Timotheos Angelidis

Measuring the Market Risk of Freight Rates: A Value-at-Risk Approach

The fluctuation of shipping freight rates (freight rate risk) is an important source of market risk for all participants in the freight markets including hedge funds, commodity and energy producers. We measure the freight rate risk by the Value-at-Risk (VaR) approach. A range of parametric and non-parametric VaR methods is applied to various popular freight markets for dry and wet cargoes. Backtesting is conducted in two stages by means of statistical tests and a subjective loss function that uses the Expected Shortfall, respectively. We find that the simplest non-parametric methods should be used to measure freight rate risk. In addition, freight rate risk is greater in the wet cargoes markets. The margins in the growing freight derivatives markets should be set accordingly.

Revisiting Mutual Fund Performance Evaluation

Mutual fund manager excess performance should be measured relative to their self-reported benchmark rather than the return of a passive portfolio with the same risk characteristics. Ignoring the self-reported benchmark results in different measurement of stock selection and timing components of excess performance. We revisit baseline empirical evidence fund performance evaluation utilizing stock selection and timing measures that incorporate the self-reported benchmark. We introduce a new factor exposure based approach for measuring the – static and dynamic – timing capabilities of mutual fund managers. We overall conclude that current studies are likely to be misstating skill because they ignore the managers’ self-reported benchmark in the performance evaluation process.

Modeling risk for long and short trading positions

Purpose – Aims to investigate the accuracy of parametric, nonparametric, and semiparametric methods in predicting the one-day-ahead value-at-risk (VaR) measure in three types of markets (stock exchanges, commodities, and exchange rates), both for long and short trading positions. Design/methodology/approach – The risk management techniques are designed to capture the main characteristics of asset returns, such as leptokurtosis and asymmetric distribution, volatility clustering, asymmetric relationship between stock returns and conditional variance, and power transformation of conditional variance. Findings – Based on back-testing measures and a loss function evaluation method, finds that the modeling of the main characteristics of asset returns produces the most accurate VaR forecasts. Especially for the high confidence levels, a risk manager must employ different volatility techniques in order to forecast accurately the VaR for the two trading positions. Practical implications – Different models achieve accurate VaR forecasts for long and short trading positions, indicating to portfolio managers the significance of modeling separately the left and the right side of the distribution of returns. Originality/value – The behavior of the risk management techniques is examined for both long and short VaR trading positions; to the best of ones knowledge, this is the first study that investigates the risk characteristics of three different financial markets simultaneously. Moreover, a two-stage model selection is implemented in contrast with the most commonly used back-testing procedures to identify a unique model. Finally, parametric, nonparametric, and semiparametric techniques are employed to investigate their performance in a unified environment.

Idiosyncratic Risk in Emerging Markets

In this study, I examine the properties and portfolio management implications of value-weighted idiosyncratic volatility in 24 emerging markets. This paper provides evidence against the view that the rise of idiosyncratic risk is a global phenomenon. Furthermore, specific and market risks jointly predict market returns as there is a negative (positive) relation between idiosyncratic (market) risk and subsequent stock returns. Idiosyncratic volatility is the most important component of tracking error volatility, and it does not exhibit either an upward or a downward trend. Thus, investors do not have to increase, on average, the number of stocks they hold to keep the active risk constant.

Backtesting VaR models:a two-stage procedure

Academics and practitioners have extensively studied Value-at-Risk (VaR) to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions. However, they have not succeeded yet as the developed testing frameworks have not been widely accepted. A two-stage backtesting procedure is proposed in order a model that not only forecasts VaR but also predicts the loss beyond VaR to be selected. Numerous conditional volatility models that capture the main characteristics of asset returns (asymmetric and leptokurtic unconditional distribution of returns, power transformation and fractional integration of the conditional variance) under four distributional assumptions (normal, GED, Student-t, and skewed Student-t) have been estimated to find the best model for three financial markets (US stock, gold and dollar-pound exchange rate markets), long and short trading positions, and two confidence levels. By following this procedure, the risk manager can significantly reduce the number of competing models.

