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Volatility Forecasting Using Threshold Heteroskedastic Models of the Intra-day Range

Chen, Cathy W. S.; Gerlach, Richard H.; Lin, Edward M.H - 2009

An effective approach for forecasting return volatility via threshold nonlinear heteroskedastic models of the daily asset price range is provided. The return is defined as the difference between the highest and lowest log intra-day asset price. A general model specification is proposed, allowing...

Persistent link: https://www.econbiz.de/10014207634

Advancing Style Analysis and Risk Modeling by Incorporating Model Uncertainty with Bayesian Model Averaging

Zhou, Hao (David) - 2013

risk and market dynamics. This paper demonstrates how macroeconomic factor models, based on Bayesian model averaging (BMA …), can help address the challenges in some specific investment analytic tasks from three perspectives: (1) selecting risk … factors and estimating risk factor exposure in risk allocation, (2) modeling the dynamic exposure of multiple asset classes to …

Persistent link: https://www.econbiz.de/10013073771

Downside Risk Evaluation with the R Package GAS

Ardia, David - 2019

Financial risk managers routinely use non-linear time series models to predict the downside risk of the capital under … prediction and the evaluation of downside risk. Emphasis is given to the two key financial downside risk measures: Value-at-Risk …

Persistent link: https://www.econbiz.de/10012902645

Beyond ‘Bayesian vs. Var’ Dilemma to Empirical Model Risk Management : Managing Risk for Hedge Funds

Malhotra, Yogesh - 2023

In the aftermath of the Global Financial Crisis, some risk management practitioners have advocated wider adoption of … Bayesian inference to replace Value- at-Risk (VaR) models in order to minimize risk failures. Despite its limitations, the … [increasingly] Bayesian—continues to be a key methodological foundation of risk management and regulation-related risk modeling …

Persistent link: https://www.econbiz.de/10014263882

Beyond ‘Bayesian vs. VaR’ Dilemma to Empirical Model Risk Management : How to Manage Risk (After Risk Management Has Failed) for Hedge Funds

Malhotra, Yogesh - 2015

In aftermath of the Financial Crisis, some risk management practitioners advocate wider adoption of Bayesian inference … to replace Value-at-Risk (VaR) models for minimizing risk failures (Borison & Hamm, 2010). They claim reliance of …-Bayesian and [increasingly] Bayesian – continues to be a key methodological foundation of risk management and regulation related …

Persistent link: https://www.econbiz.de/10013031477

Jumps and Stochastic Volatility in Oil Prices : Time Series Evidence

Larsson, Karl - 2014

In this paper we examine the empirical performance of affine jump diffusion models with stochastic volatility in a time series study of crude oil prices. We compare four different models and estimate them using the Markov Chain Monte Carlo method. The support for a stochastic volatility model...

Persistent link: https://www.econbiz.de/10013070384

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...

Malhotra, Yogesh - 2022

GLOBAL FINANCE LIQUIDITY RISK REVISITED: Development of A Framework for Liquidity Assessment in Portfolio Construction … and Quantitative-Financial Engineers-Developers Team.Our JP Morgan World Headquarters presentations titled Global Finance … Liquidity Risk Revisited: JP Morgan Alternative Assets Portfolio Liquidity Assessment Framework & Models: $500 Billion Fund of …

Persistent link: https://www.econbiz.de/10013403261

Global Finance Liquidity Risk Revisited : JP Morgan Alternative Assets Portfolio Liquidity Assessment Framework & Models: $500 Billion Fund of Funds: 17 Asset Classes

Malhotra, Yogesh - 2022

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 … Guided Teams of Quants, Portfolio Managers and Managing Directors: Built Liquidity Risk Modeling System for Deployment by the …

Persistent link: https://www.econbiz.de/10013405318

Boosting Cryptocurrency Return Prediction

Filippou, Ilias; Rapach, David; Thimsen, Christoffer - 2021

We use boosted decision trees to generate daily out-of-sample forecasts of excess returns for Bitcoin and Ethereum, the two best-known and largest cryptocurrencies. The decision trees incorporate information from 39 predictors, including variables relating to cryptocurrency fundamentals,...

Persistent link: https://www.econbiz.de/10013213970

Machine Learning Goes Global : Cross-Sectional Return Predictability in International Stock Markets

Cakici, Nusret; Fieberg, Christian; Metko, Daniel; … - 2022

with many listed firms and high idiosyncratic risk limiting arbitrage …

Persistent link: https://www.econbiz.de/10013405067