Less of a Puzzle: a new look at the forward forex market

The two-country monetary model is extended to include a consumption externality with habit persistence. The model is simulated using the artificial economy methodology. The 'puzzles' in the forward market are re-examined. The model is able to account for: (a) the low volatility of the forward discount; (b) the higher volatility of expected forward speculative profit; (c) the even higher volatility of the spot return; (d) the persistence in the forward discount; (e) the martingale behavior of spot exchange rates; and (f) the negative covariance between the expected spot return and expected forward speculative profit. It is unable to account for the forward market bias because the volatility of the expected spot return is too large relative to the volatility of the expected forward speculative profit.

Fixed versus Flexible: lessons from EMS order Flow

We extend the literature on regime-dependent volatility in two ways. First, our microstructural model provides a qualitatively new explanation. Second, we test implications of our model using Europe's recent shift to rigidly fixed rates (EMS to EMU). In the model, shocks to order flow induce more volatility under flexible rates because the elasticity of speculative demand is (endogenously) low, leading to pronounced portfolio-balance effects. New data on FF/DM transactions show that order flow had persistent effects on the exchange rate before EMU parities were announced. After announcement, the FF/DM rate was decoupled from order flow, as the model predicts.

Power-law behaviour, heterogeneity, and trend chasing

Long-range dependence in volatility is one of the most prominent examples in financial market research involving universal power laws. Its characterization has recently spurred attempts to provide some explanations of the underlying mechanism. This paper contributes to this recent line of research by analyzing a simple market fraction asset pricing model with two types of traders---fundamentalists who trade on the price deviation from estimated fundamental value and trend followers whose conditional mean and variance of the trend are updated through a geometric learning process. Our analysis shows that agent heterogeneity, risk-adjusted trend chasing through the geometric learning process, and the interplay of noisy fundamental and demand processes and the underlying deterministic dynamics can be the source of power-law distributed fluctuations. In particular, the noisy demand plays an important role in the generation of insignificant autocorrelations (ACs) on returns, while the significant decaying AC patterns of the absolute returns and squared returns are more influenced by the noisy fundamental process. A statistical analysis based on Monte Carlo simulations is conducted to characterize the decay rate. Realistic estimates of the power-law decay indices and the (FI)GARCH parameters are presented.

Innovation persistence: Survey and case-study evidence

Evidence on the persistence of innovation sheds light on the nature of the innovation process and can guide appropriate policy development. This paper examines innovation persistence in Ireland and Northern Ireland using complementary quantitative and case-study approaches. Panel data derived from innovation surveys is used, and suggests very different results to previous analyses of innovation persistence primarily based on patents data. Product and process innovation are found to exhibit strong general persistence but we find no evidence that persistence is stronger among highly active innovators. Our quantitative evidence is most strongly consistent with a process of cumulative accumulation at plant level. Our case-studies highlight a number of factors which can either interrupt or stimulate this process including market volatility, plants’ organisational context and regulatory changes. Notably, however, the balance of influences on product and process innovation persistence differs, with product innovation persistence linked more strongly to strategic factors and process changes more often driven by market pressures.
© 2007 Elsevier B.V. All rights reserved.

Choice and use of standards for dynamic headspace trapping and application to the analysis of the volatiles of baked potato

When attempting to quantify the volatile components of a food isolated by dynamic headspace trapping onto an adsorbent, the analyst has to select the most appropriate compounds to use as standards and at which stage of the analysis to add them. Factors to be borne in mind include the volatility of the standard, the response of the GC detector, and whether to add the standard to the sample or to the adsorbent trap. This chapter considers the issues and describes the application of one chosen method to the quantitation of the volatile components of baked potato.

Volatile and persistent real exchange rates with or without sticky prices

The flexible-price two-country monetary model is extended to include a consumption externality with habit persistence. Two methodologies are employed to explore this model's ability to generate volatile and persistent exchange rates. In the first, actual data is used for the exogenous driving processes. In the second, the model is simulated using estimated forcing processes. The theory, in both cases, is capable of explaining the high volatility and persistence of real and nominal exchange rates as well as the high correlation between real and nominal rates. © 2007 Elsevier B.V. All rights reserved.

