Model Averaging in Risk Management with an Application to Futures Markets

This paper considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the use of model averaging techniques as a way of dealing with the risk of inadvertently using false models in portfolio management. Evaluation of volatility models is then considered and a simple Value-at-Risk (VaR) diagnostic test is proposed for individual as well as ‘average ’ models. The asymptotic as well as the exact finite-sample distribution of the test statistic, dealing with the possibility of parameter uncertainty, are established. The model averaging idea and the VaR diagnostic tests are illustrated by an application to portfolios of daily returns on six currencies, four equity indices, four ten year government bonds and four commodities over the period 1991-2007. The empirical evidence supports the use of ‘thick’ model averaging strategies over single models or Bayesian type model averaging procedures

Is Home Bias in Assets Related to Home Bias in Goods?

Obstfeld and Rogoff (2000) have reinvigorated an old literature on the link between home bias in the goods market and home bias in the asset market by arguing that trade costs in the goods market can account for the observed portfolio home bias. The key link between home bias in the two markets is the real exchange rate. Home bias in consumption implies a different expenditure allocation across countries, which leads to different inflation rates when measured in the same currency. This leads investors from different countries to choose different portfolios to hedge against inflation uncertainty. An older partial equilibrium literature argued that such hedge portfolios are not large enough to produce substantial home bias. We link the general equilibrium and partial equilibrium literatures and show that in both the resulting home bias in the equity market depends on a covariance-variance ratio: the covariance between the real exchange rate and the excess return on home relative to foreign equity, divided by the variance of the excess return. Empirical evidence shows that this ratio and the implied home bias are close to zero, casting significant doubt on a meaningful link between home bias in the goods and asset markets. General equilibrium models that conclude otherwise imply a covariance-variance ratio that is at odds with the data.

Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data

Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time-varying joint distribution of the oil -- stocks pair accurately. Our realized GARCH with time-varying copula yields statistically better forecasts of the dependence and quantiles of the distribution relative to competing models. Employing a recently proposed conditional diversification benefits measure that considers higher-order moments and nonlinear dependence from tail events, we document decreasing benefits from diversification over the past ten years. The diversification benefits implied by our empirical model are, moreover, strongly varied over time. These findings have important implications for asset allocation, as the benefits of including oil in stock portfolios may not be as large as perceived

Dynamic Optimal Portfolio Selection in a VaR Framework

We propose a dynamic portfolio selection model that maximizes expected returns subject to a Value-at-Risk constraint. The model allows for time varying skewness and kurtosis of portfolio distributions estimating the model parameters by weighted maximum likelihood in a increasing window setup. We determine the best daily investment recommendations in terms of percentage to borrow or lend and the optimal weights of the assets in the risky portfolio. Two empirical applications illustrate in an out-of-sample context which models are preferred from a statistical and economic point of view. We find that the APARCH(1,1) model outperforms the GARCH(1,1) model. A sensitivity analysis with respect to the distributional innovation hypothesis shows that in general the skewed-t is preferred to the normal and Student-t.Portfolio Selection; Value-at-Risk; Skewed-t distribution; Weighted Maximum Likelihood.

1/N and Long Run Optimal Portfolios: Results for Mixed Asset Menus

Recent research [e.g., DeMiguel, Garlappi and Uppal, (2009a), Rev. Fin. Studies] has cast doubts on the out-of-sample performance of optimizing portfolio strategies relative to a naive, equally-weighted ones. However, most of the existing results concern the simple case in which an investor has a one-month horizon and mean-variance preferences. In this paper, we examine whether this finding holds for longer investment horizons, when the asset menu includes bonds and real estate beyond stocks and cash, and when the investor is characterized by constant relative risk aversion preferences which are not locally mean-variance for long horizons. Our experiments indicates that power utility investors with horizons of one year and longer would have on average benefited, ex-post, from an optimizing strategy that exploits simple linear predictability in asset returns over the period January 1995 - December 2007. This result is insensitive to the degree of risk aversion, to the number of predictors being included in the forecasting model, and to the deduction of transaction costs from measured portfolio performance.equally weighted portfolios; long investment horizon; real-time strategic asset allocation; public real estate vehicles; ex post performance; predictability; parameter uncertainty

