Persistence of volatility in futures markets

This article examines the characteristics of key measures of volatility for different types of futures contracts to provide a better foundation for modeling volatility behavior and derivative values. Particular attention is focused on analyzing how different measures of volatility affect volatility persistence relationships. Intraday realized measures of volatility are found to be more persistent than daily measures, the type of GARCH procedure used for conditional volatility analysis is critical, and realized volatility persistence is not coherent with conditional volatility persistence. Specifically, although there is a good fit between the realized and conditional volatilities, no coherence exists between their degrees of persistence, a counterintuitive finding that shows realized and conditional volatility measures are not a substitute for one another

Objective detection of sting jets in low-resolution datasets

Sting jets are transient coherent mesoscale strong wind features that can cause damaging surface wind gusts in extratropical cyclones. Currently, we have only limited knowledge of their climatological characteristics. Numerical weather prediction models require enough resolution to represent slantwise motions with horizontal scales of tens of kilometres and vertical scales of just a few hundred metres to represent sting jets. Hence, the climatological characteristics of sting jets and the associated extratropical cyclones can not be determined by searching for sting jets in low-resolution datasets such as reanalyses. A diagnostic is presented and evaluated for the detection in low-resolution datasets of atmospheric regions from which sting jets may originate. Previous studies have shown that conditional symmetric instability (CSI) is present in all storms studied with sting jets, while other, rapidly developing storms of a similar character but no CSI do not develop sting jets. Therefore, we assume that the release of CSI is needed for sting jets to develop. While this instability will not be released in a physically realistic way in low-resolution models (and hence sting jets are unlikely to occur), it is hypothesized that the signature of this instability (combined with other criteria that restrict analysis to moist mid-tropospheric regions in the neighbourhood of a secondary cold front) can be used to identify cyclones in which sting jets occurred in reality. The diagnostic is evaluated, and appropriate parameter thresholds defined, by applying it to three case studies simulated using two resolutions (with CSI-release resolved in only the higher-resolution simulation).

A note on estimating market–based minimum capital risk requirements: a multivariate GARCH approach

Internal risk management models of the kind popularized by J. P. Morgan are now used widely by the world’s most sophisticated financial institutions as a means of measuring risk. Using the returns on three of the most popular futures contracts on the London International Financial Futures Exchange, in this paper we investigate the possibility of using multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models for the calculation of minimum capital risk requirements (MCRRs). We propose a method for the estimation of the value at risk of a portfolio based on a multivariate GARCH model. We find that the consideration of the correlation between the contracts can lead to more accurate, and therefore more appropriate, MCRRs compared with the values obtained from a univariate approach to the problem.

An extreme value theory approach to calculating minimum capital risk requirements

This paper investigates the frequency of extreme events for three LIFFE futures contracts for the calculation of minimum capital risk requirements (MCRRs). We propose a semiparametric approach where the tails are modelled by the Generalized Pareto Distribution and smaller risks are captured by the empirical distribution function. We compare the capital requirements form this approach with those calculated from the unconditional density and from a conditional density - a GARCH(1,1) model. Our primary finding is that both in-sample and for a hold-out sample, our extreme value approach yields superior results than either of the other two models which do not explicitly model the tails of the return distribution. Since the use of these internal models will be permitted under the EC-CAD II, they could be widely adopted in the near future for determining capital adequacies. Hence, close scrutiny of competing models is required to avoid a potentially costly misallocation capital resources while at the same time ensuring the safety of the financial system.

Testing for a unit root in a process exhibiting a structural break in the presence of GARCH errors

This paper considers the effect of GARCH errors on the tests proposed byPerron (1997) for a unit root in the presence of a structural break. We assessthe impact of degeneracy and integratedness of the conditional varianceindividually and find that, apart from in the limit, the testing procedure isinsensitive to the degree of degeneracy but does exhibit an increasingover-sizing as the process becomes more integrated. When we consider the GARCHspecifications that we are likely to encounter in empirical research, we findthat the Perron tests are reasonably robust to the presence of GARCH and donot suffer from severe over-or under-rejection of a correct null hypothesis.

Early warning with calibrated and sharper probabilistic forecasts

Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In order to enhance the quality of the density forecasts, one can mix them with the unconditional density. This paper examines the value of combining conditional density forecasts with the unconditional density. The findings have positive implications for issuing early warnings in different disciplines including economics and meteorology, but UK inflation forecasts are considered as an example.

