Airline Pricing under Different Market Conditions: Evidence from European Low-Cost Carriers

Traditional theories of airline pricing maintain that fares monotonically increase as fewer seats remain available on a flight. A fortiori, this implies a monotonically increasing temporal profile of fares. In this paper, we exploit the presence of drops in offered fares over time as an indicator of an active yield management intervention by two main European Low-Cost Carriers observed daily during the period June 2002 - June 2003. Our results indicate that yield management is effective in raising a flight's load factor. Furthermore, yield management interventions are more intense, and generate a stronger impact, on more competitive routes: one possible interpretation is that a reduction in competitive pressure allows the carriers to adopt a more standardized approach to pricing. Similarly, we find that yield management interventions are more effective in raising the load factor on routes where the customer mix is more heterogenous (i.e., it includes passengers traveling for leisure, business and for family matters). On markets with homogeneous customer base, no robust yield management effect was observed.Easyjet, Intertemporal Pricing, Panel Data, Ryanair, Yield Management

Quantile forecast optimal combination to enhance safety stock estimation

[EN] The safety stock calculation requires a measure of the forecast error uncertainty. Such errors are usually assumed to be Gaussian lid (independently and identically distributed). However, deviations from lid lead to a deterioration in the performance of the supply chain. Recent research has shown that, contrary to theoretical approaches, empirical techniques that do not rely on the aforementioned assumptions can enhance the calculation of safety stocks. In particular, GARCH models cope with time-varying heterocedastic forecast error, and kernel density estimation does not need to rely on a determined distribution. However, if the forecast errors are time-varying heterocedastic and do not follow a determined distribution, the previous approaches are inadequate. We overcome this by proposing an optimal combination of the empirical methods that minimizes the asymmetric piecewise linear loss function, also known as the tick loss. The results show that combining quantile forecasts yields safety stocks with a lower cost. The methodology is illustrated with simulations and real data experiments for different lead times. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.This work was supported by the European Regional Development Fund and the Spanish Government (MINECO/FEDER, UE) under the project with reference DPI2015-64133-R. The authors would like to acknowledge the useful comments and references of three anonymous referees that led to a considerably improved version of the article.Trapero, JR.; Cardós, M.; Kourentzes, N. (2019). Quantile forecast optimal combination to enhance safety stock estimation. International Journal of Forecasting. 35(1):239-250. https://doi.org/10.1016/j.ijforecast.2018.05.009S23925035

Empirical safety stock estimation based on Kernel and GARCH models

Supply chain risk management is drawing the attention of practitioners and academics. A source of risk is demand uncertainty. To deal with it demand forecasting and safety stocks are employed. Most of the work has focused on point demand forecasting, assuming that forecast errors follow the typical normal i.i.d. assumption. The variability of the forecast errors is used to compute the safety stock, in order to reduce the risk of stockouts with a reasonable inventory investment. Nevertheless, real products' demand is very hard to forecast and that means that at minimum the normally i.i.d. assumption should be questioned. This work analyses the effects of possible deviations from these assumptions and it proposes empirical methods based on Kernel density estimators (non-parametric) and GARCH models (parametric) in order to compute the safety stock. The results show that Kernel density estimator is recommended when the forecast errors are fat tailed and GARCH models are well-suited when forecast errors present autocorrelation. Additionally, GARCH models present important improvements for lead time forecast errors, as shown in terms of customer service level, inventory investment and backorders volume. Simulations and real demand data from a manufacturer are used to illustrate our methodology

The real estate risk premium : A developed/emerging country panel data analysis

The objective of this paper is to identify the determinants of office capitalization rates for a panel of 52 countries (developed and emerging countries) between 2000 and 2006. Our assumption, based on Capital Asset Pricing Model, is that the capitalization rate should be at least proportional to the country’s risk perception, as measured by the risk premium on the 10-year government bond yield. Because of the endogeneity of the latter variable, our empirical methodology requires that we estimate first a model explaining the 10-year bond yield. It will be the occasion to discuss the determinants of the risk premium on the bond market. Using a SURE random effect Hausman-Taylor estimator (Hausman & Taylor, 1981), w also take into account the possible correlation between the country risk characteristics on the bond markets and those that determine the real estate market. Our results show that government bond yield is the main determinant of the capitalization rate. We estimate that 1 percentage point increase in the government bond yield will raisse the capitalization rate by about 0.19 percentage point. Real estate variables play also a role, but to a lesser extent. Turning to determinants of the 10-year bond yield, macroeconomic fundamentals are significant determinants of the country risk premium, especially the capacity to honor short-term financial engagements. In addition, the country’s risk history has also very important effect on the investors’ current risk perception.

The real estate risk premium: A developed/emerging country panel data analysis

The objective of this paper is to identify the determinants of office capitalization rates for a panel of 52 countries (developed and emerging countries) between 2000 and 2006. Our assumption, based on a Capital Asset Pricing Model, is that the capitalization rate should be at least proportional to the country’s risk perception, as measured by the risk premium on the 10-year government bond yield. Because of the endogeneity of the latter variable, our empirical methodology requires that we estimate first a model explaining the 10-year bond yield. It will be the occasion to discuss the determinants of the risk premium on the bond market. Using a SURE random effect Hausman-Taylor estimator (Hausman & Taylor, 1981), we also take into account the possible correlation between the country risk characteristics on the bond markets and those that determine the real estate market. Our results show that government bond yield is the main determinant of the capitalization rate. We estimate that a 1 percentage point increase in the government bond yield will raise the capitalization rate by about 0.19 percentage point. Real estate variables play also a role, but to a lesser extent. Turning to determinants of the 10-year bond yield, macroeconomic fundamentals are significant determinants of the country risk premium, especially the capacity to honor short-term financial engagements. In addition, the country’s risk history has also very important effect on the investors’ current risk perception.

A short note on the problematic concept of excess demand in asset pricing models with mean-variance optimization

Referring to asset pricing models where demand is proportional to excess returns and said to be derived from a mean-variance optimization problem, the note formulates what probably is common knowledge but hardly ever made an explicit subject of discussion. This is an insufficient distinction between the desired holding of the risky asset on the part of the speculative agents, which is the solution to the optimization problem and usually directly presented as excess demand, and the desired change in this holding, which is what should reasonably constitute the excess demand on the market. The note arrives at the conclusion that in models with a market maker the story of the maximization of expected wealth should be dropped

Money demand dynamics: some new evidence

Demand for money

The Role of Monetary Aggregates in the Policy Analysis of the Swiss National Bank

Using Swiss data from 1983 to 2008, this paper investigates whether growth rates of the different measures of the quantity of money and or excess money can be used to forecast inflation. After a preliminary data analysis, money demand relations are specified, estimated and tested. Then, employing error correction models, measures of excess money are derived. Using recursive estimates, indicator properties of monetary aggregates for inflation are assessed for the period from 2000 onwards, with time horizons of one, two, and three years. In these calculations, M2 and M3 clearly outperform M1, and excess money is generally a better predictor than the quantity of money. Taking into account also the most (available) recent observations that represent the first three quarters of the economic crisis, the money demand function of M3 remains stable while the one for M2 is strongly influenced by these three observations. While in both cases forecasts for 2010 show inflation rates inside the target zone between zero and two percent, and the same holds for forecasts based on M3 for 2011, forecasts based on M2 provide evidence that the upper limit of this zone might be violated in 2011.stability of money demand, monetary aggregates and inflation