The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

5th International Probabilistic Workshop: 28-29 November 2007, Ghent, Belgium

These are the proceedings of the 5th International Probabilistic Workshop. Even though the 5th anniversary of a conference might not be of such importance, it is quite interesting to note the development of this probabilistic conference. Originally, the series started as the 1st and 2nd Dresdner Probabilistic Symposium, which were launched to present research and applications mainly dealt with at Dresden University of Technology. Since then, the conference has grown to an internationally recognised conference dealing with research on and applications of probabilistic techniques, mainly in the field of structural engineering. Other topics have also been dealt with such as ship safety and natural hazards. Whereas the first conferences in Dresden included about 12 presentations each, the conference in Ghent has attracted nearly 30 presentations. Moving from Dresden to Vienna (University of Natural Resources and Applied Life Sciences) to Berlin (Federal Institute for Material Research and Testing) and then finally to Ghent, the conference has constantly evolved towards a truly international level. This can be seen by the language used. The first two conferences were entirely in the German language. During the conference in Berlin however, the change from the German to English language was especially apparent as some presentations were conducted in German and others in English. Now in Ghent all papers will be presented in English. Participants now, not only come from Europe, but also from other continents. Although the conference will move back to Germany again next year (2008) in Darmstadt, the international concept will remain, since so much work in the field of probabilistic safety evaluations is carried out internationally. In two years (2009) the conference will move to Delft, The Netherlands and probably in 2010 the conference will be held in Szczecin, Poland. Coming back to the present: the editors wish all participants a successful conference in Ghent