Does idiosyncratic risk matter? Evidence from European stock markets

This article examines if idiosyncratic risk can forecast stock returns for 10 European markets. We found little evidence to suggest that idiosyncratic volatility, equally or value weighted, can predict future stock market returns. However, we found that idiosyncratic risk measured as the equally weighted average variance of all stocks can significantly predict future size and value premia.

Value-at-Risk for Greek Stocks

This paper analyses the application of several volatility models to forecast daily Value-at-Risk (VaR) both for single assets and portfolios. We calculate the VaR number for 4 Greek stocks, 2 portfolios based on these securities and for the Athens Stock Exchange General Index. We model VaR for long and short trading positions by employing non-parametric methods, such as historical and filtered historical simulation, as well as parametric ones. Especially for the later techniques we use a collection of ARCH models (GARCH, EGARCH and TARCH) based on three distributional assumptions (Normal, Student-T and Skewed Student-T), while we combine the Extreme Value Theory with a volatility updating technique (via GARCH type-modeling). In order to choose one model among the various forecasting methods, we employ a two-stage backtesting procedure. In the first one, we implement two backtesting criteria (unconditional and conditional coverage) to test the statistical accuracy of the models.

Idiosyncratic risk, returns and liquidity in the London Stock Exchange: A spillover approach

In the light of recent evidence that liquidity and idiosyncratic risk may be priced factors in the cross section of expected stock returns and that market capitalization significantly affects investor behavior and liquidity, we explore the interactions between liquidity, idiosyncratic risk and return across time as well as across size-based portfolios of stocks listed in the London Stock Exchange. In a Vector Autoregressive (VAR) analytical framework, we find that volatility spills over from large cap stocks to small cap stocks and vice versa. Volatility shocks can be predicted by illiquidity shocks in both large cap as well as in the small cap portfolios. Illiquidity can be predicted by return shocks in small cap stocks. Finally, we document some evidence of asymmetric liquidity spillovers, from large cap stocks to small cap ones, supporting the intuition that common information is first incorporated in the trading behavior of large-cap investors and the liquidity of large cap stocks and is then transmitted in the trading of small stocks.

Forecasting one‐day‐ahead VaR and intra‐day realized volatility in the Athens Stock Exchange Market

Purpose - The aim is to evaluate the performance of symmetric and asymmetric ARCH models in forecasting both the one-day-ahead Value-at-Risk (VaR) and the realized intra-day volatility of two equity indices in the Athens Stock Exchange. Design/methodology/approach - Two volatility specifications are estimated, the symmetric generalized autoregressive conditional heteroscedasticity (GARCH) and the asymmetric APARCH processes. The data set consisted of daily closing prices of the General and the Bank indices from 25 April 1994 to 19 December 2003 and their intra day quotation data from 8 May 2002 to 19 December 2003. Findings - Under the VaR framework, the most appropriate method for the Bank index is the symmetric model with normally distributed innovations, while the asymmetric model with asymmetric conditional distribution applies for the General index. On the other hand, the asymmetric model tracks closer the one-step-ahead intra-day realized volatility with conditional normally distributed innovations for the Bank index but with asymmetric and leptokurtic distributed innovations for the General index. Originality/value - As concerns the Greek stock market, there are adequate methods in predicting market risk but it does not seem to be a specific model that is the most accurate for all the forecasting tasks.

Persistence of Shocks and the Reallocation of Labor

This paper proposes a theoretical and quantitative analysis of the reallocation of labor across firms in response to idiosyncratic shocks of different persistence. Creating and destroying jobs is costly and workers are paid a share of the value of the marginal worker. The model predicts that employment and labor costs react differently to transitory shocks and permanent shocks. Quantitative evaluation of the model on a panel of French firms shows the model’s performance. Modest adjustment costs are needed to reproduce observed job reallocation and inaction rates. Removing adjustment costs leads to productivity gains of 1% at the steady state. These gains are 50% larger in a economy with only transitory shocks and an order of magnitude lower in an economy with only permanent shocks. Bargaining dampens the reallocation of labor across firms, leading to larger efficiency losses from adjustment costs.