Modelling monetary transmission in UK manufacturing industry

This paper studies the transmission of monetary policy to industrial output in the UK. In order to capture asymmetries, a system of threshold equations is considered. However, unlike previous research, endogenous threshold parameters are allowed to be different for each equation. Such an approach may be appealing from an economic point of view and is shown to be of importance after suitable econometric evaluation. Results show evidence of cross-sectional differences across industries and asymmetries in some sectors. These findings contribute to the debate about the importance of alternative economic theories to explain these asymmetries and support the use of a sectorally disaggregated approach to the analysis of monetary transmission.     

Learning by trial and error

A person learns by trial and error if he occasionally tries out new strategies, rejecting choices that are erroneous in the sense that they do not lead to higher payoffs. In a game, however, strategies can become erroneous due to a change of behavior by someone else. We introduce a learning rule in which behavior is conditional on whether a player experiences an error of the first or second type. This rule, called interactive trial and error learning, implements Nash equilibrium behavior in any game with generic payoffs and at least one pure Nash equilibrium.     

Estimation of Common Factors under Cross‐Sectional and Temporal Aggregation Constraints

The paper estimates a large‐scale mixed‐frequency dynamic factor model for the euro area, using monthly series along with gross domestic product (GDP) and its main components, obtained from the quarterly national accounts (NA). The latter define broad measures of real economic activity (such as GDP and its decomposition by expenditure type and by branch of activity) that we are willing to include in the factor model, in order to improve its coverage of the economy and thus the representativeness of the factors. The main problem with their inclusion is not one of model consistency, but rather of data availability and timeliness, as the NA series are quarterly and are available with a large publication lag. Our model is a traditional dynamic factor model formulated at the monthly frequency in terms of the stationary representation of the variables, which however becomes nonlinear when the observational constraints are taken into account. These are of two kinds: nonlinear temporal aggregation constraints, due to the fact that the model is formulated in terms of the unobserved monthly logarithmic changes, but we observe only the sum of the monthly levels within a quarter, and nonlinear cross‐sectional constraints, since GDP and its main components are linked by the NA identities, but the series are expressed in chained volumes. The paper provides an exact treatment of the observational constraints and proposes iterative algorithms for estimating the parameters of the factor model and for signal extraction, thereby producing nowcasts of monthly GDP and its main components, as well as measures of their reliability.     

Multivariate extremes for models with constant conditional correlations

Models with constant conditional correlations are versatile tools for describing the behavior of multivariate time series of financial returns. Mathematically speaking, they are solutions of a special class of stochastic recurrence equations (SRE). The extremal behavior of general solutions of SRE has been studied in detail by Kesten [Kesten, H., 1973. Random difference equations and renewal theory for products of random matrices. Acta Mathematica 131, 207–248] and Perfekt [Perfekt, R., 1997. Extreme value theory for a class of Markov chains with values in ^d. Advances in Applied Probability 29, 138–164]. The central concept to understanding the joint extremal behavior of such multivariate time series is the multivariate regular variation spectral measure. In this paper, we propose an estimator for the spectral measure associated with solutions of SRE and prove its consistency. Our estimator is the tail empirical measure of the multivariate time series. Successful use of the estimator depends on a good choice of k, the number of upper order statistics contributing to the empirical measure. We introduce a new criteria for the choice of k based on a scaling property of the spectral measure. We investigate the performance of our estimation technique on exchange rate time series from HFDF96 data set. The estimated spectral measure is used to calculate probabilities of joint extreme returns and probabilities of large movements in an exchange rate conditional on the occurrence of extreme returns in another exchange rate. We find a high level of dependence between the extreme movements of most of the currencies in the EU. We also investigate the changes in the level of dependence between the extreme returns of pairs of currencies as the sampling frequency decreases. When at least one return is extreme, a strong dependence between the components is present already at the 4-hour level for most of the European currencies.     

Heterogeneities within Industries and Structure-Performance Models

This paper tests whether the results from standard structure-conduct-performance [SCP] models estimated at the industry level are sensitive to the degree of heterogeneity of the firms in the industries. Industries are separated into homogeneous and heterogeneous categories depending on whether the profit rates of firms within an industry converge on a common value or not. In homogeneous industries we find that both the long-run projected returns on assets for the industries and Bureau of Census price-cost-margins are well explained by variables usually included in SCP models, as in particular industry concentration. In contrast, few if any of the usual SCP-model variables are statistically significant in the regressions for heterogeneous industries.     

