Modelling the benchmark spot curve for the Serbian

The objective of this paper is to estimate Serbian benchmark spot curves using the Svensson parametric model. The main challenges that we tackle are: sparse data, different currency denominations of short and longer term maturities, and infrequent transactions in the short-term market segment vs daily traded medium and long-term market segment. We find that the model is flexible enough to account for most of the data variability. The model parameters are interpreted in economic terms

Exchange-traded funds of the eurozone sovereign debt

Periods of high uncertainty bring liquidity concerns to the forefront for sovereign bond investors. Arguably the most liquid and cost-effective way for retail and small institutional investors to gain diversified sovereign bond exposure is through an exchange traded fund (ETF). In this paper we study the performance, country exposure, and replicating characteristics of a sample of 31 European index ETFs with exposure to eurozone sovereign debt. The obtained results are presented in the context of underlying index selection rules, types of replication, and movements in sovereign debt interest rates and sovereign CDS spreads. It is demonstrated that the ETFs focused on accurately track corresponding bond indices. This is consistent with earlier findings for equity index ETFs. Our results may be of interest for institutional investors, regulators, and everyone interested in sovereign debt investments

Mean univariate- GARCH VaR portfolio optimization: actual portfolio approach

In accordance with Basel Capital Accords, the Capital Requirements (CR) for market risk exposure of banks is a nonlinear function of Value-at-Risk (VaR). Importantly, the CR is calculated based on a bank’s actual portfolio, i.e. the portfolio represented by its current holdings. To tackle mean-VaR portfolio optimization within the actual portfolio framework (APF), we propose a novel mean-VaR optimization method where VaR is estimated using a univariate Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) volatility model. The optimization was performed by employing a Nondominated Sorting Genetic Algorithm (NSGA-II). On a sample of 40 large US stocks, our procedure provided superior mean-VaR trade-offs compared to those obtained from applying more customary mean-multivariate GARCH and historical VaR models. The results hold true in both low and high volatility samples

Market risk management in a post-Basel II regulatory environment

We propose a novel method of Mean-Capital Requirement portfolio optimization. The optimization is performed using a parallel framework for optimization based on the Nondominated Sorting Genetic Algorithm II. Capital requirements for market risk include an additional stress component introduced by the recent Basel 2.5 regulation. Our optimization with the Basel 2.5 formula in the objective function produces superior results to those of the old (Basel II) formula in stress scenarios in which the correlations of asset returns change considerably. These improvements are achieved at the expense of reduced cardinality of Pareto-optimal portfolios. This reduced cardinality (and thus portfolio diversification) in periods of relatively low market volatility may have unintended consequences for banks’ risk exposure

Scientific Papers

ABSTRACT: The objective of this paper is to estimate Serbian benchmark spot curves using the Svensson parametric model. The main challenges that we tackle are: sparse data, different currency denominations of short and longer term maturities, and infrequent transactions in the short-term market segment vs daily traded medium and long-term market segment. We find that the model is flexible enough to account for most of the data variability. The model parameters are interpreted in economic terms. KEY WORDS: fixed-income market, benchmark spot curves, yield curv

European bond ETFs: tracking errors and the sovereign debt crisis

This study examines the tracking performance of 31 eurozone sovereign debt exchange traded index funds (ETFs) during 2007-2010. The tracking performance is assessed by four different tracking error models. Overall, funds underperform their respective benchmarks. Active returns (net of fees) vary substantially (from +46.74 to -30.36 basis points) and are of considerable economic interest. The significant differences in the performance of swap-based and in-kind funds highlight the importance of appropriate (e.g. correlation vs. cointegration based) metrics required for the assessment of funds adopting different replication methods. We also document important changes in the tracking performance due to the changing characteristics of EU sovereign bonds since the start of the sovereign debt crisis

Mean-maximum drawdown optimization of buy-and-hold portfolios using a multi-objective evolutionary algorithm

We develop a novel Mean-Max Drawdown portfolio optimization approach using buy-and-hold portfolios. The optimization is performed utilizing a multi-objective evolutionary algorithm on a sample of S&P 100 constituents. Our optimization procedure provides portfolios with better Mean-Max Drawdown trade-offs compared to relevant benchmarks, regardless of the selected subsamples and market conditions. The superior performance of our approach is particularly pronounced in periods with reversing market trends (i.e. a market rally and a fall in the same subsample)