Gareth W. Peters

Trends in Crypto-Currencies and Blockchain Technologies: A Monetary Theory and Regulation Perspective

The internet era has generated a requirement for low-cost, anonymous and rapidly verifiable transactions to be used for online barter, and fast settling money has emerged as a consequence. For the most part, electronic money (e-money) has fulfilled this role, but the last few years have seen two new types of money emerge — centralized virtual currencies, usually for the purpose of transacting in social and gaming economies, and cryptocurrencies, which aim to eliminate the need for financial intermediaries by offering direct peer-to-peer (P2P) online payments. We describe the historical context that led to the development of these currencies and some modern and recent trends in their uptake, in terms of both usage in the real economy and as investment products. As these currencies are purely digital constructs, with no government or local authority backing, we discuss them in the context of monetary theory, in order to determine how they may be valued under each. Finally, we provide an overview of the state of regulatory readiness in terms of dealing with transactions in these currencies in various regions of the world.

Liquidity commonality does not imply liquidity resilience commonality: a functional characterisation for ultra-high frequency cross-sectional LOB data

We present a large-scale study of commonality in liquidity and resilience across assets in an ultra high-frequency (millisecond-timestamped) Limit Order Book (LOB) data-set from a pan-European electronic equity trading facility. We first show that extant work in quantifying liquidity commonality through the degree of explanatory power of the dominant modes of variation of liquidity (extracted through Principal Component Analysis) fails to account for heavy-tailed features in the data, thus producing potentially misleading results. We employ Independent Component Analysis, which both decorrelates the liquidity measures in the asset cross section, but also reduces higher order statistical dependencies. To measure commonality in liquidity resilience, we utilise a novel characterisation proposed by Panayi et al. [Market resilience, 2014] for the time required for return to a threshold liquidity level. This reflects a dimension of liquidity that is not captured by the majority of liquidity measures and has important ramifications for understanding supply and demand pressures for market makers in electronic exchanges, as well as regulators and HFTs. When the metric is mapped out across a range of thresholds, it produces the daily Liquidity Resilience Profile for a given asset. This daily summary of liquidity resilience behaviour from the vast LOB data-set is then amenable to a functional data representation. This enables the comparison of liquidity resilience in the asset cross section via functional linear sub-space decompositions and functional regression. The functional regression results presented here suggest that market factors for liquidity resilience (as extracted through functional principal components analysis) can explain between 10 and 40% of the variation in liquidity resilience at low liquidity thresholds, but are less explanatory at more extreme levels, where individual asset factors take effect.

Heavy-Tailed Features and Dependence in Limit Order Book Volume Profiles in Futures Markets

This paper investigates fundamental stochastic attributes of the random structures of the volume profiles of the limit order book. We find statistical evidence that heavy-tailed sub-exponential volume profiles occur on the limit order book and these features are best captured via the generalized Pareto distribution MLE method. In futures exchanges, the heavy tail features are not asset class dependent and occur on ultra or mid-range high frequency. Volume forecasting models should account for heavy tails, time varying parameters and long memory. In application, utilizing the generalized Pareto distribution to model volume profiles allows one to avoid over-estimating the round trip cost of trading.

Bayesian modelling, Monte Carlo sampling and capital allocation of insurance risks

The main objective of this work is to develop a detailed step-by-step guide to the development and application of a new class of efficient Monte Carlo methods to solve practically important problems faced by insurers under the new solvency regulations. In particular, a novel Monte Carlo method to calculate capital allocations for a general insurance company is developed, with focus on coherent capital allocation that is compliant with the Swiss Solvency Test. The data used is based on the balance sheet of a representative stylized company. For each line of business in that company, allocations are calculated for the one-year risk with dependencies based on correlations given by the Swiss Solvency Test. Two different approaches for dealing with parameter uncertainty are discussed and simulation algorithms based on (pseudo-marginal) Sequential Monte Carlo algorithms are described and their efficiency is analysed.

Estimation of Cointegrated Spaces: A Numerical Case Study on Efficiency, Accuracy and Influence of the Model Noise

In this paper we develop an analysis of multivariate time series that exhibit reduced rank cointegration, implying that a lower dimensional linear projection of the process can be obtained in which the projected process becomes stationary. Detection of the rank and basis upon which to project the process for stationarity to hold is a critical problem when working with such settings in practice. There is a range of practice when performing estimation of in such multivariate time series settings. In this paper we provide a review of a few selected different models and estimation techniques for these multivariate time series. Having presented an overview of important new directions with regard to estimation of cointegration relationships we then turn our attention to the performance of a range of estimation procedures. In particular we design a range of numerical studies in order to assess some of these approaches in terms of efficiency and accuracy. In particular, we study the question related to examining the robustness of these classes of estimation procedure in Bayesian and non-parametric estimation approaches to the influence of the model noise in the estimation of the cointegration space. In this context we develop a novel Bayesian inference procedure not previously studied in cointegration models to estimate the cointegration space. This is based on a Markov Chain Monte Carlo sampling method, that consists of a novel extension of Hamiltonian and Geodesic Monte Carlo for the present problem. We will illustrate the performance of this method numerically and show that it produces results on par with an efficient Gibbs Sampler.