Lean Trees—A General Approach for Improving Performance of Lattice Models for Option Pricing

The well-known binomial and trinomial tree models for option pricing are examined from the point of view of numerical efficiency. Common lattices use a large part of time resources for calculations which are almost irrelevant for the solution. To avoid this waste of resources, the tree is reduced to a lean form which yields the same order of convergence, but with a reduction of numerical effort. In numerical tests it is shown that the proposed method leads to a significant improvement in real calculation time without loss of accuracy for a broad class of derivatives.     

Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices

In this article different spatial statistics techniques to analyze the behavior of used dwelling market prices are compared. We fit two lattice models: simultaneous and conditional autoregressive, a geostatistical model, the so-called universal kriging and finally, a linear mixed-effect model. Different spatial neighborhood structures are considered, as well as different spatial weight matrices and covariance models. The results are illustrated through a real data set of 293 properties from Pamplona, Spain.     

Exchange rate volatility,sectoral trade,and the aggregation bias

This paper proposes a sectoral theoretical model in an imperfect competition framework, with country-specific and industry-specific original variables, notably factor productivity, scale economies, or product differentiation. It is then empirically estimated in a panel data model, at a sectoral and geographical disaggregation level, to test the impact of exchange rate volatility on G-7 countries' exports. Economies of scale are estimated from a non-linear translog production system. Two exchange rate volatility measurements have been used: the moving sample standard deviation and the GARCH approach. The main finding shows that the impact of exchange rate volatility on exports varies considerably, depending on the industry covered and the export destination markets. As a consequence, there is both a sectoral and geographical aggregation bias when estimating the effects of exchange rate variations. JEL no. F1, F12, F14     

Calibration of Gaussian heath, Jarrow and Morton and random field interest rate term structure models

We show how the recently introduced Gaussian random field interest rate term structure models can be calibrated accurately and quickly to market caps and swaptions prices. We show how the calibrated random field model can be approximated arbitrarily closely with a multi-factor Gaussian Heath, Jarrow and Morton model. We argue that the Gaussian random field model is easier to calibrate and can be used as an indirect way to calibrate multi-factor Gaussian Heath, Jarrow and Morton interest rate models.This work was carried out when the author was at the Financial Options Research Centre, Warwick Business School, University of Warwick. I wish to thank Stewart Hodges for many helpful discussions and comments and Martin Cooper of Tokai Bank, Europe, for supplying the data used in this paper. I also wish to thank the Economic and Social Research Council and FORC for funding. An earlier version of this paper entitled Multi-Factor Gaussian HJM Approximation to Kennedy and Calibration to Caps and Swaptions Prices was presented at the 9th Annual Conference, FORC., Warwick Business School, September 1996. Another version also appears in the author's Ph.D. thesis. I am grateful to the helpful comments provided by Marti Subrahmanyam and the two anonymous referees. All errors are my own and the views expressed in no way reflect the opinion of my employer.     

The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher

The purpose of this study is to demonstrate potential problems associated with the use of bankruptcy prediction models in current research. The tests in this study demonstrate the problems that may arise when bankruptcy prediction models are inappropriately applied. This analysis evaluated the Zmijewski (1984) and Ohlson (1980) models using time periods, industries, and financial distress situations other than those used to originally develop the models. The findings indicated that both models were sensitive to time periods. That is, the accuracy of the models declined when applied to time periods different from those used to develop the models. The findings also suggest that the accuracy of each model continues to decline moving from the 1988–1991 to the 1992–1999 sample period. Additionally, Ohlson's (Zmijewski's) model was (was not) sensitive to industry classifications. The findings of this study also suggest that the Ohlson and Zmijewski models are not sensitive to financial distress situations other than those used to develop the models. Thus, the models appear to be more generally useful for predicting financial distress, not just bankruptcy.In sum, the results of this study suggest that researchers should use bankruptcy prediction models cautiously. Applying the models to time periods and industries other than those used to develop the models may result in a significant decline in the models' accuracies. Additionally, some bankruptcy prediction models may be more appropriate for evaluating various forms of financial distress as opposed to just bankruptcy. To avoid erroneous applications of bankruptcy prediction models in the future, it is necessary for researchers not only to understand the uses of prediction models, but also to understand the limitations of the models.