Category Archives: Volatility Modeling

Enhancing Mutual Fund Returns With Market Timing

Summary In this article, I will apply market timing techniques to several popular mutual funds. The market timing approach produces annual rates of return that are 3% to 7% higher, with lower risk, than an equivalent buy and hold mutual … Continue reading

Posted in Forecasting, Market Timing, Time Series Modeling, Trading, Volatility Modeling | Tagged , , | Leave a comment

How to Bulletproof Your Portfolio

Summary How to stay in the market and navigate the rocky terrain ahead, without risking hard won gains. A hedging program to get you out of trouble at the right time and step back in when skies are clear. Even … Continue reading

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A Scalping Strategy in E-Mini Futures

This is a follow up post to my post on the Mathematics of Scalping. To illustrate the scalping methodology, I coded up a simple strategy based on the techniques described in the post. The strategy trades a single @ES contract on 1-minute … Continue reading

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Stationarity and Fat Tails

In this post I am going to explore how, starting from the assumption of a stable, Gaussian distribution in a returns process, we evolve to a system that displays all the characteristics of empirical market data, notably time-dependent moments, high … Continue reading

Posted in Fat Tails, Mathematica, Regime Shifts, Regime Switching, Uncategorized, Volatility Modeling | Tagged , , , , , , | Comments Off

The Mathematics of Scalping

NOTE:  if you are unable to see the Mathematica models below, you can download the free Wolfram CDF player and you may also need this plug-in. You can also download the complete Mathematica CDF file here. In this post I want … Continue reading

Posted in Futures, Mathematca, Mathematica, Scalping, Trading, Volatility Modeling | Tagged , , , , , , , , , , , , , , , , | Comments Off

Implied Volatility in Merton’s Jump Diffusion Model

The “implied volatility” corresponding to an option price is the value of the volatility parameter for which the Black-Scholes model gives the same price. A well-known phenomenon in market option prices is the “volatility smile”, in which the implied volatility … Continue reading

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Option Prices in the Variance Gamma Model

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Range-Based EGARCH Option Pricing Models (REGARCH)

The research in this post and the related paper on Range Based EGARCH Option pricing Models is focused on the innovative range-based volatility models introduced in Alizadeh, Brandt, and Diebold (2002) (hereafter ABD).  We develop new option pricing models using … Continue reading

Posted in Financial Engineering, Forecasting, Long Memory, Multifactor Models, Options, REGARCH, S&P500 Index, Volatility Modeling | Tagged , , , | Comments Off

On Testing Direction Prediction Accuracy

As regards the question of forecasting accuracy discussed in the paper on Forecasting Volatility in the S&P 500 Index, there are two possible misunderstandings here that need to be cleared up.  These arise from remarks by one commentator  as follows: … Continue reading

Posted in Direction Prediction, Forecasting, Modeling, Options, S&P500 Index, Volatility Modeling, volatility sign prediction forecasting Engle | Tagged , , , , , , , | Comments Off

Long Memory and Regime Shifts in Asset Volatility

This post covers quite a wide range of concepts in volatility modeling relating to long memory and regime shifts. The post discusses autocorrelation, long memory, fractional integration, black noise, white noise, Hurst Exponents, regime shift detections, Asian markets and various topics froms nonlinear dynamics. Continue reading

Posted in ARFIMA, Asian markets, Black Noise, Correlation Dimension, Correlation Integral, FIGARCH, Forecasting, Fractional Brownian Motion, Fractional Integration, Henon Attractor, Hurst Exponent, Logistic Attractor, Long Memory, Modeling, Nonlinear Dynamics, Pink Noise, Regime Shifts, Strange Attractor, Uncategorized, Volatility Modeling, White Noise | Tagged , , , , , , , , , , , , | Comments Off