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# Tag Archives: Volatility

## Volatility ETF Strategy – Nov 2014 Update: +1.42%

HIGHLIGHTS CAGR over 39% annually Sharpe ratio in excess of 3 Max drawdown -13.40% Liquid, exchange-traded ETF assets Fully automated, algorithmic execution Monthly portfolio turnover Managed accounts with daily MTM Minimum investment $250,000 Fee structure 2%/20% VALUE OF $1,000 … Continue reading

Posted in Uncategorized, VIX Index, Volatility ETF Strategy, Volatility Modeling
Tagged ETF, Performance, VIX, Volatility
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## Volatility ETF Strategy – Sept 2014 Update

HIGHLIGHTS CAGR over 40% annually Sharpe ratio in excess of 3 Max drawdown -4.3% Liquid, exchange-traded ETF assets Fully automated, algorithmic execution Monthly portfolio turnover Managed accounts with daily MTM Minimum investment $250,000 Fee structure 2%/20% VALUE OF $1,000 2012-2014 … Continue reading

Posted in ETFs, VIX Index, Volatility ETF Strategy, Volatility Modeling
Tagged ETFs, Performance, VIX, Volatility
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## Not the Market Top

Our most reliable market timing indicator is a system that “trades” the CBOE VIX Index, a measure of option volatility in the S&P500 Index. While the VIX index itself is not tradable, the system provides a signal that can be … Continue reading

Posted in Market Timing, VIX Index, Volatility Modeling
Tagged Market Timing, Market Top, VIX, Volatility
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## Volatility Strategy +15.19% in August: Here’s How

WHERE VOLATILITY THRIVES Mark Gilbert has written extensively in BloombergView about the demise of volatility across asset classes and what this may portend for markets (see Volatility Dies, Hedge Funds Lose). As Mark and other commentators have pointed out, the … Continue reading

## 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 Computable Document Format, Dynamic model, E-min, Execution, Extreme Value Distribution, Futures, Gaussian, High Frequency, IOC orders, Latency, Limit order book, Mathematica, Mote Carlo Simulation, Non-Gaussian, Scalping, Trade Expression, Volatility
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## Volatility Forecasting in Emerging Markets

The great majority of empirical studies have focused on asset markets in the US and other developed economies. The purpose of this research is to determine to what extent the findings of other researchers in relation to the characteristics of … Continue reading

Posted in Asian markets, Cointegration, Econometrics, Emerging Markets, FIGARCH, Forecasting, Fractional Cointegration, Fractional Integration, Granger Causality, Hurst Exponent, Long Memory, REGARCH
Tagged ARFIMA, Emerging Markets, Fractional Cointegration, Fractional Integration, Granger Causality, KOSPI, Long Memory, MultiFactor Models, REGARCH, Regime Shifts, Volatility
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## 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 ARFIMA, Black Noise, Forecasting, Fractional Brownian Motion, Fractional Integration, Henon Attractor, Logistic Attractor, Long Memory, Regime Shifts, Strange Attractor, Volatility, Volatility Dynamics, White Noise
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