Serge Darolles

Structural Laplace Transform and Compound Autoregressive Models

This paper presents a new general class of compound autoregressive (Car) models for non-Gaussian time series. The distinctive feature of the class is that Car models are specified by means of the conditional Laplace transforms. This approach allows for simple derivation of the ergodicity conditions and ensures the existence of forecasting distributions in closed form, at any horizon. The last property is of particular interest for applications to finance and economics that investigate the term structure of variables and/or of their nonlinear transforms. The Car class includes a number of time-series models that already exist in the literature, as well as new models introduced in this paper. Their applications are illustrated by examples of portfolio management, term structure and extreme risk analysis.

Improving VWAP strategies: A dynamic volume approach☆

In this paper, we present a new methodology for modelling intraday volume, which allows for a reduction of the execution risk in VWAP (Volume Weighted Average Price) orders. The results are obtained for all the stocks included in the CAC40 index at the beginning of September 2004. The idea of considered models is based on the decomposition of traded volume into two parts: one reflects volume changes due to market evolution; the second describes the stock specific volume pattern. The dynamic of the specific volume part is depicted by ARMA and SETAR models. The implementation of VWAP strategies allows some dynamic adjustments during the day in order to improve tracking of the end-of-day VWAP.

Kernel-based nonlinear canonical analysis and time reversibility

We consider a kernel-based approach to nonlinear canonical correlation analysis and its implementation for time series. We deduce a test procedure of the reversibility hypothesis. The method is applied to the analysis of stochastic differential equation from high-frequency data on stock returns.

Measuring the liquidity part of volume

In this paper, we develop an extended framework of the daily return-volume relationship which incorporates information and liquidity shocks. First, we distinguish between two trading strategies, information-based and liquidity-based trading and suggest that their respective impacts on returns and volume should be modeled differently. Second, we extend the microstructure setting of Grossman and Miller (1988) at the daily frequency in order to model the impact of liquidity frictions on daily trading characteristics. In particular, the model explains how the liquidity frictions can increase the daily traded volume, in the presence of liquidity arbitragers. Finally, based on this structural framework, we extend the econometric model of Tauchen and Pitts (1983) and derive a modified mixture of distribution hypothesis (MDH) model with two latent factors related to information and liquidity. This model allows us to infer the presence of liquidity frictions from daily data. We thus propose a stock-specific liquidity measure using daily return and volume observations of FTSE100 stocks. JEL classification: C51, C52, G12

Intraday transaction price dynamics

High frequency transaction prices exhibit two major characteristics: they are discrete in level and only exist at random transaction dates. In this paper we seek to model transaction price dynamics, taking into account these two features. We specify the transaction price process as a Markov Chain with random transaction dates, and discuss various tools for dynamic analysis like the canonical decomposition, the scale and speed measures. The approach is applied to high frequency data on the stock Elf-Aquitaine traded on the Paris Bourse.

Approximating payoffs and pricing formulas

We use the ideas developed by Madan and Milne (1994. Mathematical Finance 3, 223-245), Lacoste (1996. Mathematical Finance 6, 197-213) to explore the optimality of polynomial approximations in pricing securities. In particular, we look at the approximations for security payoffs as well as the associated pricing formula in a L2 framework. We apply these ideas to two examples, one where the state variable follows an Ornstein-Uhlenbeck process and one based on Brownian motion with reflecting barriers, to illustrate the strengths and weaknesses of the approach.

Survival of Hedge Funds: Frailty vs Contagion

In this paper we examine the dependence between the liquidation risks of individual hedge funds. This dependence can result either from common exogenous shocks (shared frailty), or from contagion phenomena, which occur when an endogenous behaviour of a fund manager impacts the Net Asset Values of other funds. We introduce dynamic models able to distinguish between frailty and contagion phenomena, and test for the presence of such dependence effects, according to the age and management style of the fund. We demonstrate the empirical relevance of our approach by measuring the magnitudes of contagion and exogenous frailty in liquidation risk dependence in the TASS database. The empirical analysis is completed by stress-tests on portfolios of hedge funds.

The Effects of Management and Provision Accounts on Hedge Fund Returns

A characteristic of hedge funds is not only an active portfolio management, but also the allocation of portfolio performance between different accounts, which are the accounts for the external investors, an account for the management firm and a provision account. Despite a lack of transparency in hedge fund market, the strategy of performance allocation is publicly available. This paper shows that these complex performance allocation strategies might explain stylized facts observed in hedge fund returns, such as return persistence, skewed return distribution, bias ratio, or implied increasing risk appetite.

L-Performance with an Application to Hedge Funds

This paper introduces a new fund performance measure, called the L-performance. It is proposed as an alternative to the Sharpe performance measure that is commonly used for fund performance valuation despite its inability to account for skewness and thick tails of fund return distributions. The L-performance improves upon the Sharpe measure in this respect. Technically, the L-performance is based on sample statistics, called L-moments, which are conceptually close to the conventional power moments, but provide more detailed information about the extremes. For this reason, the L-moments are used for prediction and assessment of extreme events, such as floods and earthquakes. In this paper, the new L-performance measure is calculated for a variety of hedge funds and is used to derive a fund ranking.

Robust Portfolio Allocation with Systematic Risk Contribution Restrictions

The standard mean-variance approach can imply extreme weights in some assets in the optimal allocation and a lack of stability of this allocation over time. To improve the robustness of the portfolio allocation, but also to better control for the portfolio turnover and the sensitivity of the portfolio to systematic risk, it is proposed in this paper to introduce additional constraints on both the total systematic risk contribution of the portfolio and its turnover. Our paper extends the existing literature on risk parity in three directions: i) we consider other risk criteria than the variance, such as the Value-at-Risk (VaR), or the Expected Shortfall; ii) we manage separately the systematic and idiosyncratic components of the portfolio risk; iii) we introduce a set of portfolio management approaches which control for the degree of market neutrality of the portfolio, for the strength of the constraint on systematic risk contribution and for the turnover.