J. Risk Financial Manag.2016, 9(1), 2; doi:10.3390/jrfm9010002 - published 29 February 2016 Show/Hide Abstract
Abstract: VaR (Value at Risk) and CVaR (Conditional Value at Risk) are implied by option prices. Their relationships to option prices are derived initially under the pricing measure. It does not require assumptions about the distribution of portfolio returns. The effects of changes of measure are modest at the short horizons typically used in applications. The computation of CVaR from option price is very convenient, because this measure is not elicitable, making direct comparisons of statistical inferences from market data problematic.
J. Risk Financial Manag.2016, 9(1), 1; doi:10.3390/jrfm9010001 - published 31 December 2015 Show/Hide Abstract
Abstract: The impact of a stress scenario of default events on the loss distribution of a credit portfolio can be assessed by determining the loss distribution conditional on these events. While it is conceptually easy to estimate loss distributions conditional on default events by means of Monte Carlo simulation, it becomes impractical for two or more simultaneous defaults as then the conditioning event is extremely rare. We provide an analytical approach to the calculation of the conditional loss distribution for the CreditRisk + portfolio model with independent random loss given default distributions. The analytical solution for this case can be used to check the accuracy of an approximation to the conditional loss distribution whereby the unconditional model is run with stressed input probabilities of default (PDs). It turns out that this approximation is unbiased. Numerical examples, however, suggest that the approximation may be seriously inaccurate but that the inaccuracy leads to overestimation of tail losses and, hence, the approach errs on the conservative side.
J. Risk Financial Manag.2015, 8(4), 369-374; doi:10.3390/jrfm8040369 - published 22 December 2015 Show/Hide Abstract
Abstract: The purpose of the paper is to present the fundamental equation in tourism finance that connects tourism research to empirical finance and financial econometrics. The energy industry, which includes, oil, gas and bio-energy fuels, together with the tourism industry, are two of the most important industries in the world today in terms of employment and generating income. The primary purpose in attracting domestic and international tourists to a country, region or city is to maximize tourism expenditure. The paper will concentrate on daily tourism expenditure, regardless of whether such data might be readily available. If such data are not available, a practical method is presented to calculate the appropriate data.
J. Risk Financial Manag.2015, 8(4), 355-368; doi:10.3390/jrfm8040355 - published 29 September 2015 Show/Hide Abstract
Abstract: We study a discrete-time interaction risk model with delayed claims within the framework of the compound binomial model. Using the technique of generating functions, we derive both a recursive formula and a defective renewal equation for the expected discounted penalty function. As applications, the probabilities of ruin and the joint distributions of the surplus one period to ruin and the deficit at ruin are investigated. Numerical illustrations are also given.
J. Risk Financial Manag.2015, 8(3), 337-354; doi:10.3390/jrfm8030337 - published 24 August 2015 Show/Hide Abstract
Abstract: In this study, we try to examine whether the forecast errors obtained by the ANN models affect the breakout of financial crises. Additionally, we try to investigate how much the asymmetric information and forecast errors are reflected on the output values. In our study, we used the exchange rate of USD/TRY (USD), the Borsa Istanbul 100 Index (BIST), and gold price (GP) as our output variables of our Artificial Neural Network (ANN) models. We observe that the predicted ANN model has a strong explanation capability for the 2001 and 2008 crises. Our calculations of some symmetry measures such as mean absolute percentage error (MAPE), symmetric mean absolute percentage error (sMAPE), and Shannon entropy (SE), clearly demonstrate the degree of asymmetric information and the deterioration of the financial system prior to, during, and after the financial crisis. We found that the asymmetric information prior to crisis is larger as compared to other periods. This situation can be interpreted as early warning signals before the potential crises. This evidence seems to favor an asymmetric information view of financial crises.
J. Risk Financial Manag.2015, 8(3), 311-336; doi:10.3390/jrfm8030311 - published 5 August 2015 Show/Hide Abstract
Abstract: We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of threshold-autoregressive models, where changes in regimes are governed by past returns. The ability to capture changes in volatility regimes and using more accurate volatility measures allow outperforming other benchmark models, such as linear heterogeneous autoregressive model and GARCH specifications. Finally, we show how to derive closed-form expression for multiple-step-ahead forecasting by exploiting information about the conditional distribution of returns.