Do benchmark African equity indices exhibit the stylized facts?

This paper investigates if benchmark African equity indices exhibit the stylized facts reported for financial time-series returns. The returns distributions of the Africa All-Share, Large, Medium and Small Company Indices were found to be leptokurtotic, had fat-tails, over time experienced volatility clustering and exhibited long memory in volatility. Both the All-Share and Large Company Indices were found to exhibit leverage effects. In contrast, positive shocks had a greater impact on future volatility for the Small Company Index which implies a reverse leverage effect. This finding could reflect a bull/bubble market for small capitalisation stocks in Africa.

The Impact of Engagement Processes on the First Appointment Attendance Rate at a Regional Outpatient Psychological Trauma Service.

The effectiveness of simple measures to increase attendance at first appointments is briefly reviewed. The Family Trauma Centre’s remit and pre-study engagement process are described. The perceived idiosyncratic aspects of inviting people suffering from psychological trauma to attend a clinical service are noted as contributory factors in initially tolerating a high first appointment DNA rate. Three new initial engagement processes are then described and results of their application to 30 referrals in total are presented. The overwhelming finding is that paying close attention to any of the three initial engagement processes significantly increases first appointment attendance. Based on these findings the Centre developed a new initial engagement protocol.. The principle that services should pay more attention to their engagement processes than on the characteristics of their client groups when seeking to reduce first appointment DNA rates is supported.

Uncertainty, learning and growth

The paper extends Blackburn and Galindev's (Economics Letters, Vol. 79 (2003), pp. 417-421) stochastic growth model in which productivity growth entails both external and internal learning behaviour with a constant relative risk aversion utility function and productivity shocks. Consequently, the relationship between long-term growth and short-term volatility depends not only on the relative importance of each learning mechanism but also on a parameter measuring individuals' attitude towards risk.

Genetic Algorithm Search for Predictive Patterns in Multidimensional Time Series

Based on an algorithm for pattern matching in character strings, we implement a pattern matching machine that searches for occurrences of patterns in multidimensional time series. Before the search process takes place, time series are encoded in user-designed alphabets. The patterns, on the other hand, are formulated as regular expressions that are composed of letters from these alphabets and operators. Furthermore, we develop a genetic algorithm to breed patterns that maximize a user-defined fitness function. In an application to financial data, we show that patterns bred to predict high exchange rates volatility in training samples retain statistically significant predictive power in validation samples.

Modeling Multivariate Interest Rates Using Time-Varying Copulas and Reducible Nonlinear Stochastic Differential Equations

We propose a new approach for modeling nonlinear multivariate interest rate processes based on time-varying copulas and reducible stochastic differential equations (SDEs). In the modeling of the marginal processes, we consider a class of nonlinear SDEs that are reducible to Ornstein--Uhlenbeck (OU) process or Cox, Ingersoll, and Ross (1985) (CIR) process. The reducibility is achieved via a nonlinear transformation function. The main advantage of this approach is that these SDEs can account for nonlinear features, observed in short-term interest rate series, while at the same time leading to exact discretization and closed-form likelihood functions. Although a rich set of specifications may be entertained, our exposition focuses on a couple of nonlinear constant elasticity volatility (CEV) processes, denoted as OU-CEV and CIR-CEV, respectively. These two processes encompass a number of existing models that have closed-form likelihood functions. The transition density, the conditional distribution function, and the steady-state density function are derived in closed form as well as the conditional and unconditional moments for both processes. In order to obtain a more flexible functional form over time, we allow the transformation function to be time varying. Results from our study of U.S. and UK short-term interest rates suggest that the new models outperform existing parametric models with closed-form likelihood functions. We also find the time-varying effects in the transformation functions statistically significant. To examine the joint behavior of interest rate series, we propose flexible nonlinear multivariate models by joining univariate nonlinear processes via appropriate copulas. We study the conditional dependence structure of the two rates using Patton (2006a) time-varying symmetrized Joe--Clayton copula. We find evidence of asymmetric dependence between the two rates, and that the level of dependence is positively related to the level of the two rates. (JEL: C13, C32, G12) Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

