1,721,064 research outputs found
Downside risk asset pricing revisited: a new non-linear threshold model
No full textWe derive an asset pricing equilibrium formula in which the risk premium on a risky asset is given by a weighted sum of the regular beta capital asset pricing model and a market portfolio downside risk beta. The equilibrium model is obtained from a new utility function that builds on the class of downside risk functions introduced in Bawa (1975, 1978) and that can be interpreted as an alternative to the disappointment utility functions of Dekel (1986) and Gul (1991), and the loss aversion utility functions as in Tversky and Kahneman (1991, 1992). This equilibrium model is econometrically represented by a non-linear threshold model that depends on a target return indicating market downturns. In the case where the target return is unknown we introduce an estimator of this threshold and a hypothesis test to assess statistically the significance of the downside risk parameter in the risk premium of the risky asset. An empirical exercise to industry, size and book-to-market portfolios uncovers a strong relationship between the risk premium and market portfolio downside ris
On the role of volatility for modelling risk exposure
No full textWe show in this paper that volatility measures can be misleading indicators of risk if returns do not follow a Gaussian distribution. A more reliable measure of risk is the probability distribution of the return on an asset. Estimators for these measures are usually challenging and need of nonparametric and semi-parametric techniques. The aim of this paper is twofold. First, it proposes the use of semi-parametric estimators of the distribution function of the return on an asset based on extreme value theory for computing Value-at-Risk; and second, it discusses the validity of different volatility models in this semi-parametric framework. The conclusion is that different volatility models can yield different valid risk measures if coupled with the appropriate distribution function. Hence the puzzle in the choice of volatility measures. This is shown in an empirical exercise for data of financial indexes from USA, UK, Germany, Japan and Spain
Optimal portfolio allocation and asset centrality revisited
Full text linkThis paper revisits the relationship between eigenvector asset centrality and optimal asset allocation in a minimum variance portfolio. We show that the standard definition of eigenvector centrality is misleading when the adjacency matrix in a network can take negative values. This is, for example, the case when the network topology is induced by the correlation matrix between assets in a portfolio. To correct for this, we introduce the concept of positive and negative eigenvector centrality. Our results show that the loss function associated to the minimum variance portfolio is positively/negatively related to the positive and negative eigenvector centrality under short-selling constraints butcannot be generalized beyond that. Furthermore, in contrast to what is claimed in the related literature, this relationship does not imply any monotonic relationship between the centrality of an asset and its optimal portfolio allocation. These theoretical insights are illustrated empirically in a portfolio allocation exercise with assets from U.S. and U.K. financial markets
A nonparametric predictive regression model using partitioning estimators based on Taylor expansions
Full text linkThis article proposes a nonparametric predictive regression model. The unknown function modeling the predictive relationship is approximated using polynomial Taylor expansions applied over disjoint intervals covering the support of the predictor variable. The model is estimated using the theory on partitioning estimators that is extended to a stationary time series setting. We show pointwise and uniform convergence of the proposed estimator and derive its asymptotic normality. These asymptotic results are applied to test for the presence of predictive ability. We develop an asymptotic pointwise test of predictive ability using the critical values of a Normal distribution, and a uniform test with asymptotic distribution that is approximated using a p-value transformation and Wild bootstrap methods. These theoretical insights are illustrated in an extensive simulation exercise and also in an empirical application to forecasting high-frequency based realized volatility measures. Our results provide empirical support to the presence of nonlinear autoregressive predictability of these measures for the constituents of the Dow Jones index.</p
Differences between short and long term risk aversion: an optimal asset allocation perspective
No full textThis paper studies the long-term asset allocation problem of an investor with different risk aversion attitudes to the short and the long term. We characterize investor's preferences with a utility function exhibiting a regime shift in risk aversion at some point of the multiperiod investment horizon that is estimated using threshold nonlinearity methods. Our empirical results for a portfolio of cash, bonds and stocks suggest that long-term risk aversion is higher than short-term risk aversion and increases with the investment horizon. The exposure of the investment portfolio from stocks to bonds and cash increases with the degree of risk aversion
Optimal portfolio choices using financial leverage
No full textThis paper investigates the role of leverage in determining the investor’s optimal asset allocation over multiperiod investment horizons. To do this, we allow investors to lever their financial position by borrowing from credit markets. GMM methods are used to estimate and test the optimal portfolio weights and individual’s optimal choice of financial leverage. These optimal choices are assumed to be parametric functions of a set of state variables describing the evolution of the economy. The empirical application of this methodology to a portfolio of cash, bonds and stocks reveals that a) financial leverage limits the reaction of investors to changes in the investment opportunity set; b) individuals increase leverage during recessions and deleverage in expansionary periods; c) optimal portfolio weights and financial leverage are negatively related to the degree of investor’s risk aversion and positively related to the investment horizo
A nonlinear threshold model for the dependence of extremes of stationary sequences
No full textWe propose a TAR(3,1)-GARCH(1,1) model able to describe two different types of extreme events: a first type generated by large uncertainty regimes and a second type where extremes come from isolated dread/joy events. The novelty of this model resides on the definition of the regimes, motivated by the occurrence of extreme values, and of the threshold variable, defined by the shock affecting the process one period lagged. The model is able to uncover dependence and clustering of extremes in high and low volatility periods. A Wald type test to detect nonlinearities on the conditional mean process defined by an unobservable threshold variable is introduced. In the empirical application, we find evidence of predictability for extreme returns on SPDR S&P500 fund during the recent crisis period, July 2008 to March 2011. This finding seems to support the presence of some persistence and mean reversion in the dynamics of returns after the occurrence of extreme shocks
Volatility spillover between economic sectors in financial crisis prediction: Evidence spanning the great financial crisis and Covid-19 pandemic
No full textThis paper measures volatility spillovers between sectors of economic activity using network connectivity measures. Volatility spillovers are an accurate proxy for the transmission of risk across sectors and are particularly informative during crisis periods. To do this, we apply the novel methodology proposed in Diebold and Yilmaz (2012) to seven economic sectors of U.S. economic activity and find that Banking&Insurance, Energy, Technology and Biotechnology are the main channels through which shocks propagate to the rest of the economy. Banking&Insurance is especially relevant during the 2007–2009 global financial crisis while the Energy sector and Technology are especially relevant during the COVID-19 crisis. We also show that volatility spillovers exhibit ability to predict high episodes of volatility for the S&P 500 index being useful as early financial crisis indicators
Hedging demand in long-term asset allocation with an application to carry trade strategies
No full textWe derive a closed-form expression for the mean and marginal hedging demand on risky assets in long-term asset allocation problems for individuals with CRRA preferences. Our parametric portfolio policy rule accommodates an arbitrarily large number of state variables for predicting the state of nature, and number of assets in the portfolio. The closedform expression for the hedging demand is exact under polynomial specifications of the portfolio policy rule and a suitable approximation for unknown smooth parametric portfolio policy rules using Taylor expansions. The hedging demand on risky assets depends positively on the predictability of the risky asset and the persistence of the predictors, and negatively on the degree of investor’s relative risk aversion. We illustrate these insights empirically for a basket of currencies by showing the outperformance of rebalancing carry trade strategies over different investment horizons against a short-term (myopic) portfoli
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