Modelling sophisticated 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... more
Modelling sophisticated 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 regular-vine copula based model employs partial correlations to construct the regular vine structure and offer superior flexibility in the selection of the distributions to model financial dependence structure. The model also captures the asymmetry between multivariate variables using bivariate copulas with flexible tail dependence. Empirical evidence suggests that co-movements in currency markets are most likely to experience a crash and boom together thus, concluding that currency markets are integrated due to the nature of the global financial systems. The C-Vine copula specification is favoured over the other copula specifications in modeling the dependence dynamics between currency exchange rates.Mathematics Subject Classification: 62H20, 62H12Keywords: Copula; regular vines; C-Vine, D-Vine; currency exchange rates; tail dependence; pair-copula constructions.
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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... more
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 accuracy. The paper empirically determines the extent to which the aforementioned methods are reliable in estimating one-day ahead Value at Risk (VaR). The analysis is based on daily closing prices of the USD/KES exchange rates over the period starting January 03, 2003 to December 31, 2016. In order to assess the performance of the models, the rolling window of approximately four years (n=1000 days) is used for backtesting purposes. The backtesting analysis covers the sub-period from November 2008 to December 2016, consequently including the most volatile periods of the Kenyan shilling and the historical all-time high in September 2015. The ...
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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... more
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 accuracy. The paper empirically determines the extent to which the aforementioned methods are reliable in estimating one-day ahead Value at Risk (VaR). The analysis is based on daily closing prices of the USD/KES exchange rates over the period starting January 03, 2003 to December 31, 2016. In order to assess the performance of the models, the rolling window of approximately four years (n=1000 days) is used for backtesting purposes. The backtesting analysis covers the sub-period from November 2008 to December 2016, consequently including the most volatile periods of the Kenyan shilling and the historical all-time high in September 2015. The empirical results demonstrate that GJR-GARCH-t approach and Filtered Historical Simulation method with GARCH volatility specification perform competitively accurate in estimating VaR forecasts for both standard and more extreme quantiles thereby generally out-performing all the other models under consideration.
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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... more
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 exchange rate returns such as volatility clustering and leverage effect. The performance of the symmetric GARCH (1, 1) and GARCH-M models as well as the asymmetric EGARCH (1, 1), GJR-GARCH (1, 1) and APARCH (1, 1) models with different residual distributions are applied to data. The most adequate models for estimating volatility of the exchange rates are the asymmetric APARCH model, GJR-GARCH model and EGARCH model with Student's t-distribution.