This article includes a description of the ARCH, GARCH, and EGARCH models and the estimation of their parameters using maximum likelihood. An alternative model is proposed for the analysis of financial series and used to study price and returns series for Gillette stock. The choice of models...

The objective of this paper is to analyze and analytically quantify the effect of additive outliers in the forecasting of volatility from an ARCH Model. For it, we start by distinguishing between Additive Level Outliers (ALO) and Additive Volatility Outliers (AVO), obtaining the analytical...

For decades, the academic literature has focused on three survey measures of expected inflation: the Livingston Survey, the Survey of Professional Forecasters, and the Michigan Survey. While these measures have been useful in developing models of forecasting inflation, the data are low frequency...

The Ohlson (1995) equity valuation and returns models are consistent with mathematical formulation. Since value relevance models that relate Ohlson (1995) focus on information dynamics of accounting and other information for explaining equity value, an inconsistency between Ohlson's (1995)...

[Update: Within four weeks of the original publication of this research report, Risk Magazine reported in its 28th February 2012 issue story titled 'Goodbye VaR? Basel to Consider Other Risk Metrics': "A review of trading book capital rules, due to be launched in March by the Basel Committee on...

A Hidden Markov Model (HMM) is used to model the VIX (the Cboe Volatility Index). A 4- state Gaussian mixture is fitted to the VIX price history from 1990 to 2022. Using a growing window of training data, the price of the S&P500 is predicted and two trading algorithms are presented, based on the...

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 Bayesian methodology has significant advantages. Just...

In this paper, we describe and compare three methods that can be used in forecasting chaotic time series. We simulate four well known chaos and apply the methods developed here. We discuss the existence of measure noise and structural noise and their impact on predictions. Particular attention...

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 inference on subjective judgment, the key limitation...