Stochastic modelling of financial time series with memory and multifractal scaling

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

Numerical simulation of the Riesz fractional diffusion equation with a nonlinear source term

In this paper, A Riesz fractional diffusion equation with a nonlinear source term (RFDE-NST) is considered. This equation is commonly used to model the growth and spreading of biological species. According to the equivalent of the Riemann-Liouville(R-L) and Gr¨unwald-Letnikov(GL) fractional derivative definitions, an implicit difference approximation (IFDA) for the RFDE-NST is derived. We prove the IFDA is unconditionally stable and convergent. In order to evaluate the efficiency of the IFDA, a comparison with a fractional method of lines (FMOL) is used. Finally, two numerical examples are presented to show that the numerical results are in good agreement with our theoretical analysis.

Fundamental solution and discrete random walk model for time-space fractional diffusion equation

In this paper, we consider a time-space fractional diffusion equation of distributed order (TSFDEDO). The TSFDEDO is obtained from the standard advection-dispersion equation by replacing the first-order time derivative by the Caputo fractional derivative of order α∈(0,1], the first-order and second-order space derivatives by the Riesz fractional derivatives of orders β 1∈(0,1) and β 2∈(1,2], respectively. We derive the fundamental solution for the TSFDEDO with an initial condition (TSFDEDO-IC). The fundamental solution can be interpreted as a spatial probability density function evolving in time. We also investigate a discrete random walk model based on an explicit finite difference approximation for the TSFDEDO-IC.

Land use at privatized Australian airports : classification and analyses

In recent years the air transport industry has experienced unprecedented growth, driven by strong local and global economies. Whether this growth can continue in the face of anticipated oil crises; international economic forecasts and recent influenza outbreaks is yet to be seen. One thing is certain, airport owners and operators will continue to be faced with challenging environments in which to do business. In response, many airports recognize the value in diversifying their revenue streams through a variety of landside property developments within the airport boundary. In Australia it is the type and intended market of this development that is a point of contention between private airport corporations and their surrounding municipalities. The aim of this preliminary research is to identify and categorize on-airport development occurring at the twenty-two privatized Australian airports which are administered under the Airports Act [1996]. This new knowledge will assist airport and municipal planners in understanding the current extent and category of on-airport land use, allowing them to make better decisions when proposing development both within airport master plans and beyond the airport boundary in local town and municipal plans.

Context-Based Modelling of Interferometric Signals for the Assessment of Tear-Film Suface Quality

Modelling of interferometric signals related to tear film surface quality is considered. In the context of tear film surface quality estimation in normal healthy eyes, two clinical parameters are of interest: the build-up time, and the average interblink surface quality. The former is closely related to the signal derivative while the latter to the signal itself. Polynomial signal models, chosen for a particular set of noisy interferometric measurements, can be optimally selected, in some sense, with a range of information criteria such as AIC, MDL, Cp, and CME. Those criteria, however, do not always guarantee that the true derivative of the signal is accurately represented and they often overestimate it. Here, a practical method for judicious selection of model order in a polynomial fitting to a signal is proposed so that the derivative of the signal is adequately represented. The paper highlights the importance of context-based signal modelling in model order selection.

Combining electrospun scaffolds with electrosprayed hydrogels leads to three-dimensional cellularization of hybrid constructs

A common problem in the design of tissue engineered scaffolds using electrospun scaffolds is the poor cellular infiltration into the structure. To tackle this issue, three approaches to scaffold design using electrospinning were investigated: selective leaching of a water-soluble fiber phase (poly ethylene oxide (PEO) or gelatin), the use of micron-sized fibers as the scaffold, and a combination of micron-sized fibers with codeposition of a hyaluronic acid-derivative hydrogel, Heprasil. These designs were achieved by modifying a conventional electrospinning system with two charged capillaries and a rotating mandrel collector. Three types of scaffolds were fabricated: medical grade poly(epsilon-caprolactone)/collagen (mPCL/Col) cospun with PEO or gelatin, mPCL/Col meshes with micron-sized fibers, and mPCL/Col microfibers cosprayed with Heprasil. All three scaffold types supported attachment and proliferation of human fetal osteoblasts. However, selective leaching only marginally improved cellular infiltration when compared to meshes obtained by conventional electrospinning. Better cell penetration was seen in mPCL/Col microfibers, and this effect was more pronounced when Heprasil regions were present in the structure. Thus, such techniques could be further exploited for the design of cell permeable fibrous meshes for tissue engineering applications.

Azo-linked corner porphyrin systems: synthesis, crystal structures and spectroscopic investigation

1,2-Bis[10,15-di(3,5-di-tert-butyl)phenylporphyrinatonickel(II)-5-yl]diazene was synthesised via copper catalysed coupling of aminated nickel(II) 5,10-diarylporphyrin (“corner porphyrin”) and its X-ray crystal structure was determined. Two different crystals yielded different structures, one with the free meso positions in a trans-like orientation, and the other with a cis-like disposition. The free meso positions of the obtained dimer have been further functionalised while the synthesis of a zinc analogue has so far been unsuccessful. The X-ray crystal structure of the dinitro derivative of the dinickel(II) azoporphyrin was determined, and the structure showed a cis-like disposition of the nitro groups.

Why adopt now? Multiple case studies and survey studies comparing small, medium and large firms

While a number of factors have been highlighted in the innovation adoption literature, little is known about the key triggers of innovation adoption across differently-sized firms. This study compares case studies of small, medium and large manufacturing firms who recently decided to adopt a process innovation. We also employ organizational surveys from 134 firms investigating the factors which influence innovation adoption. The quantitative results support the qualitative findings that the external pressures are a trigger among small to medium firms, while the larger firm’s adoption was associated with internal causes.

Do derivatives have a role in the risk-shifting behaviour of fund managers?

In this paper we examine the extent to which derivatives are used to affect the risk-shifting behaviour of Australian equity fund managers. We find, after periods of good and poor performance, the risk-shifting behaviour of fund managers is different between derivative users and non-users. Our results support the gaming and active competition hypotheses but there is little support for the cash flow hypothesis. The study also allows for a complex reporting environment by analysing data across three alternate time periods: the calendar year, financial year and quarterly frames. Given that our results are not consistent across time periods for users and non-users of derivatives, some caution in interpretation is required.