The impact of association measures within the portfolio dimensionality reduction problem

The dependency structure of random sources plays a crucial role in portfolio theory and in several pricing and risk management problems. In this paper, we discuss the possible usage of alternative association measures in portfolio problems. Among association measures, we highlight those that are consistent with the choices of risk-averse investors and we characterise semidefinite positive association measures. Additionally, we propose new portfolio selection problems that optimise the association between the portfolio and market benchmarks and follow a dimensionality reduction problem. Finally, by carrying out an empirical analysis, we show the impact of selected association measures within the portfolio problem. This analysis proves that the proper usage of both a risk measure and an association measure can increase the portfolio performance substantially

Structural credit risk models with subordinated processes

We discuss structural models based on Merton's framework. First, we observe that the classical assumptions of the Merton model are generally rejected. Secondly, we implement a structural credit risk model based on stable non-Gaussian processes as a representative of subordinated models in order to overcome some drawbacks of the Merton one. Finally, following the KMV-Merton estimation methodology, we propose an empirical comparison between the results obtained from the classical KMV-Merton model and the stable Paretian one. In particular, we suggest alternative parameter estimation for subordinated processes, and we optimize the performance for the stable Paretian model.Web of Scienceart. no. 13827

Optimal portfolio performance with exchange-traded funds

In this paper, the portfolio selection problem in exchange-traded fund (hereafter ETF) markets is considered. Since the ETFs track some market indexes with lower costs than the indexes, their development and popularity is grown enormously in the last decade. Moreover, ETF characteristics also present several advantages for the investors that we briefly examine for the U.S. and European markets of ETFs. In particular, we first introduce a new performance measure consistent with the optimal choices of non-satiable risk-averse investors and then we discuss the optimization of a few performance measures on the U.S. and European ETF markets. Finally, we propose an empirical comparison among the ex-post wealth obtained by optimizing the new performance measure, the Sharpe ratio and the Rachev ratio

Risk profile using PCM and RSM

In this paper we analyze the investors’ risk profile in order to meet the minimal requirements that Italian financial institutions must satisfy by law. We focus particularly on three latent traits of the investor’s risk profile: knowledge of financial instruments, the investor’s personal predisposition to risk/earn, and the investor’s temporal horizon. We specifically identify a questionnaire whose items describe different characteristics of these three latent variables. In order to take into account the investor’s preferences and his/her psychological attitude we propose analyzing the risk profile questionnaire with two different sub-models of the polytomous Rasch model: the Partial Credit Model (PCM) and the Rating Scale Model (RSM). Finally, we discuss the possible uses of the proposed analysis in a financial context

Backtesting AVaR and VaR with a simulated copula

The aim of this study is to verify whether the average value at risk (AVaR) can be a good alternative to the value at risk (VaR) for estimating portfolio losses, especially regarding tail events. To achieve this aim, we use a copula framework to estimate the dependence between the stock returns of a portfolio composed of 94 components of the S&P100 index to compute the AVaR and VaR and compare the results with respect to the Gaussian exponentially weighted moving average (EWMA). To compute the simulated returns, we employ the algorithm used by Biglova et al. (2014) in portfolio selection problems and then backtest the model with Kupiec’s and Christoffersen’s tests. The results are coherent with the literature; in particular, the VaR computed both via the copula and via the EWMA seems to fail to provide an accurate risk measurement while the AVaR with the copula and EWMA appears to be more reliable

An Empirical Comparison Among VaR Models and Time Rules with Elliptical and Stable Distributed Returns

Abstract This paper compares and investigates the impact of different VaR models with conditional elliptical and stable distributed returns. In particular, we analyze some non-Gaussian VaR models and discuss the applicability of some temporal aggregation rules. Thus, we propose and examine the performance of several VaR models: (i) an EWMA model with Student's t conditional distributions, (ii) a stable sub-Gaussian model, (iii) a stable asymmetric model. All models are subjected to backtest on out-of-sample data in order to assess their forecasting power and to show how the associated aggregation rules are performed in practice