Now showing 1 - 6 of 6
  • Publication
    Time varying risk aversion : an application to energy hedging
    Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hedging market participants. The resulting estimates are applied to derive explicit risk aversion based optimal hedge strategies for both short and long hedgers. Out-of-sample results are also presented based on a unique approach that allows us to forecast risk aversion, thereby estimating hedge strategies that address the potential future needs of energy hedgers. We find that the risk aversion based hedges differ significantly from simpler OLS hedges. When implemented in-sample, risk aversion hedges for short hedgers outperform the OLS hedge ratio in a utility based comparison.
      967
  • Publication
    Housing risk and return : evidence from a housing asset-pricing model
    This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. Assuming investment is restricted to housing, the paper specifies and tests a housing asset pricing model, whereby expected returns of metropolitan-specific housing markets are equated to the market return, as represented by aggregate US house price time-series. We augment the model by examining the impact of additional risk factors including aggregate stock market returns, idiosyncratic risk, momentum, and Metropolitan Statistical Area (MSA) size effects. Further, we test the robustness of the asset pricing results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. We find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show that market betas have varied substantially over time. Also, we find the basic housing model results are robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests on the validity of the model using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller behavioral research indicating the importance of speculative forces in the determination of U.S. housing returns.
      872
  • Publication
    Hedging and risk aversion constraints
    (University College Dublin. School of Business. Centre for Financial Markets, 2005) ;
    We examine whether hedging effectiveness is affected by asymmetry in the return distribution by applying tail specific metrics to compare the hedging effectiveness of short and long hedgers using crude oil futures contracts. The metrics used include Lower Partial Moments (LPM), Value at Risk (VaR) and Conditional Value at Risk (CVAR). Comparisons are applied to a number of hedging strategies including OLS and both Symmetric and Asymmetric GARCH models. Our findings show that asymmetry reduces in-sample hedging performance and that there are significant differences in hedging performance between short and long hedgers. Thus, tail specific performance metrics should be applied in evaluating hedging effectiveness. We also find that the Ordinary Least Squares (OLS) model provides consistently good performance across different measures of hedging effectiveness and estimation methods irrespective of the characteristics of the underlying distribution.
      178
  • Publication
    Realized volatility and minimum capital requirements
    (Money Macro and Finance Research Group, 2003)
    Key to the imposition of appropriate minimum capital requirements on a daily basis requires accurate volatility estimation. Here, measures are presented based on discrete estimation of aggregated high frequency UK futures realisations underpinned by a continuous time framework. Squared and absolute returns are incorporated into the measurement process so as to rely on the quadratic variation of a diffusion process and be robust in the presence of fat tails. The realized volatility estimates incorporate the long memory property. The dynamics of the volatility variable are adequately captured. Resulting rescaled returns are applied to minimum capital requirement calculations.
      480
  • Publication
    Validating the backtests of risk measures
    Financial risk model evaluation or backtesting is a key part of the internal model’s approach to market risk management as laid out by the Basle Committee on Banking Supervision (2004). However there are a number of backtests that may be applied and there is little guidance as to the most appropriate method. The goal of this paper is to analyze the ability of various evaluation methodologies to gauge the accuracy of risk models. We compare evaluation effectiveness using the standard binomial approach, together with the interval forecast backtesting, the density forecast backtesting and the probability forecast backtesting. Our comparison is completed for three risk measures: Value-at-Risk (VaR), Expected Shortfall (ES) and Spectral Risk measure (SRM). We pay special attention to applications related to ES and SRM as backtesting of these models have not been explored in any detail thus far. Based on the Monte Carlo simulations and the empirical study, a number of interesting results emerge. Firstly within hypothesisbased tests, including the binomial backtesting, the interval forecast backtesting and the density forecasts backtesting, the overall dominance of density forecast backtesting is confirmed. In particular, the backtesting for SRM and ES is more effective than for VaR in identifying an incorrect model from alternative models in a small sample setting. Secondly, we propose a loss function for SRM where the probability forecast backtesting is capable of identifying accurate models from alternative models. Thirdly, in all of the backtesting methods examined, the choice of the distribution specification is a more important factor in determining the evaluation performance than the choice of the volatility specification.
      1074
  • Publication
    Housing risk and return : evidence from a housing asset-pricing model
    This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a housing asset pricing model (H-CAPM), whereby expected returns of metropolitan-specific housing markets are equated to the market return, as represented by aggregate US house price time-series. We augment the model by examining the impact of additional risk factors including aggregate stock market returns, idiosyncratic risk, momentum, and Metropolitan Statistical Area (MSA) size effects. Further, we test the robustness of H-CAPM results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. Consistent with the traditional CAPM, we find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show that market betas have varied substantially over time. Also, we find the basic housing CAPM results are robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests of the validity of the model using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller behavioral research indicating the importance of speculative forces in the determination of U.S. housing returns.
      1674