Ivana Tomas Žiković

Evaluating the Performance of VaR Models in Energy Markets

We analyze the relative performance of 13 VaR models using daily returns of WTI, Brent, natural gas and heating oil one-month futures contracts. After obtaining VaR estimates we evaluate the statistical significance of the differences in performance of the analyzed VaR models. We employ the simulation-based methodology proposed by Žikovic and Filer in Czech J Econ Finan 63(4):327–359, 2013, which allows us to rank competing VaR models. Somewhat surprisingly, the obtained results indicate that for a large number of different VaR models there is no statistical difference in their performance, as measured by the Lopez size adjusted score. However, filtered historical simulation (FHS) and the BRW model stand out as robust and consistent approaches that – in most cases – significantly outperform the remaining VaR models.

Two Sides of the Same Coin: Risk Measures in the Energy Markets

This study investigates whether there exist common model features that yield consistently superior results under both value-at-risk (VaR) and expected shortfall (ES) risk metrics in the energy commodities markets. We analyze the performance of ten VaR and seven ES models on the daily spot prices of West Texas Intermediate, Brent, natural gas, heating oil, US low sulphur coal and uranium yellow cake. Our back testing results show that advanced semi-parametric VaR and ES models, which entail sophisticated modeling of conditional volatility and extreme tails, are required to capture the true level of risk in the energy markets. We find consistency in the performance of tested risk models under both VaR and ES measures. In our tested sample, the filtered historical simulation and mirrored historical simulation models rank among the best VaR and ES models. A common feature of both models is that they do not make a priori para-metrical assumptions about the return distribution but use empirical historical returns.

Challenges in Predicting Financial Distress in Emerging Economies : The Case of Croatia

Using an extensive dataset of over 44,000 firms for a period that includes an economic upswing and a recession, this article examines how time-varying, firm-specific variables and changes in the macroeconomic environment affect the probability of firms’ financial distress. Traditional single-period approaches used to predict financial distress are based on unrealistically restrictive assumptions and cannot dynamically account for changes in financial indicators and macroeconomic conditions. Therefore discrete-time hazard models (using logit and cloglog) are applied; these indicate that the probability of distress is strongly influenced by both firm-specific and macroeconomic variables. Although firm-specific variables play an essential role, the results show that macroeconomic variables are important for understanding the fluctuations in the probability of distress over time. Furthermore, the findings provide evidence that both the legal criteria and a firm’s financial health should be considered when identifying firms in emerging economies that are in distress because existing bankruptcy laws are not applied.Using an extensive dataset of over 44,000 firms for a period that includes an economic upswing and a recession, this article examines how time-varying, firm-specific variables and changes in the macroeconomic environment affect the probability of firms’ financial distress. Traditional single-period approaches used to predict financial distress are based on unrealistically restrictive assumptions and cannot dynamically account for changes in financial indicators and macroeconomic conditions. Therefore discrete-time hazard models (using logit and cloglog) are applied; these indicate that the probability of distress is strongly influenced by both firm-specific and macroeconomic variables. Although firm-specific variables play an essential role, the results show that macroeconomic variables are important for understanding the fluctuations in the probability of distress over time. Furthermore, the findings provide evidence that both the legal criteria and a firm’s financial health should be considered when identifying firms in emerging economies that are in distress because existing bankruptcy laws are not applied.

Modelling the Impact of Macroeconomic Variables on Aggregate Corporate Insolvency: Case of Croatia

Abstract The majority of research papers dealing with corporate failure and insolvency in transition countries use a combination of financial ratios in investigating corporate failures, i.e., the microeconomic approach. By relying solely on the microeconomic approach, it is not possible to completely capture the complexity of business operations. In recent years, there has been a growing interest in exploring the predictive power of macroeconomic variables in forecasting insolvencies. As the macroeconomic approach has been applied mainly in the analysis of developed economies, this article investigates the influence of macroeconomic variables on aggregate corporate insolvency in Croatia, using the vector error-correction model (VECM) for the period 2000–2011. The results have shown a long-run dynamic connection between the corporate insolvency rate and the rate of unemployment while corporate credits, long-term interest rates and industrial production have a short-term effect on the corporate insolvency rate.The majority of research papers dealing with corporate failure and insolvency in transition countries use a combination of financial ratios in investi