Monitoring and forecasting annual public deficit every month: the case of France

In this paper we forecast annual budget deficits using monthly information. Using French monthly data on central government revenues and expenditures, the method we propose consists of: (1) estimating monthly ARIMA models for all items of central government revenues and expenditures; (2) inferring the annual ARIMA models from the monthly models; (3) using the inferred annual ARIMA models to perform one-step-ahead forecasts for each item; (4) compounding the annual forecasts of all revenues and expenditures to obtain an annual budget deficit forecast. The major empirical benefit of this technique is that as soon as new monthly data become available, annual deficit forecasts are updated. This allows us to detect in advance possible slippages in central government finances. For years 2002–2004, forecasts obtained following the proposed approach are compared with a benchmark method and with official predictions published by the French government. An evaluation of their relative performance is provided.      

Conditional correlations in the returns on oil companies stock prices and their determinants

The identification of the forces that drive stock returns and the dynamics of their associated volatilities is a major concern in empirical economics and finance. This analysis is extremely important for determining optimal hedging strategies. This paper investigates the stock prices’ returns and their financial risk factors for several integrated oil companies, namely Bp (BP), Chevron-Texaco (CVX), Eni (ENI), Exxon-Mobil (XOM), Royal Dutch (RD) and Total-Fina Elf (TFE). We measure the actual co-risk in stock returns and their determinants “within” and “between” the different oil companies, using multivariate cointegration techniques in modelling the conditional mean, as well as multivariate GARCH models for the conditional variances. The distinguishing features of this paper are: (i) focus on the determinants of the market value of each company using the cointegrated VAR/VECM methodology; (ii) specification of the conditional variances of VECM residuals with the Constant Conditional Correlation (CCC) multivariate GARCH model of Bollerslev [(1990) Review of Economics and Statistics 72:498–505] and the Dynamic Conditional Correlation (DCC) multivariate GARCH model of Engle [(2002) Journal of Business and Economic Statistics 20:339–350]; (iii) discussion of the performance of optimal hedge ratios calculated with the DCC estimates. The “within” and “between” DCC indicate time-varying interdependence between stock return volatilities and their determinants. Moreover, DCC models are shown to produce more accurate hedging strategies.     

Moderating the impact of global sourcing on inventories through supply chain management

In recent years, companies have paid growing attention to supply chain management at a global level. With regard to the upstream part of the supply chain, the need for better suppliers, the research into specific competences and concerns related to international competition have forced companies to improve their ability to cope with suppliers located in different countries around the world. The literature suggests that the geographical distance of suppliers should create higher inventory levels primarily because of longer and more uncertain lead times. However, as this paper aims to demonstrate, companies can limit this effect by means of specific investments in the supply chain and in their relationships with suppliers. The empirical analysis is based on data from the last edition of the International Manufacturing Strategy Survey (IMSS). The results show that companies performing global sourcing have invested in supply chain management (SCM) and that this has been helpful in keeping their inventories under control.