Sub-irrigation of Petunia: benefits in dry summers

With the increasing frequency and magnitude of warmer days during the summer in the UK, bedding plants which were a traditional part of the urban green landscape are perceived as unsustainable and water-demanding. During recent summers when bans on irrigation have been imposed, use and sales of bedding plants have dropped dramatically having a negative financial impact on the nursery industry. Retaining bedding species as a feature in public and even private spaces in future may be conditional on them being managed in a manner that minimises their water use. Using Petunia x hybrida ‘Hurrah White’ we aimed to discover which irrigation approach was the most efficient for maintaining plants’ ornamental quality (flower numbers, size and longevity), shoot and root growth under water deficit and periods of complete water withdrawal. Plants were grown from plugs for 51 days in wooden rhizotrons (0.35 m (h) x 0.1 m (w) x 0.065 m (d)); the rhizotrons’ front comprised clear Perspex which enabled us to monitor root growth closely. Irrigation treatments were: 1. watering with the amount which constitutes 50% of container capacity by conventional surface drip-irrigation (‘50% TOP’); 2. 50% as sub-irrigation at 10 cm depth (‘50% SUB’); 3. ‘split’ irrigation: 25% as surface drip- and 25% as sub-irrigation at 15 cm depth (‘25/25 SPLIT’); 4. 25% as conventional surface drip-irrigation (‘25% TOP’). Plants were irrigated daily at 18:00 apart from days 34-36 (inclusive) when water was withdrawn for all the treatments. Plants in ‘50% SUB’ had the most flowers and their size was comparable to that of ‘50% TOP’. Differences between treatments in other ‘quality’ parameters (height, shoot number) were biologically small. There was less root growth at deeper soil surface levels for ‘50% TOP’ which indicated that irrigation methods like ‘50% SUB’ and ‘25/25 SPLIT’ and stronger water deficits encouraged deeper root growth. It is suggested that sub-irrigation at 10 cm depth with water amounts of 50% container capacity would result in the most root growth with the maximum flowering for Petunia. Leaf stomatal conductance appeared to be most sensitive to the changes in substrate moisture content in the deepest part of the soil profile, where most roots were situated.

Separating skill from luck in REIT mutual funds

This study uses a bootstrap methodology to explicitly distinguish between skill and luck for 80 Real Estate Investment Trust Mutual Funds in the period January 1995 to May 2008. The methodology successfully captures non-normality in the idiosyncratic risk of the funds. Using unconditional, beta conditional and alpha-beta conditional estimation models, the results indicate that all but one fund demonstrates poor skill. Tests of robustness show that this finding is largely invariant to REIT market conditions and maturity.

Dynamic correlations across REIT sub-sectors

The issue of whether Real Estate Investment Trusts should pursue a focused or diversified investment strategy remains an ongoing debate within both the academic and industry communities. This paper considers the relationship between REITs focused on different property sectors in a GARCH-DCC framework. The daily conditional correlations reveal that since 1990 there has been a marked upward trend in the coefficients between US REIT sub-sectors. The findings imply that REITs are behaving in a far more homogeneous manner than in the past. Furthermore, the argument that REITs should be focused in order that investors can make the diversification decision is reduced.

Who benefits from reducing the cost of formality? Quantile regression discontinuity analysis

This paper studies the effects of increasing formality via tax reduction and simplification schemes on micro-firm performance. It uses the 1997 Brazilian SIMPLES program. We develop a simple theoretical model to show that SIMPLES has an impact only on a segment of the micro-firm population, for which the effect of formality on firm performance can be identified, and that can be analyzed along the single dimensional quantiles of the conditional firm revenues. To estimate the effect of formality, we use an econometric approach that compares eligible and non-eligible firms, born before and after SIMPLES in a local interval about the introduction of SIMPLES. We use an estimator that combines both quantile regression and the regression discontinuity identification strategy. The empirical results corroborate the positive effect of formality on microfirms' performance and produce a clear characterization of who benefits from these programs.