Introducing system-based spatial electricity load forecasting

The main motivation of this research is to help reduce the Green House Gases (GHG) emissions of the electricity sector, and counteract the effects on nature and people. Traditional methods of power planning are not optimised to achieve this, and only consider Capital Expenditure (Capex) and Operational Expenditure (Opex) reduction as their main objectives. Minimising GHG emissions is now an additional objective of power planning. One way of achieving this is by optimising the distance of generators to the loads to reduce the transmission losses, and also by harnessing the available regional sources of renewable energies and increasing their integration in the network. Efficient load forecasting methods, capable of describing the regional behaviours of the electricity consumption are developed in this research, and can provide priceless input to electricity planners. Such forecasting methods, known as spatial forecasting, can be used to extract short-term and medium-term information of the electricity consumption of different regions. This work also provides tools for making decisions about the most accurate way of pre-processing consumption data and choosing the most efficient forecasting procedure. Chapter 1 talks about emissions of GHGs and their adverse effect on the nature. It introduces electricity sector as one of the major contributors of human made GHG emissions. It then describes the components of electrical power network and the planning of it. Finally the chapter concludes that an efficient spatial load forecasting method is required to help with spatial planning of power networks. The spatial planning can include more regional components like proximity of generation components to consumers, or the levels of harnessed renewable energy in each area. In such an approach, GHG reduction can be also considered along with Capex and Opex minimisation to plan the future of power networks. Chapter 2 provides definitions on power network components and the load forecasting methods. It starts with definition of power systems and explanation on how electrical energy is superior to all other forms of energy from end user point of view. Electricity generation systems and the sources of energy to produce electricity are described next. Typical generation unit sizes in MW, continuity of the supply, and also its predictability are summarised in a table at the end of this section. Thereafter, transmission lines and distribution systems are described, as other component of electrical power networks. Importance of having an accurate forecast of electricity demand and the common ways to do it are presented next. At the end of this chapter, the deficiencies of current forecasting methods are highlighted and one major goal is defined for this work. It is to overcome the deficiencies of individual forecasting methods by combining them and using them only where it performs efficient. It also mentions that the work is going to closely look at the behaviour of input data to the forecasting method to seek better methods for preparing them. Chapter 3 describes South West Interconnected System (SWIS) as the case study for this work. The reasons for selecting SWIS as the case study are mentioned, followed by a quick history of it and how it has been expanded over the last hundred years. To be able to complete spatial forecasting, the area under study needs to be divided into regions. SWIS is then divided into eight regions for this purpose. A visual presentation of the eight regions on the map is presented at the end of this chapter for more clarity. Chapter 4 performs a short forecasting method on one of the SWIS regions. The selected region is called Metro East. Metro East region is mainly composed of residential consumers. Unlike commercial and industrial consumers, the residential ones are not following a working schedule. That's why it makes them to behave differently and more randomly comparing to the other two. This means more complicated demand to forecast. This is the main reason that Metro East is selected to be studied on this chapter. One of the main components of this chapter is to introduce the methods that have been used for pre-processing of input data. The pre-processing stages include data resolution adjustment, replacement of missing data, removing outliers, clustering and signal reconstruction. A well pre-processed set of data is critical component of any forecasting strategy. The second component of chapter 4 is to generate one day ahead and seven day ahead forecasts of Metro East electricity consumption, using three different training methods. The forecasted results are comparable to other studies done on short term load forecasting. However the author questions the accuracy of classic approach of load forecasting. Classic approach is basically what have been done in the field of load forecasting for decades, which is very similar to the works done in chapter 4. In classic approach, a method gets tested on a case study with an acceptable level of accuracy. Then that method gets introduced as a very accurate tool to be applied on demand forecasting purposes. This work is showing that such accurate method cannot be accurate at all when being applied to other different case studies. Future chapters study this in further details, and come up with some guidelines on how to have accurate load forecast based on the nature of the case study in hand. Chapter 5 applies the methods of load forecasting developed in chapter 4 onto eight different case studies. By doing this, it can be seen that there is no single method of forecasting that can be accurate for all case studies out there. Temperature sensitivity and distribution of the load data of all the regions is closely studied for fifteen years of data. A load type determination criterion is presented in Table 5. By using this table, and preparing Rayleigh, Generalised Pareto, and Generalised Extreme Value distributions of the load data under study, anyone will be able to say whether their load under study is mainly commercial, residential or industrial. The outdoor temperature is one of the main inputs of short term electricity forecasting. Same chapter shows that residential loads are having a greater temperature sensitivity comparing to the other two. The results of one day and seven day ahead forecasts of the eight regions are presented at the end of chapter 5, using two methods of neural networks and decision trees. The results suggest that the two methods need to be used alternatively based on the characteristics of the case study and ambient temperature to achieve the best result. Chapter 6 explains the system based medium term load forecasting. The approach to medium term forecasting is completely different to the one developed for the short term one. Two main differences between Short-Term Load Forecasting (STLF) and Medium-Term Load Forecasting (MTLF) are the availability of weather data and the forecasting objectives. Because of the nature of the weather, temperature forecasts of a year ahead are completely impossible. Also in medium term load forecasting the focus of planners is mainly on peak load and energy consumption forecasts. The forecasting method presented in this chapter is achieved by superimposing annual trend, annual seasonality and forecasted residuals by neural networks and decision trees. Similar to chapter 5, the forecasting strategy is applied to eight different case studies for comparison. It is concluded that based on the case under study, the accuracy of the methods changes. It also provides some advices on the best practices to perform medium load forecasting, considering the characteristics of the load. For instance, it conclude that for industrial regions regression trees performs better than neural network based methods. The same applies to CBD region where commercial load dominates. For some residential areas neural networks behave better. This is because of higher nonlinearity of residential load. The major contributions of this work can be summarised as below: - The topic of the study, i.e. spatial load forecasting and the potential of using it in efficient power planning, is relatively a new topic in the electricity market literature. Moreover, many of the known spatial load forecasting methods have not yet been widely used because of the size, variety, and availability of the data required. The methodology proposed in this study can successfully be applied to spatial forecasting. - While conventional methods are useful for short-term predictions with acceptable accuracy, they fail when medium-to-long term load forecasting is dealt with. The methodology conceived and implemented in this thesis is significantly better than those known as state-of-the-art and can give very satisfactory results for medium-term predictions. - The load analysis criterion, particularly using Q-Q (Quantile vs. Quantile) plots is a unique and original finding of this work. While Q-Q plots are largely used in traditional statistics to compare two samples of data, it has never been applied before for electricity load forecasting purposes. Based on its definition and use, an electricity planner can understand which part of the load is the dominating factor (i.e. whether it is residential, commercial or industrial). And then, based on this, he/she can decide how to go ahead with choosing the most effective forecasting method. Based on this, the thesis provides a very useful criterion for decision making in the energy market. - One of the major findings of the thesis is that there is no one optimum way of forecasting electricity load in different scenarios. The results presented in the thesis have shown that a method that can accurately forecast the load on a system (3% error for a year ahead) can perform completely different in forecasting another system (observed errors of around 14%). This study demonstrates that a method which is claimed to have a given accuracy can be considerably inaccurate when applied on a different case study. - Using an ambient temperature-based criterion (i.e. the average maximum temperature of the month) to choose the correct forecasting method is another major finding of the study. In fact, the author has demonstrated that for a temperature sensitive load, different forecasting methods should be used and then combined to get the most accurate result

