Nicolas A. Papageorgiou

Copula-based semiparametric models for multivariate time series

The authors extend to multivariate contexts the copula-based univariate time series modeling approach of Chen & Fan [X. Chen, Y. Fan, Estimation of copula-based semiparametric time series models, J. Econometrics 130 (2006) 307-335; X. Chen, Y. Fan, Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification, J. Econometrics 135 (2006) 125-154]. In so doing, they tackle simultaneously serial dependence and interdependence between time series. Their technique differs from the usual approach to time series copula modeling in which the series are first modeled individually and copulas are used to model the dependence between their innovations. The authors discuss parameter estimation and goodness-of-fit testing for their model, with emphasis on meta-elliptical and Archimedean copulas. The method is illustrated with data on the Canadian/US exchange rate and the value of oil futures over a ten-year period.

Replicating the Properties of Hedge Fund Returns

In this article, the authors implement a multivariate extension of the Dybvig [1988] Payoff Distribution Model that can be used to replicate not only the marginal distribution of most hedge fund returns but also their dependence on other asset classes. In addition to proposing ways to overcome the hedging and compatibility inconsistencies in Kat and Palaro [2005], the authors extend the results of Schweizer [1995] and adapt American option pricing techniques to evaluate the model and also derive an optimal dynamic trading (hedging) strategy. The proposed methodology can be used as a benchmark for evaluating fund performance, as well as to replicate hedge funds or generate synthetic funds.

A Constant-Volatility Framework for Managing Tail Risk

Since Lehman Brothers collapsed in 2008, tail-risk hedging has become an increasingly important concern for investors. Traditional approaches, such as purchasing options or variance swaps as insurance, are often expensive, illiquid, and result in a substantial drag on performance. A more prudent, cost-effective way to maintain a constant risk exposure is to actively manage portfolio exposure according to the prevailing volatility level within underlying assets. The authors implement a robust methodology based on Dybvig’s payoff distribution model to target a constant level of volatility and normalize monthly returns. This approach to portfolio and risk management can help investors obtain their desired risk exposures over both short and longer time frames, reduce exposure to tail risk, and in general increase portfolios’ risk-adjusted performance.

Credit migration and basket derivatives pricing with copulas

The multivariate modeling of default risk is a crucial aspect of the pricing of credit derivative products referencing a portfolio of underlying assets, and the evaluation of Value at Risk of such portfolios. This paper proposes a model for the joint dynamics of credit ratings of several firms. Namely, individual credit ratings are modeled by univariate continuous time Markov chain, while their joint dynamic is modeled using copulas. A by-product of the method is the joint laws of the default times of all the firms in the portfolio. The use of copulas allows us to incorporate our knowledge of the modeling of univariate processes, into a multivariate framework. The Normal and Student copulas commonly used in the literature as well as by practitioners do not produce very different estimates of default risk prices. We show that this result is restricted to these two two basic copulas. That is, for any other family of copula, the choice of the copula greatly affects the pricing of default risk.

Higher-Moment Risk Exposures in Hedge Funds

The paper singles out the key roles of US equity skewness and kurtosis in the determination of the market premia embedded in Hedge Fund returns. We propose a conditional higher-moment asset pricing model with location, trading and higher-moment factors in order to describe the dynamics of the Equity Hedge (Market Neutral, Short Selling and Long/Short strategies), Event Driven, Relative Value, and Funds of Hedge Funds styles. The volatility, skewness and kurtosis implied in the US options markets are used by Hedge Fund managers as instruments to anticipate market movements. Managers should adjust their market exposure in response to variations in the implied higher moments. We show that higher-moment premia improve a conditional asset pricing model both in terms of explanatory power (R-squares and Schwarz criterion) and specification errors across all Hedge Fund styles.

The payoff distribution model: an application to dynamic portfolio insurance

We propose an innovative approach for dynamic portfolio insurance that overcomes many of the limitations of the earlier techniques. We transform the Payoff Distribution Model, originally introduced by Dybvig (1988) as a performance measure, to a fund management tool. This approach allows us to generate funds with pre-specified distributional properties. Specifically, we generate funds that are characterized by a Left Truncated Gaussian distribution and then demonstrate out of sample, using different performance and risk measures, that this approach to managing market exposure leads to a better risk control at a lower cost than more popular techniques such as the CPPI.

Optimal Hedging of American Options in Discrete Time

In this article we study the price of an American style option based on hedging the underlying assets in discrete time. Like its European style analog, the value of the option is not given in general by an expectation with respect to an equivalent martingale measure. We provide the optimal solution that minimizes the hedging error variance. When the assets dynamics are Markovian or a component of a Markov process, the solution can be approximated easily by numerical methods already proposed for pricing American options. We proceed to a Monte Carlo experiment in which the hedging performance of the solution is evaluated. For assets returns that are either Gaussian or Variance Gamma, it is shown that the proposed solution results in lower root mean square hedging error than with traditional delta hedging.

Predicting the Direction of Interest Rate Movements

This research develops a simple probit model for predicting the direction of long-term interest rates. One variation uses the slope of the term structure, and another uses the forward rate as a predictor variable. Out-of-sample tests on Federal Reserve data indicate that for a one-month forecast horizon, the model correctly predicts the direction of 5-, 7-, 10-, and 30-year yields with more than 60% success. The success rate is nearly as good when we use data obtained from yield curve estimates. While these results are not good enough to be the sole determinant in bond investment strategies, the model can provide useful information.

Betas and the Myth of Market Neutrality

Market neutral funds are commonly advertised as alternative investments that offer returns which are uncorrelated with the broad market. Utilizing recent advances in financial econometrics, we demonstrate that using standard forecasting methods to construct market (beta) neutral funds is often very inaccurate. Our findings demonstrate that the econometric methods that are commonly employed for forecasting the beta (systematic) risk typically lack sufficient accuracy to permit the successful construction of market neutral portfolios. The results in this paper also highlight the need for higher frequency returns data to be utilized more commonly. Using daily returns over the past year, we demonstrate an approach that is easy to implement and delivers a substantial improvement, relative to other methods, when attempting to construct a market neutral portfolio.

Where do Hedge Fund Managers Come from? Past Employment Experience and Managerial Performance

Hedge funds are secretive products whose quality is difficult to ascertain in advance of investment. We examine two views of past work experience as predictors of hedge fund manager pedigree. In one, sector specific (hedge fund) work experience is positively related to performance. In the other, related industry (mutual funds, prime brokerages, custodian firms and securities brokerages) experience correlates with superior performance. Overall, aspects of specific and generally related industry experience appear important in signaling hedge fund quality. Funds whose management team possesses past hedge fund experience report superior performance. However, diversifying across experience types in a fund has no impact on returns. Hedge fund manager teams with prime brokerage and custodian experience along both proportional and diversity dimensions experience higher survival probabilities.