ABSTRACT This article presents general conditions under which it is possible to obtain asset pricing relations from the intertemporal optimal investment decision of the firm. Under the assumption of linear homogeneous production and adjustment cost functions (the Hayashi (1982) conditions), it is possible to establish, state by state, the equality between the return on investment and the market return of the financial claims issued by the firm. This result proves to be, in essence, robust to the consideration of very general constraints on investment and the inclusion of taxes.

This article presents general conditions under which it is possible to obtain asset pricing relations from the intertemporal optimal investment decision of the firm. Under the assumption of linear homogeneous production and adjustment cost functions, it is possible to establish, state by state, the equality between the return on investment and the market return of the financial claims issued by the firm. This result proves to be, in essence, robust to the consideration of very general constraints on investment and the inclusion of taxes.

This article presents a general framework for identifying and modeling the joint-tail distribution based on multivariate extreme value theories. We argue that the multivariate approach is the most efficient and effective way to study extreme events such as systemic risk and crisis. We show, using returns on five major stock indices, that the use of traditional dependence measures could lead to inaccurate portfolio risk assessment. We explain how the framework proposed here could be exploited in a number of finance applications such as portfolio selection, risk management, Sharpe ratio targeting, hedging, option valuation, and credit risk analysis.

We estimate a general microstructure model of the transitory and permanent impact of order flow on stock prices. Jumps are detected in both the transaction price (observation equation) and fundamental value (state equation). The model’s parameters and variances are updated in real time. Prices can be altered by both the size and direction of trades, and the effects of buy-initiated and sell-initiated trades are different. We estimate this model using tick-by-tick data for 12 large-capitalization stocks traded on the Euronext-Paris Bourse. We find that, at tick frequency, the overnight return, the intraday jumps, and the continuous innovations represent approximately 7%,8.5%, and 36.7% of the total variation of stock returns. The microstructure model explains on average 47.7% of the total variation. Once jumps are filtered and parameters are estimated in real time, we also find that the price impact of trades is symmetric on average. However, the price of highly liquid stocks with a large proportion of sell-initiated orders tends to be more sensitive to buy trades, whereas the price of less liquid stocks with a large proportion of buy-initiated orders tends to be more sensitive to sell trades.

In this paper, we compare various methods that extract a Risk Neutral Density (RND) out of PIBOR interest-rate futures options and we investigate how traders react to a political event. Our benchmark model derives from A. Brace, D. Ga̧tarek, M. Musiela [Mathematical Finance 7 (1997) 127–155]. We also consider a mixture of log-normals (as in W.R. Melik, C.P. Thomas, Journal of Financial and Quantitative Analysis 32 (1997) 91–116), an Hermite expansion (as in P. Abken, D.B. Madan, S. Ramamurtie, Estimation of risk-neutral and statistical densities by Hermite polynomial approximation: with an application to Eurodollar Futures Options, Federal Reserve Bank of Atlanta, 1996), and a method based on Maximum Entropy (according to P. Buchen, M. Kelly, Journal of Financial and Quantitative Analysis 31 (1996) 143–159). We take care of the early exercise feature and we show how to approximate RNDs for a fixed time to maturity. The various methods generate similar RNDs. A daily panel of options running from February 1997 to July 1997 reveals that operators expected the snap election a few days before the official announcement was made and that a substantial amount of political uncertainty subsisted even a month after the elections. Uncertainty evolved with polls forecasts of the future government.

Systemic risk may be defined as the propensity of a financial institution to be undercapitalized when the financial system as a whole is undercapitalized. In this article, we investigate the case of non-US institutions, with several factors explaining the dynamics of financial firms returns and with asynchronicity of time zones. We apply this methodology to the 196 largest European financial firms and estimate their systemic risk over the 2000–12 period. We find that, for certain countries, the cost for the taxpayer to rescue the riskiest domestic banks is so high that some banks might be considered too big to be saved.

WE WARN OF A CLASS of problems that can occur when inverting confluent characteristic functions (CF's). The term confluence is often used in Mathematics in connection with analysis and/or dynamic (difference, differential, and integral) equations; for example, see the classic text by Whittaker and Watson (1927). A confluence (of singularities) is a joint degeneracy that occurs within a function; here, the CF. When one is dealing with the CF of a k-dimensional variate where k > 1, these joint degeneracies can distort the derivation of the marginal density of some lower-dimensional combination of the k components. The distortions are both analytical and numerical. In this note, we first express the distributional problem in the simplest bivariate case, then clarify it with examples from a simple autoregressive (AR) model. Let R, S be two continuous (for simplicity) variates based on a sample of n observations, with joint CF pn(u,v) =E[euR+ivS], i = , and Pr{S > 0} = 1. The joint density h,jr, s) of R and S is expressed by means of the inversion formula as

This article presents a general framework for identifying and modeling the joint-tail distribution based on multivariate extreme value theories. We argue that the multivariate approach is the most efficient and effective way to study extreme events such as systemic risk and crisis. We show, using returns on five major stock indices, that the use of traditional dependence measures could lead to inaccurate portfolio risk assessment. We explain how the framework proposed here could be exploited in a number of finance applications such as portfolio selection, risk management, Sharpe ratio targeting, hedging, option valuation, and credit risk analysis.