1/N and long run optimal portfolios: results for mixed asset menus

Recent research [e.g., DeMiguel, Garlappi and Uppal, (2009), Rev. Fin. Studies] has cast doubts on the out-of-sample performance of optimizing portfolio strategies relative to naive, equally weighted ones. However, existing results concern the simple case in which an investor has a one-month horizon and meanvariance preferences. In this paper, we examine whether their result holds for longer investment horizons, when the asset menu includes bonds and real estate beyond stocks and cash, and when the investor is characterized by constant relative risk aversion preferences which are not locally mean-variance for long horizons. Our experiments indicates that power utility investors with horizons of one year and longer would have on average benefited, ex-post, from an optimizing strategy that exploits simple linear predictability in asset returns over the period January 1995 - December 2007. This result is insensitive to the degree of risk aversion, to the number of predictors being included in the forecasting model, and to the deduction of transaction costs from measured portfolio performance.Econometric models ; Asset pricing ; Rate of return

Pricing Liquidity Risk with Heterogeneous Investment Horizons

We develop an asset pricing model with stochastic transaction costs and investors with heterogeneous horizons. Depending on their horizon, investors hold different sets of assets in equilibrium. This generates segmentation and spillover effects for expected returns, where the liquidity (risk) premium of illiquid assets is determined by investor horizons and the correlation between liquid and illiquid asset returns. We estimate our model for the cross-section of U.S. stock returns and find that it generates a good fit, mainly due to a combination of a substantial expected liquidity premium and segmentation effects, while the liquidity risk premium is small

Optimal Value and Growth Tilts in Long-Horizon Portfolios

We develop an analytical solution to the dynamic portfolio choice problem of an investor with power utility defined over wealth at a finite horizon who faces an investment opportunity set with time-varying risk premia, real interest rates and inflation. The variation in investment opportunities is captured by a flexible vector autoregressive parameterization, which readily accommodates a large number of assets and state variables. We find that the optimal dynamic portfolio strategy is an affine function of the vector of state variables describing investment opportunities, with coefficients that are a function of the investment horizon. We apply our method to the optimal portfolio choice problem of an investor who can choose between value and growth stock portfolios, and among these equity portfolios plus bills and bonds. For equity-only investors, the optimal mean allocation of short-horizon investors is heavily tilted away from growth stocks regardless of their risk aversion. However, the mean allocation to growth stocks increases dramatically with the investment horizon, implying that growth is less risky than value at long horizons for equity-only investors. By contrast, long-horizon conservative investors who have access to bills and bonds do not hold equities in their portfolio. These investors are concerned with interest rate risk, and empirically growth stocks are not particularly good hedges for bond returns. We also explore the welfare implications of adopting the optimal dynamic rebalancing strategy vis a vis other intuitive, but suboptimal, portfolio choice schemes and find significant welfare gains for all long-horizon investors.

Generalized asset pricing: Expected Downside Risk-Based Equilibrium Modelling

We introduce an equilibrium asset pricing model, which we build on the relationship between a novel risk measure, the Expected Downside Risk (EDR) and the expected return. On the one hand, our proposed risk measure uses a nonparametric approach that allows us to get rid of any assumption on the distribution of returns. On the other hand, our asset pricing model is based on loss-averse investors of Prospect Theory, through which we implement the risk-seeking behaviour of investors in a dynamic setting. By including EDR in our proposed model unrealistic assumptions of commonly used equilibrium models - such as the exclusion of risk-seeking or price-maker investors and the assumption of unlimited leverage opportunity for a unique interest rate - can be omitted. Therefore, we argue that based on more realistic assumptions our model is able to describe equilibrium expected returns with higher accuracy, which we support by empirical evidence as well.Comment: 55 pages, 15 figures, 1 table, 3 appandices, Econ. Model. (2015