source site Bayesian inference estimation and model selection via filtering is studied.
Financial Econometrics Modeling: Market Microstructure, Factor Models and Financial Risk Measures. Editors: Gregoriou, G., Pascalau, R. (Eds.) Free Preview. Financial Econometrics Modeling: Market Microstructure, Factor Models and Financial Risk Measures. Editors; (view affiliations). Greg N. Gregoriou; Razvan.
The convergence theorems for consistent, efficient algorithms are established. Two general approaches for constructing algorithms are discussed.
One approach is the Markov chain approximation method, and the other is sequential Monte Carlo or particle filtering. Both methods provide parallel recursive online algorithms for estimation and model selections. It is increasingly popular in financial economics to estimate volatilities of asset returns by the methods based on realized volatility and bipower realized volatility from high-frequency data.
However the most available methods are not directly relevant when the number of assets involved is large, due to the lack of accuracy in estimating high dimensional matrices. Therefore it is pertinent to reduce the effective size of volatility matrices in order to produce adequate estimates and forecasts.
Furthermore, since high-frequency financial data for different assets are typically not recorded at the same time points, conventional dimension-reduction techniques are not directly applicable. In this paper we propose a new method for modelling volatility matrices based on multivariate non-synchronized high frequency return data. The new methodology consists of three steps: i estimate realized co-volatility matrices directly based on high-frequency data, ii fit a matrix factor model for daily volatility based on the estimated co-volatility matrices, and iii fit a vector autoregressive VAR model for the volatility factors.
The asymptotic theory for the proposed estimators has been established. We illustrate the new methodology with the high-frequency price data on several hundreds of stocks traded in Shen Zhen and Shanghai Stock Exchanges over a period of days in This paper investigates the use of tick-by-tick data for market risk measurement.
An ultra-high-frequency GARCH model extending the framework of Engle is used to specify the joint density of the marked point process of durations and high-frequency returns. Results show that our approach constitutes reliable means of measuring intraday risk for traders who are very active on the market. Our methodology allows for distinguishing on the total risk measure the effect of random trade durations from the effect of random returns and analyzing the interaction between these factors.
We find that the information contained in the time between transactions is relevant to risk analysis, which is consistent with predictions from asymmetric-information models in the market microstructure literature.
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See more details at Online Price Match. Email address. Our research activities in this area cover a wide variety of themes such as factor investing, mutual fund performance, option pricing and credit risk modelling. They include work to find new methodological contributions to gain a better understanding of the dynamics and risk factors governing financial markets. The research also examines the issue of exuberance in asset prices or, bubbles, and develops methods to detect explosive behaviour in asset prices in real time as well as the implementation of early warning diagnosis tests.
The centre hosts the UK Housing Observatory , which includes real-time monitoring of real estate markets and forecasting house prices. In Dynamic Stochastic General Equilibrium models DSGE , a major focus of our research are the interactions between the financial sector and the real economy altered by various forms of credit and financial frictions. We investigate the effects of monetary policies, financial regulation, and fiscal policies on the dynamics of the business cycle and welfare. Within dynamic general-equilibrium overlapping-generations models OLG , recent research evaluates policy changes that involve redistributions both within and across generations in the presence of uninsurable risks.
Other important research themes include the association between taxation, bank bailouts and macro-prudential regulations. And, an understanding of the stabilisation and redistribution properties of monetary and fiscal policies in an economy populated by heterogeneous agents and characterised by inequality. Our research in this area aims to understand the fundamental mechanisms, institutions, and policies that shape the functioning of banks, their credit policies, and interconnectivity.
These research objectives are especially important in the post-subprime-crisis era and span the global banking sector with a special emphasis on the UK and European banks. We propose new state-of-the-art tools to address problems of measurement and estimation of bank efficiency, productivity an elusive concept as well as stability in the financial sector.
A catalyst for this growth has been the resilience of the sector during the Global Financial Crisis. This is an area of research that is also closely linked to our Gulf One Lab for Computational and Economic Research project. Our current projects include four key topics on real-estate markets, bank supervisory and price risks. Nikolaus Hautsch, from the University of Vienna. The project also includes Professor Torben Andersen, from Northwestern University, as a distinguished international collaborator.
The objectives of the research are to improve our understanding of the microstructure of equity and derivative markets, their interrelations on a micro level and their information content for future prices, volatility, liquidity and jump risks. It involves financial econometrics, limit order book analysis, volatility modelling, market microstructure and derivative pricing.
The research evaluates how the public disclosure of supervisory assessments of individual banks affect bank stakeholders, other banks, and bank supervisors in both crisis and non-crisis periods. The insights for this study aim to inform the debate on whether, and under which conditions, such assessments should be made publicly available.
This is a project developed by the Economics Department at Lancaster University Management School aimed at improving understanding of the UK national and regional house price dynamics. This includes real-time monitoring of real estate markets and indicators of house price exuberance. Made up of researchers and students from the Department of Economics, as well as other global academic institutions, research expertise is in the area of applied econometric modelling, banking, Bayesian analysis, energy markets, environmental economics, high frequency data econometrics and Islamic finance.
Please visit our Research Initiatives for more information. We are home to a vibrant doctoral community where our PhD students are encouraged to take advantage of the School's research strengths to develop core skills. We welcome applications from those who wish to study in economics and finance. For more information, please contact Teresa Aldren.
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Scroll to content. About this Centre Our members include financial and time series econometricians, macroeconomists, and researchers in asset pricing.