GLOBAL FINANCE LIQUIDITY RISK REVISITED: Development of A Framework for Liquidity Assessment in Portfolio Construction Process: Presentations to the JP Morgan Global Head of Quant Research & Analytics and US Head of Portfolio Construction Teams:Presentations To: JP Morgan Global Head of Quant...

GLOBAL FINANCE LIQUIDITY RISK REVISITED: JP Morgan Alternative Assets Portfolio Liquidity Assessment Framework & Models: $500 Billion Fund of Funds: 17 Asset ClassesPresentations atJP Morgan World HQ, 270 Park Ave, Manhattan, NY, USAToJP Morgan Global Head of Quant Research & Analytics, JP...

In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap proach which is easily implemented in empirical...

Implicit in industry standard option pricing models is the expectation that roughly 25% of stocks with 60% consistent volatility will septuple within 10 years, an extraordinary rate of appreciation. The exceptionally high equilibrium anticipated returns for an improbably large percentage of high...

This paper develops an augmented Artificial Neural Network forecast-simulation procedure for estimating both the current fundamental price of a financial asset and the state-dependent distribution (including volatilities) from which future returns will be fundamentally drawn. The results provide...

This paper investigates the ex post rational price of Shiller (1981) to determine if it is a reliable guide to fundamental valuations of dividend-yielding assets. The ex post rational price is widely used to determine whether stock market price movements are rational responses to market news or...

We develop a coordination game to model interactions between fundamentals and liquidity during unstable periods in financial markets. We then propose a flexible econometric framework for estimation of the model and analysis of its quantitative implications. The specific empirical application is...

This paper develops an augmented Artificial Neural Network forecast-simulation procedure for estimating both the current fundamental price of a financial asset and the state-dependent distribution (including volatilities) from which future returns will be fundamentally drawn. The results provide...

In this paper, we review the most common specifications of discrete-time stochas- tic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap- proach which is easily implemented in empirical...

Volatility models have been extensively used in risk modeling especially GARCH models under the normal distribution. Although they generate highly significant coefficient estimates, these models are known to have poor forecasting power. It is therefore interesting to develop a different approach...