PDM 2006: Proceedings of the International Workshop on Data Mining

Recently major processor manufacturers have announced a dramatic shift in their paradigm to increase computing power over the coming years. Instead of focusing on faster clock speeds and more powerful single core CPUs, the trend clearly goes towards multi core systems. This will also result in a paradigm shift for the development of algorithms for computationally expensive tasks, such as data mining applications. Obviously, work on parallel algorithms is not new per se but concentrated efforts in the many application domains are still missing. Multi-core systems, but also clusters of workstations and even large-scale distributed computing infrastructures provide new opportunities and pose new challenges for the design of parallel and distributed algorithms. Since data mining and machine learning systems rely on high performance computing systems, research on the corresponding algorithms must be on the forefront of parallel algorithm research in order to keep pushing data mining and machine learning applications to be more powerful and, especially for the former, interactive. To bring together researchers and practitioners working in this exciting field, a workshop on parallel data mining was organized as part of PKDD/ECML 2006 (Berlin, Germany). The six contributions selected for the program describe various aspects of data mining and machine learning approaches featuring low to high degrees of parallelism: The first contribution focuses the classic problem of distributed association rule mining and focuses on communication efficiency to improve the state of the art. After this a parallelization technique for speeding up decision tree construction by means of thread-level parallelism for shared memory systems is presented. The next paper discusses the design of a parallel approach for dis- tributed memory systems of the frequent subgraphs mining problem. This approach is based on a hierarchical communication topology to solve issues related to multi-domain computational envi- ronments. The forth paper describes the combined use and the customization of software packages to facilitate a top down parallelism in the tuning of Support Vector Machines (SVM) and the next contribution presents an interesting idea concerning parallel training of Conditional Random Fields (CRFs) and motivates their use in labeling sequential data. The last contribution finally focuses on very efficient feature selection. It describes a parallel algorithm for feature selection from random subsets. Selecting the papers included in this volume would not have been possible without the help of an international Program Committee that has provided detailed reviews for each paper. We would like to also thank Matthew Otey who helped with publicity for the workshop.

Dynamic correlations between REIT sub-sectors and the implications for diversification

The issue of whether Real Estate Investment Trusts (REITs) should pursue a focused or diversified investment strategy remains an ongoing debate within both the academic and industry communities. This article considers the relationship between REITs focused on different property sectors in a Generalized Autoregressive Conditional Heteroscedasticity-Dynamic Control Correlation (GARCH-DCC) framework. The daily conditional correlations reveal that since 1990 there has been a marked upward trend in the coefficients between US REIT sub-sectors. The findings imply that REITs are behaving in a far more homogeneous manner than in the past. Furthermore, the argument that REITs should be focused in order that investors can make the diversification decision is reduced.

Validation of three new measure-correlate-predict models for the long-term prospection of the wind resource

The estimation of the long-term wind resource at a prospective site based on a relatively short on-site measurement campaign is an indispensable task in the development of a commercial wind farm. The typical industry approach is based on the measure-correlate-predict �MCP� method where a relational model between the site wind velocity data and the data obtained from a suitable reference site is built from concurrent records. In a subsequent step, a long-term prediction for the prospective site is obtained from a combination of the relational model and the historic reference data. In the present paper, a systematic study is presented where three new MCP models, together with two published reference models �a simple linear regression and the variance ratio method�, have been evaluated based on concurrent synthetic wind speed time series for two sites, simulating the prospective and the reference site. The synthetic method has the advantage of generating time series with the desired statistical properties, including Weibull scale and shape factors, required to evaluate the five methods under all plausible conditions. In this work, first a systematic discussion of the statistical fundamentals behind MCP methods is provided and three new models, one based on a nonlinear regression and two �termed kernel methods� derived from the use of conditional probability density functions, are proposed. All models are evaluated by using five metrics under a wide range of values of the correlation coefficient, the Weibull scale, and the Weibull shape factor. Only one of all models, a kernel method based on bivariate Weibull probability functions, is capable of accurately predicting all performance metrics studied.

Color adjectives and radical contextualism

Radical contextualists have observed that the content of what is said by the utterance of a sentence is shaped in far-reaching ways by the context of utterance. And they have argued that the ways in which the content of what is said is shaped by context cannot be explained by semantic theory. A striking number of the examples that radical contextualists use to support their view involve sentences containing color adjectives (“red”, “green”, etc.). In this paper, I show how the most sophisticated analysis of color adjectives within the explanatory framework of compositional truth conditional semantics—recently developed by Kennedy and McNally (Synthese 174(1):79–98 2010)—needs to be modified to handle the full range of contextual variation displayed by color adjectives.

Subjective well-being and relative poverty in rural Bangladesh

This paper revisits the debate over the importance of absolute vs. relative income as a correlate of subjective well-being using data from Bangladesh, one of the poorest countries in the world with high levels of corruption and poor governance. We do so by combining household data with population census and village survey records. Our results show that conditional on own household income, respondents report higher satisfaction levels when they experience an increase in their income over the past years. More importantly, individuals who report their income to be lower than their neighbours in the village also report less satisfaction with life. At the same time, our evidence suggests that relative wealth effect is stronger for the rich. Similarly, in villages with higher inequality, individuals report less satisfaction with life. However, when compared to the effect of absolute income, these effects (i.e. relative income and local inequality) are modest. Amongst other factors, we study the influence of institutional quality. Institutional quality, measured in terms of confidence in police, matters for well-being: it enters with a positive and significant coefficient in the well-being function.