violated in real-world financial markets, resulting in mispricing and inaccurate hedging strategies for options. Such discrepancies may result in...
This paper implements the analysis of volatility behaviour of the eight major cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, Monero, Stellar, Dash and Tether) for the period starting from October 13th 2015 to November 18th 2019. The GARCH-type models with heavy-tailed distributions are fitted to filter the conditional volatility exhibited b...
The recent global coronavirus (COVID-19) pandemic has had an enormous economic impact on the financial markets across the world. It has created an unprecedented level of risk uncertainty, prompting investors to impetuously dispose of their assets leading to significant losses over a very short period. In this paper, the conditional heteroscedastic...
The recent global pandemic of contagious coronavirus (COVID-19) has had an enormous impact on the financial markets across the world. It has created an unprecedented level of risk uncertainty, prompting investors to impetuously dispose of their assets leading to significant losses over a very short period. In this paper, the conditional heterosceda...
Cryptocurrencies have become increasingly popular in recent years attracting wide coverage from the media and drawing the attention of the academia, investors, speculators, regulators, and governments worldwide. This paper focuses on modelling volatility dynamics for cryptocurrencies as digital investment assets using generalized autoregressive con...
Cryptocurrencies have become increasingly popular in recent years attracting the attention of the media, academia, investors, speculators, regulators, and governments worldwide. This paper focuses on modelling the volatility dynamics of eight most popular cryptocurrencies in terms of their market capitalization for the period starting from 7th Augu...
Modelling sophisticated high-dimensional dependence structures for financial assets in a portfolio framework require flexible dependence models. However, existing high-dimensional dependence models are rather restrictive on the dependence structure. These restrictions compromise the realization of sophisticated dependence characteristics such as ta...
This paper implements the statistical modelling of the dependence structure of currency exchange rates using the concept of copulas. The GARCH-EVT-Copula model is applied to estimate the portfolio Value-at-Risk (VaR) of currency exchange rates. First the univariate ARMA-GARCH model is used to filter the return series. The generalized Pareto distrib...
Modelling Portfolio Currency Exchange Risk
using GARCH-EVT-Copula based model
Claims experience in non-life insurance is contingent on random eventualities
of claim frequency and claim severity. By design, a single policy may possibly
incur more than one claim such that the total number of claims as well as the
total size of claims due on any given portfolio is unpredictable. For insurers to
be able to settle claims that may...
Questions
Questions (4)
Kindly assist the R code for estimating constant and time-varying copula functions for bivariate time-series (e.g. Normal, Clayton, Rotates Clayton, Plackett, Frank, Gumbel, Rotated Gumbel, Student, Symmetrised Joe-Clayton). These copulas are then compared by relying on criteria such as Log-likelihood, AIC or BIC.
Especially the Symmetrised Joe-Clayton in R???
I have Forex returns series and I have been trying to fit a Dynamic EVT model using R. I have got results but they aren't what I expected. I suspect that the problem is the R-code I have written. Anybody who can assist with the R-code to implement the GARCH-EVT model???
Hey,
I would like to complete the estimation of out-of Sample VaR using GARCH-EVT model. I have done the following;
1. Fitted a AR-GARCH model to the return series; using the first 1250 observations.
2. extracted the standardized residuals,
3. fitted the GPD to the residuals and obtained the parameter estimates.
My question is; How do I implement the estimation of the dynamic VaR from this step and test the VaR violations against the actual returns.....
The R code to implement this plz.
. I have been able to do the following steps in R;
1. Fit GARCH models to each series.
2. Extract standardized returns.
3. Transform standardized returns to uniform marginals using the parametric IFM method by Joe.
4. Fit the copulas and estimate the parameters
5. Generate 100 1-day ahead forecasts from the copulas.
6. Reverse transform the simulated values.
7. Use these transformed forecasts in ugarchsim (using custom.dist)
8. Extract forecasted mu and sigma.
9. Calculate 95% and 99% VaR equally weighted portfolio of 5 assets with weights 1/5
Any assistance on the step 7, 8 and 9 using the rugarch package or any other package in R will be highly appreciated
