Tag Archives: Kurtosis

What Wealth Managers and Family Offices Need to Understand About Alternative Investing

The most recent Morningstar survey provides an interesting snapshot of the state of the alternatives market.  In 2013, for the third successive year, liquid alternatives was the fastest growing category of mutual funds, drawing in flows totaling $95.6 billion.  The fastest … Continue reading

Posted in Alternative Investment, Hedge Funds | Tagged , , , , , , | Comments Off

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

Market Timing in the S&P 500 Index Using Volatility Forecasts

To illustrate some of the possibilities of this approach, we constructed a simple market timing strategy in which a position was taken in the S&P 500 index or in 90-Day T-Bills, depending on an ex-ante forecast of positive returns from the logit regression model (and using an expanding window to estimate the drift coefficient). We assume that the position is held for 30 days and rebalanced at the end of each period. In this test we make no allowance for market impact, or transaction costs.
Continue reading

Posted in Binary Options, Forecasting, Logit Regression, Market Timing, S&P500 Index, Volatility Modeling, volatility sign prediction forecasting Engle | Tagged , , , , , , , , | Comments Off

Forecasting Volatility in the S&P500 Index

Echoing the findings of parallel empirical research, this study points to the conclusion that historical realized volatility adds little to the explanatory power of implied volatility forecasts. However, one perplexing feature of implied volatility forecasts is their persistent upwards bias. As a result, forecasting models using high-frequency historical data may have an edge over implied volatility forecasts in predicting the direction of future realized volatility. The ability to time the market by correctly predicting its direction approximately 62% of the time appears to offer the potential to generate abnormal returns by a simple strategy of buying and selling at-the-money straddles and delta-hedging the resulting positions on a daily basis through to expiration, even after allowing for realistic transaction and hedging costs. Continue reading

Posted in Derivatives, Forecasting, GARCH, Market Efficiency, Options, Volatility Modeling, volatility sign prediction forecasting Engle | Tagged , , , , , , , , | Comments Off