Markov type models for large-valued interbank payment systems

Due to the reform of payment systems from netting settlement systems to Real Time Gross Settlement systems (RTGS) around the world in recent years, there is a dramatic increase in the interest in modeling the large-valued interbank payment system. Recently some queueing facilities have been introduced in the response to the liquidity management within the RTGS systems. Since stochastic process models have been wildly applied in social networks, and some aspects of which have similar statistical properties with the payment system, therefore, based on the existing empirical research, a Markov type model for RTGS payment system with queueing and collateral borrowing facilities was developed. We analysed the effect on the performance of the payment system of the parameters, such as the probabilities of payment delay, the initial cash position of participating banks and the probabilities of cross bank payments. Two models were proposed; one is the simplest model where payments were assumed to be equally distributed among participating banks, the other one is a so-called "cluster" model, that there exists a concentration of payments flow between a few banks according to the evidence from empirical studies. We have found that the performance of the system depends on these parameters. A modest amount of total initial liquidity required by banks would achieve a desired performance, that minimising the number of unsettled payments by the end of a business day and negligible average lifetime of the debts. Because of the change of large-valued interbank payment systems, the concern has shift from credit risk to liquidity risk, and the payment systems around world started considering or already implemented different liquidity saving mechanisms to reduce the high demand of liquidity and maintain the low risk of default in the mean time. We proposed a specified queueing facility to the "cluster" model with modification with the consideration of the feature of the UK RTGS payment system, CHAPS. Some of thepayments would be submitted into a external queue by certain rules, and will be settled according an algorithm of bilateral or multilateral offsetting. While participating banks's post liquidity will be reserved for "important" payments only. The experiment of using simulated data showed that the liquidity saving mechanism was not equally beneficial to every bank, the banks who dominated most of the payment flow even suffered from higher level of debts at the end of a business day comparing with a pure RTGS system without any queueing facility. The stability of the structure of the central queue was verified. There was evidence that banks in the UK payment system would set up limits for other members to prevent unexpected credit exposure, and with these limits, banks also achieved a moderate liquidity saving in CHAPS. Both central bank and participating banks are interested in the probability that the limits are excess. The problem can be reduced to the calculation of boundary crossing probability from a Brownian motion with stochastic boundaries. Boundary crossing problems are very popular in many fields of Statistics. With powerful tools, such as martingales and infinitesimal generator of Brownian motion, we presented an alternative method and derived a set of theorems of boundary crossing probabilities for a Brownian motion with different kinds of stochastic boundaries, especially compound Poisson process boundaries. Both the numerical results and simulation experiments are studies. A variation of the method would be discussed when apply it to other stochastic boundaries, for instances, Gamma process, Inverse Gaussian process and Telegraph process. Finally, we provided a brief survey of approximations of Levy processes. The boundary crossing probabilities theorems derived earlier could be extended to a fair general situation with Levy process boundaries, by using an appropriate approximation

Endogenous bank risk and efficiency

We develop a framework to incorporate bank risk, as measured from the variance of profits or returns, within a model of frontier efficiency. Our framework follows the premise that risk is endogenously related to efficiency. We estimate our model using panel data for U.S. banks and Bayesian techniques. We show that excluding risk from the efficiency model significantly biases the efficiency estimates and the ranking of banks according to their competitive advantage. We also demonstrate that there is a negative risk-efficiency nexus with causality running both ways, while our estimates of risk are fully consistent with the developments in the banking industry over the period 1976-2014

Multithreading for high performance finance risk analysis

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel UniversityWith the increasing of risks in the financial market, the models of risk management are developing quickly. The standard of the accuracy and effect of the models is improved continuously. This thesis investigates Value at Risk (VaR) which is an important method for measuring the market risk. It reviews the three methods which can be used to quantify VaR. These methods are parameter method, historical data processing method and Monte Carlo simulation method. Monte Carlo simulation has been widely employed for finance risk analysis. One challenge in Monte Carlo simulation is its computation complexity. For this purpose, this thesis researches into multithreading technique for high performance

