arXiv subjects

Stavros Stavroyiannis

Publications and source records attributed to Stavros Stavroyiannis.

4 recordsLinked to original sources

arXiv2022

Cointegration and ARDL specification between the Dubai crude oil and the US natural gas market

This paper examines the relationship between the price of the Dubai crude oil and the price of the US natural gas using an updated monthly dataset from 1992 to 2018, incorporating the latter events in the energy markets. After employing a variety of unit root and cointegration tests, the long-run relationship is examined via the autoregressive distributed lag (ARDL) cointegration technique, along with the Toda-Yamamoto (1995) causality test. Our results indicate that there is a long-run relationship with a unidirectional causality running from the Dubai crude oil market to the US natural gas market. A variety of post specification tests indicate that the selected ARDL model is well-specified, and the results of the Toda-Yamamoto approach via impulse response functions, forecast error variance decompositions, and historical decompositions with generalized weights, show that the Dubai crude oil price retains a positive relationship and affects the US natural gas price.

econ.EM↗

arXiv2017

Value-at-Risk and Expected Shortfall for the major digital currencies

Digital currencies and cryptocurrencies have hesitantly started to penetrate the investors, and the next step will be the regulatory risk management framework. We examine the Value-at-Risk and Expected Shortfall properties for the major digital currencies, Bitcoin, Ethereum, Litecoin, and Ripple. The methodology used is GARCH modelling followed by Filtered Historical Simulation. We find that digital currencies are subject to a higher risk, therefore, to higher sufficient buffer and risk capital to cover potential losses.

q-fin.RM↗

arXiv2017

A note on the Nelson Cao inequality constraints in the GJR-GARCH model: Is there a leverage effect?

The majority of stylized facts of financial time series and several Value-at-Risk measures are modeled via univariate or multivariate GARCH processes. It is not rare that advanced GARCH models fail to converge for computational reasons, and a usual parsimonious approach is the GJR-GARCH model. There is a disagreement in the literature and the specialized econometric software, on which constraints should be used for the parameters, introducing indirectly the distinction between asymmetry and leverage. We show that the approach used by various software packages is not consistent with the Nelson-Cao inequality constraints. Implementing Monte Carlo simulations, despite of the results being empirically correct, the estimated parameters are not theoretically coherent with the Nelson-Cao constraints for ensuring positivity of conditional variances. On the other hand ruling out the leverage hypothesis, the asymmetry term in the GJR model can take negative values when typical constraints like the condition for the existence of the second and fourth moments, are imposed.

q-fin.ST↗

arXiv2016

Value-at-Risk and backtesting with the APARCH model and the standardized Pearson type IV distribution

We examine the efficiency of the Asymmetric Power ARCH (APARCH) model in the case where the residuals follow the standardized Pearson type IV distribution. The model is tested with a variety of loss functions and the efficiency is examined via application of several statistical tests and risk measures. The results indicate that the APARCH model with the standardized Pearson type IV distribution is accurate, within the general financial risk modeling perspective, providing the financial analyst with an additional skewed distribution for incorporation in the risk management tools.

q-fin.RM↗