Search

This paper provides some theoretical foundations for using moving average (MA) rules in the stock market. In particular, the paper analyzes the conditional probability of price increments and examines how this probability varies over time. We prove under certain assumptions that the probability of being in an uptrend is greater than the probability of being in a downtrend. This demonstration partially justifies the common use of MA rules in the stock market. Finally, we propose an ex-post empirical analysis to evaluate and compare the performance of some MA rules and other portfolio strategies in the US stock market. In this context, we also suggest a methodology that incorporates these trading rules as alarm rules to predict potential market failures. Our ex-post results confirm the advantages of using these trading rules to predict market trends and crises. [ABSTRACT FROM AUTHOR]

In this paper, we develop a portfolio optimization method to maximize the performance of a fixed income portfolio. To achieve this aim, we define a two-step optimization problem where we firstly manage the immunization risk and then we maximize the portfolio wealth in a reward-risk framework. In the first optimization step, we create funds of bonds with constant immunization measure over time, and we propose an innovative immunization measure for bond portfolio management that leads to a more flexible immunization approach and a better trade-off between reward and risk. In the second optimization step, maximizing two performance measures on these baskets of bonds we obtain portfolio strategies that consider different investors' profiles. An empirical application to the US fixed income market during 2002-2012 period is provided. Applying the portfolio optimization method to different bond classes, we compare the results with an equity index. This ex-post analysis indicates the benefits of the proposed portfolio strategy in outperforming the benchmark and proves that capital flows to safer markets during crisis periods. [ABSTRACT FROM AUTHOR]

The article proposes two alternative dispersion measures consistent with additive shifts that can be used in investment portfolio selection problems. It examines the stochastic dominance of mean-dispersion approach and the properties of the two dispersion measures and compares these measures with the standard deviation and the conditional value at risk benchmark on the U.S. stock market. An analysis of portfolio diversification and turnover of investment strategies is presented.

In this article we study the portfolio selection problem in the presence of systemic risk. We propose reward-risk measures that account for systemic risk and provide a methodology to generate realistic return scenarios. The methodology involves first analyzing the empirical behavior of several MSCI country indexes, suggesting how to approximate future scenarios. Then we examine the profitability of several strategies based on the forecasted evolution of returns. In particular, we compare the optimal sample paths of future wealth obtained by performing reward-risk portfolio optimization on simulated data and we discuss the ex-post performance of the proposed portfolio strategies. [ABSTRACT FROM AUTHOR]

In this note, we examine the impact of non linear reward and risk measures on portfolio selection. In particular, we compare the ex-post final wealth sample paths of strategies based on the Sharpe ratio and strategies based on non-linear reward/risk measures. As suggested by the recent literature, we model dependencies with an asymmetric t copula estimated on the innovations of the marginals that follow an ARMA-GARCH type model. Therefore, we first simulate future scenarios, on the basis of which allocation decisions are made, and then we compare the ex-post final wealth obtained with non-linear risk and reward strategies and the wealth obtained with classic portfolio strategies. [ABSTRACT FROM AUTHOR]

In this paper; we propose a methodology to value the portfolio choices based on the prediction of future returns where the dependence structure of joint returns and the behavior of single returns are estimated separately. In particular, we assume the marginals evolve as an ARMA(0,2)-GARCH(0,2) model with stable paretian residuals and the joint distribution of residuals is estimated with an asymmetric t-copula. Then, we compare the ex-post final wealth sample paths of different strategies based on reward/risk ratios. Doing so we examine and discuss the impact of the forecasting method with respect to classic myopic portfolio strategies based on the same reward/risk ratios. [ABSTRACT FROM AUTHOR]

In this paper we describe and apply estimating function theory to evaluate the parameters of parametric distributions uniquely defined by their characteristic functions. We first implement an estimating function model based on the first four moments of a parametric function of the underlying random variables. For instance we propose two parametric functions of the underlying random variables so as to obtain its first moments more easily by the simple knowledge of the characteristic function. Thus we consider the estimates that present the minimal asymptotic variance with respect to the parameter of the function. Then we propose an empirical analysis based on simulated stable Paretian distributions. Using simulated data of stable distributions we evaluate the forecasting power of the proposed methodology comparing it with the analogous maximum likelihood estimates. Moreover we show how to apply the same methodology to some well-known infinitely divisible distributions. [ABSTRACT FROM AUTHOR]

This paper unifies the classical theory of stochastic dominance and investor preferences with the recent literature on risk measures applied to the choice problem faced by investors. First, we summarize the main stochastic dominance rules used in the finance literature. Then we discuss the connection with the theory of integral stochastic orders and we introduce orderings consistent with investors' preferences. Thus, we classify them, distinguishing several categories of orderings associated with different classes of investors. Finally, we show how we can use risk measures and orderings consistent with some preferences to determine the investors' optimal choices. [ABSTRACT FROM AUTHOR]

In this paper, we provide further insight into the stock return momentum phenomena by investigating the sources of momentum profits. Applying statistical factor analysis, we identify the most important variables significantly affecting momentum profits: volatility and changes in the currency component of M1. We also document the periodic dynamics of momentum returns that is their inflation at quarter-, month- and year- ends and their deflation at quarter-, month- and year - beginnings. [ABSTRACT FROM AUTHOR]

This paper examines the properties that a risk measure should satisfy in order to characterize an investor's preferences. In particular, we propose some intuitive and realistic examples that describe several desirable features of an ideal risk measure. This analysis is the first step in understanding how to classify an investor's risk. Risk is an asymmetric, relative, heteroskedastic, multidimensional concept that has to take into account asymptotic behavior of returns, inter-temporal dependence, risk-time aggregation, and the impact of several economic phenomena that could influence an investor's preferences. In order to consider the financial impact of the several aspects of risk, we propose and analyze the relationship between distributional modeling and risk measures. Similar to the notion of ideal probability metric to a given approximation problem, we are in the search for an ideal risk measure or ideal performance ratio for a portfolio selection problem. We then emphasize the parallels between risk measures and probability metrics, underlying the computational advantage and disadvantage of different approaches. [ABSTRACT FROM AUTHOR]

This paper discusses and analyzes risk measure properties in order to understand how a risk measure has to be used to optimize the investor's portfolio choices. In particular, we distinguish between two admissible classes of risk measures proposed in the portfolio literature: safety-risk measures and dispersion measures. We study and describe how the risk could depend on other distributional parameters. Then, we examine and discuss the differences between statistical parametric models and linear fund separation ones. Finally, we propose an empirical comparison among three different portfolio choice models which depend on the mean, on a risk measure, and on a skewness parameter. Thus, we assess and value the impact on the investor's preferences of three different risk measures even considering some derivative assets among the possible choices. [ABSTRACT FROM AUTHOR]

This article focuses on risk estimation in portfolio theory. Financial risk was considered a correcting factor for expected return. The Markowitz mean- variance framework was an excellent beginning, with the main innovation being measurement of the dependence structure of returns while accounting for their correlation. The absolute deviation of a random variable is the expected absolute value of the difference between the random variable and its mean. The absolute deviation is a linear risk measure consistent with a stochastic dominance order. The stable dispersion measure is the scale parameter of a stable Paretian distribution (also called the a-stable distribution). Therefore, the mean-stable dispersion approach is a distributional approach based on the assumption that returns are stable Paretian distributed. The functional central limit theorem for normalized sums of independent and identically distributed random variables theoretically justifies the stable Paretian approach.