Accelerating inference in cosmology and seismology with generative models

Statistical analyses in many physical sciences require running simulations of the system that is being examined. Such simulations provide complementary information to the theoretical analytic models, and represent an invaluable tool to investigate the dynamics of complex systems. However, running simulations is often computationally expensive, and the high number of required mocks to obtain sufficient statistical precision often makes the problem intractable. In recent years, machine learning has emerged as a possible solution to speed up the generation of scientific simulations. Machine learning generative models usually rely on iteratively feeding some true simulations to the algorithm, until it learns the important common features and is capable of producing accurate simulations in a fraction of the time. In this thesis, advanced machine learning algorithms are explored and applied to the challenge of accelerating physical simulations. Various techniques are applied to problems in cosmology and seismology, showing benefits and limitations of such an approach through a critical analysis. The algorithms are applied to compelling problems in the fields, including surrogate models for the seismic wave equation, the emulation of cosmological summary statistics, and the fast generation of large simulations of the Universe. These problems are formulated within a relevant statistical framework, and tied to real data analysis pipelines. In the conclusions, a critical overview of the results is provided, together with an outlook over possible future expansions of the work presented in the thesis

Modelling empirical features and liquidity resilience in the Limit Order Book

The contribution of this body of work is in developing new methods for modelling interactions in modern financial markets and understanding the origins of pervasive features of trading data. The advent of electronic trading and the improvement in trading technology has brought about vast changes in individual trading behaviours, and thus in the overall dynamics of trading interactions. The increased sophistication of market venues has led to the diminishing of the role of specialists in making markets, a more direct interaction between trading parties and the emergence of the Limit Order Book (LOB) as the pre-eminent trading system. However, this has also been accompanied by an increased fluctuation in the liquidity available for immediate execution, as market makers try to balance the provision of liquidity against the probability of an adverse price move, with liquidity traders being increasingly aware of this and searching for the optimal placement strategy to reduce execution costs. The varying intra-day liquidity levels in the LOB are one of the main issues examined here. The thesis proposes a new measure for the resilience of liquidity, based on the duration of intra-day liquidity droughts. The flexible survival regression framework employed can accommodate any liquidity measure and any threshold liquidity level of choice to model these durations, and relate them to covariates summarising the state of the LOB. Of these covariates, the frequency of the droughts and the value of the liquidity measure are found to have substantial power in explaining the variation in the new resilience metric. We have shown that the model also has substantial predictive power for the duration of these liquidity droughts, and could thus be of use in estimating the time between subsequent tranches of a large order in an optimal execution setting. A number of recent studies have uncovered a commonality in liquidity that extends across markets and across countries. We outline the implications of using the PCA regression approaches that have been employed in recent studies through synthetic examples, and demonstrate that using such an approach for the study of European stocks can mislead regarding the level of liquidity commonality. We also propose a method via which to measure commonality in liquidity resilience, using an extension of the resilience metric identified earlier. This involves the first use of functional data analysis in this setting, as a way of summarising resilience data, as well as measuring commonality via functional principal components analysis regression. Trading interactions are considered using a form of agent-based modelling in the LOB, where the activity is assumed to arise from the interaction of liquidity providers, liquidity demanders and noise traders. The highly detailed nature of the model entails that one can quantify the dependence between order arrival rates at different prices, as well as market orders and cancellations. In this context, we demonstrate the value of indirect inference and simulation-based estimation methods (multi-objective optimisation in particular) for models for which direct estimation through maximum likelihood is difficult (for example, when the likelihood cannot be obtained in closed form). Besides being a novel contribution to the area of agent-based modelling, we demonstrate how the model can be used in a regulation setting, to quantify the effect of the introduction of new financial regulation

