Model Averaging in Risk Management with an Application to Futures Markets

By M. Hashem. Social Science Research Network, 2009

136 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/acc4ed021945dbfef06958dc422ecde17fe4c1d5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Xinyu Zhang
2. Alan T. K. Wan
3. Ker-Chau Li
4. Guohua Zou
5. Tomohiro Ando
6. Liangjun Su
7. Xun Lu
8. Giuseppe De Luca

In Finance

1. Xinyu Zhang
2. Ker-Chau Li
3. Guohua Zou
4. Tomohiro Ando
5. Xun Lu
6. Liangjun Su
7. J. R. Magnus
8. Giuseppe De Luca

Citations in the corpus

• A Model-Averaging Approach for High-Dimensional Regression (2014, background): “…for applications such as stock returns (Avramov 2002), exchange rates (Wright 2008), output growth (Garratt et al. 2003), portfolio management (Pesaran, Schleicher, and Zaffaroni 2009), currency crisis analysis (Crespo-Cuaresma and Slacik 2009), analysis of preferential trade agreements…”
• Robust risk measurement and model risk (2014, unknown): “This idea is developed from a Bayesian perspective in, for example, Draper [19] and Raftery, Madigan, and Hoeting [37] and applied to portfolio selection in Pesaran, Schleicher, and Zaffaroni [35].”
• Model averaging by jackknife criterion in models with dependent data (2013, background): “There is also an emerging empirical literature that employs FMA in applied settings (Kapetanios et al., 2008a,b; Pesaran et al., 2009; Wan and Zhang, 2009).”
• "Momentum Strategies in Futures Markets and Trend-following Funds" (2013, unknown): “8 In line with the futures literature (e.g. see de Roon et al., 2000; Pesaran et al., 2009; Moskowitz et al., 2012), we find that there is large cross-sectional variation in the return distributions of the different assets in our dataset.”