Towards an understanding of the causes and effects of software requirements change: two case studies

Changes to software requirements not only pose a risk to the successful delivery of software applications but also provide opportunity for improved usability and value. Increased understanding of the causes and consequences of change can support requirements management and also make progress towards the goal of change anticipation. This paper presents the results of two case studies that address objectives arising from that ultimate goal. The first case study evaluated the potential of a change source taxonomy containing the elements ‘market’, ‘organisation’, ‘vision’, ‘specification’, and ‘solution’ to provide a meaningful basis for change classification and measurement. The second case study investigated whether the requirements attributes of novelty, complexity, and dependency correlated with requirements volatility. While insufficiency of data in the first case study precluded an investigation of changes arising due to the change source of ‘market’, for the remainder of the change sources, results indicate a significant difference in cost, value to the customer and management considerations. Findings show that higher cost and value changes arose more often from ‘organisation’ and ‘vision’ sources; these changes also generally involved the co-operation of more stakeholder groups and were considered to be less controllable than changes arising from the ‘specification’ or ‘solution’ sources. Results from the second case study indicate that only ‘requirements dependency’ is consistently correlated with volatility and that changes coming from each change source affect different groups of requirements. We conclude that the taxonomy can provide a meaningful means of change classification, but that a single requirement attribute is insufficient for change prediction. A theoretical causal account of requirements change is drawn from the implications of the combined results of the two case studies.

Why and how FDI stocks are a biased measure of MNE affiliate activity

Many international business (IB) studies have used foreign direct investment (FDI) stocks to measure the aggregate value-adding activity of multinational enterprises (MNE) affiliates in host countries. We argue that FDI stocks are a biased measure of that activity, because the degree to which they overestimate or underestimate affiliate activity varies systematically with host-country characteristics. First, most FDI into countries that serve as tax havens generate no actual productive activity; thus FDI stocks in such countries overestimate affiliate activity. Second, FDI stocks do not include locally raised external funds, funds widely used in countries with well-developed financial markets or volatile exchange rates, resulting in an underestimation of affiliate activity in such countries. Finally, the extent to which FDI translates into affiliate activity increases with affiliate labor productivity, so in countries where labor is more productive, FDI stocks also result in an underestimation of affiliate activity. We test these hypotheses by first regressing affiliate value-added and affiliate sales on FDI stocks to calculate a country-specific mismatch, and then by regressing this mismatch on a host country's tax haven status, level of financial market development, exchange rate volatility, and affiliate labor productivity. All hypotheses are supported, implying that FDI stocks are a biased measure of MNE affiliate activity, and hence that the results of FDI-data-based studies of such activity need to be reconsidered. [ABSTRACT FROM AUTHOR]

Quantitative and qualitative trapping of volatile methylated selenium species entrained through nitric acid

Quantification and speciation of volatile selenium (Se) fluxes in remote areas has not been feasible previously, due to the absence of a simple and easily transportable trapping technique that preserves speciation. This paper presents a chemo-trapping method with nitric acid (HNO3) for volatile Se species, which preserves speciation of trapped compounds. The recovery and speciation of dimethylselenide (DMSe) and dimethyl diselenide (DMDSe) entrained through both concentrated nitric acid and hydrogen peroxide (H2O2) were compared by HPLC-ICP-MS and HPLC-HG-AFS analyses. It was demonstrated that trap reproducibility was better for nitric acid and a recovery of 65.2 +/- 1.9% for DMSe and 81.3 +/- 3.9% for DMDSe was found in nitric acid traps. HPLC-ES-MS identified dimethyl selenoxide (DMSeO) as the trapped product of DMSe. Methylseleninic acid (MSA) was identified to be the single product of DMDSe trapping. These oxidized derivatives have a high stability and low volatility, which makes nitric acid a highly attractive trapping liquid for volatile Se species and enables reconstruction of the speciation of those species. The presented trapping method is simple, quantifiable, reproducible, and robust and can potentially be applied to qualitatively and quantitatively study Se volatilization in a wide range of natural environments.