Signal sampling and processing in magnetic resonance applications

In this thesis, signal sampling and processing techniques are developed for magnetic resonance applications, to improve the estimation of magnetic resonance parameters and to reduce experimental acquisition times. Two processing techniques are developed for Nuclear Magnetic Resonance (NMR) relaxation and diffusion experiments. $L_1$ regularization is recommended for extracting parameter distributions which are a priori known to be composed of sparse features. $L_1$ regularization is shown to be stable at signal-to-noise ratios < 20 and capable of resolving relaxation time constants and diffusion coefficients which differ by as little as 10%, such as in relaxation and diffusion studies of hexane/dodecane in porous media. Modified Total Generalized Variation (MTGV) regularization is recommended for extracting parameter distributions for which there is no prior knowledge of whether they are composed of sparse or smooth features. MTGV regularization is shown to perform better than conventional processing techniques and $L_1$ regularization over a range of simulated distributions. A method for optimising sampling patterns for relaxation and diffusion experiments, based on the Cramér-Rao Lower Bound theory, is presented. The method is validated against pulsed field gradient NMR diffusion data of two experimental systems. In the first experimental system, the sampling pattern is optimised for the most accurate estimation of the lognormal distribution parameters of an emulsion droplet size distribution of toluene in water. In the second experimental system, the sampling pattern is optimised for the most accurate estimation of the bi-exponential model parameters of a binary mixture of methane/ethane adsorbed in a zeolite. The proposed method predicts an uncertainty in estimating the model parameters which is < 10% different from the uncertainty estimated from the experimental data sampled using the same sampling pattern. Signal sampling and processing techniques are subsequently combined to reduce experimental acquisition times, which opens opportunities for studying unsteady systems over a long acquisition time and investigating fast-changing phenomena. A 32-fold decrease in the experimental acquisition time is achieved in extracting 3D spatially resolved spin spin relaxation maps. This is expected to be useful in investigating porous media systems. Three-component velocity maps on a 2D image, acquired every 4 ms, are used to capture, for the first time, the hydrodynamics of a bubble burst event. The experimental data are used to validate the predictions of numerical works.Gates Cambridge Scholarshi

Bayesian inference for challenging scientific models

Advances in technology and computation have led to ever more complicated scientific models of phenomena across a wide variety of fields. Many of these models present challenges for Bayesian inference, as a result of computationally intensive likelihoods, high-dimensional parameter spaces or large dataset sizes. In this thesis we show how we can apply developments in probabilistic machine learning and statistics to do inference with examples of these types of models. As a demonstration of an applied inference problem involving a non-trivial likelihood computation, we show how a combination of optimisation and MCMC methods along with careful consideration of priors can be used to infer the parameters of an ODE model of the cardiac action potential. We then consider the problem of pileup, a phenomenon that occurs in astronomy when using CCD detectors to observe bright sources. It complicates the fitting of even simple spectral models by introducing an observation model with a large number of continuous and discrete latent variables that scales with the size of the dataset. We develop an MCMC-based method that can work in the presence of pileup by explicitly marginalising out discrete variables and using adaptive HMC on the remaining continuous variables. We show with synthetic experiments that it allows us to fit spectral models in the presence of pileup without biasing the results. We also compare it to neural Simulation- Based Inference approaches, and find that they perform comparably to the MCMC-based approach whilst being able to scale to larger datasets. As an example of a problem where we wish to do inference with extremely large datasets, we consider the Extreme Deconvolution method. The method fits a probability density to a dataset where each observation has Gaussian noise added with a known sample-specific covariance, originally intended for use with astronomical datasets. The existing fitting method is batch EM, which would not normally be applied to large datasets such as the Gaia catalog containing noisy observations of a billion stars. In this thesis we propose two minibatch variants of extreme deconvolution, based on an online variation of the EM algorithm, and direct gradient-based optimisation of the log-likelihood, both of which can run on GPUs. We demonstrate that these methods provide faster fitting, whilst being able to scale to much larger models for use with larger datasets. We then extend the extreme deconvolution approach to work with non- Gaussian noise, and to use more flexible density estimators such as normalizing flows. Since both adjustments lead to an intractable likelihood, we resort to amortized variational inference in order to fit them. We show that for some datasets that flows can outperform Gaussian mixtures for extreme deconvolution, and that fitting with non-Gaussian noise is now possible