Asset pricing and investor risk in subordinated asset securitisation

As a sign of ambivalence in the regulatory definition of capital adequacy for credit risk and the quest for more efficient refinancing sources collateral loan obligations (CLOs) have become a prominent securitisation mechanism. This paper presents a loss-based asset pricing model for the valuation of constituent tranches within a CLO-style security design. The model specifically examines how tranche subordination translates securitised credit risk into investment risk of issued tranches as beneficial interests on a designated loan pool typically underlying a CLO transaction. We obtain a tranchespecific term structure from an intensity-based simulation of defaults under both robust statistical analysis and extreme value theory (EVT). Loss sharing between issuers and investors according to a simplified subordination mechanism allows issuers to decompose securitised credit risk exposures into a collection of default sensitive debt securities with divergent risk profiles and expected investor returns. Our estimation results suggest a dichotomous effect of loss cascading, with the default term structure of the most junior tranche of CLO transactions (“first loss position”) being distinctly different from that of the remaining, more senior “investor tranches”. The first loss position carries large expected loss (with high investor return) and low leverage, whereas all other tranches mainly suffer from loss volatility (unexpected loss). These findings might explain why issuers retain the most junior tranche as credit enhancement to attenuate asymmetric information between issuers and investors. At the same time, the issuer discretion in the configuration of loss subordination within particular security design might give rise to implicit investment risk in senior tranches in the event of systemic shocks. JEL Classifications: C15, C22, D82, F34, G13, G18, G2

Modelling sustainability efficiency in banking

We significantly contribute to the empirical literature by investigating sustainability efficiency in the banking industry and decompose it into internal sustainability and external sustainability in an explicit manner. We fill in the gap of the literature by considering internal sustainability from two perspectives which are banking stability and economic performance. We also extend the current studies by including both banks' social contributions (contributions to the society and the company development) and environmental responsibility to the estimation of external sustainability. Finally, we fill in the gap of the literature by estimating the determinants of bank sustainability efficiency. The findings from the output distance function and the panel vector autoregressive model show that the sustainability efficiency level in the Chinese banking industry (2007–2017) ranges from 0.45 to 0.75 (maximum sustainability efficiency score is 1 and minimum sustainability efficiency score is 0). There is a larger difference in terms of external sustainability efficiency in the sample, while stability is still one of the most serious issues, as reflected by the low stability efficiency score compared to other efficiency concepts. The results also show that internal sustainability efficiency is significantly affected by the firm specific determinants, business environment determinants and economic environment determinants

Recent Developments in Financial Economics and Econometrics: An Overview

Research papers in empirical finance and financial econometrics are among the most widely cited, downloaded and viewed articles in the discipline of Finance. The special issue presents several papers by leading scholars in the field on “Recent Developments in Financial Economics and Econometrics”. The breadth of coverage is substantial, and includes original research and comprehensive review papers on theoretical, empirical and numerical topics in Financial Economics and Econometrics by leading researchers in finance, financial economics, financial econometrics and financial statistics. The purpose of this special issue on “Recent Developments in Financial Economics and Econometrics” is to highlight several novel and significant developments in financial economics and financial econometrics, specifically dynamic price integration in the global gold market, a conditional single index model with local covariates for detecting and evaluating active management, whether the Basel Accord has improved risk management during the global financial crisis, the role of banking regulation in an economy under credit risk and liquidity shock, separating information maximum likelihood estimation of the integrated volatility and covariance with micro-market noise, stress testing correlation matrices for risk management, whether bank relationship matters for corporate risk taking, with evidence from listed firms in Taiwan, pricing options on stocks denominated in different currencies, with theory and illustrations, EVT and tail-risk modelling, with evidence from market indices and volatility series, the economics of data using simple model free volatility in a high frequency world, arbitrage-free implied volatility surfaces for options on single stock futures, the non-uniform pricing effect of employee stock options using quantile regression, nonlinear dynamics and recurrence plots for detecting financial crisis, how news sentiment impacts asset volatility, with evidence from long memory and regime-switching approaches, quantitative evaluation of contingent capital and its applications, high quantiles estimation with Quasi-PORT and DPOT, with an application to value-at-risk for financial variables, evaluating inflation targeting based on the distribution of inflation and inflation volatility, the size effects of volatility spillovers for firm performance and exchange rates in tourism, forecasting volatility with the realized range in the presence of noise and non-trading, using CARRX models to study factors affecting the volatilities of Asian equity markets, deciphering the Libor and Euribor spreads during the subprime crisis, information transmission between sovereign debt CDS and other financial factors for Latin America, time-varying mixture GARCH models and asymmetric volatility, and diagnostic checking for non-stationary ARMA models with an application to financial data