### Forums

### Blogroll

### Search

### Archives

### Categories

- Algorithmic Trading (6)
- Alternative Investment (1)
- ARFIMA (1)
- ARMA (4)
- Asian markets (2)
- Binary Options (1)
- Black Noise (2)
- Cointegration (2)
- Correlation Dimension (1)
- Correlation Integral (1)
- Derivatives (3)
- Direction Prediction (4)
- Dispersion (1)
- Econometrics (7)
- Econophysics (2)
- Education (1)
- Emerging Markets (1)
- ETFs (2)
- Fat Tails (2)
- FIGARCH (2)
- Financial Engineering (5)
- Forecasting (17)
- Fourier Transforms (1)
- Fractional Brownian Motion (2)
- Fractional Cointegration (2)
- Fractional Integration (3)
- Futures (2)
- GARCH (1)
- Graduate Programs (1)
- Granger Causality (1)
- Hedge Funds (1)
- Henon Attractor (1)
- High Frequency Finance (5)
- High Frequency Trading (4)
- Hurst Exponent (2)
- Hybrid Products (1)
- Interest Rate Models (1)
- Jobs (1)
- Logistic Attractor (1)
- Logit Regression (2)
- Long Memory (4)
- Machine Learning (4)
- Market Efficiency (2)
- Market Microstructure (3)
- Market Timing (2)
- Markov Model (1)
- Markov State Models (1)
- Mathematca (1)
- Mathematica (3)
- Matlab (2)
- Mean Reversion (2)
- Metals (1)
- Model Review (1)
- Modeling (4)
- Momentum (1)
- Multifactor Models (2)
- Nearest Neighbor (1)
- Neural Networks (1)
- Nonlinear Classification (2)
- Nonlinear Dynamics (2)
- Options (6)
- Order Flow (1)
- Pairs Trading (2)
- Pink Noise (2)
- Principal Components Analysis (1)
- Purchasing Power Parity (1)
- Quant/Traders (1)
- Random Forrests (1)
- Recruitment (1)
- REGARCH (3)
- Regime Shifts (4)
- Regime Switching (2)
- Regression (1)
- Risk Management (1)
- S&P500 Index (7)
- Scalping (1)
- Signal Processing (1)
- Spline Methods (1)
- Statistical Arbitrage (2)
- Stochastic Differential Equations (1)
- Strange Attractor (1)
- Support Vector Machines (1)
- Time Series Modeling (4)
- Toxic Flow (1)
- Trading (6)
- Uncategorized (5)
- Unit Roots (1)
- Volatility Modeling (17)
- volatility sign prediction forecasting Engle (3)
- White Noise (3)
- Yield Curve Modeling (1)

### Tag Cloud

ARFIMA ARMA Models Black Noise Derivatives Direction Prediction E-mini ETFs Financial engineering Forecasting Fractional Brownian Motion Fractional Cointegration Fractional Integration Futures GARCH High Frequency Trading Hybrid Products Jump Diffusion Kalman Filter Kurtosis Long Memory Machine Learning Market Microstructure Market Timing Mathematica MultiFactor Models Natural Gas Option Pricing Options Pairs Trading REGARCH Regime Shifts Robustness S&P500 Index Scalping Skewness Smile Statistical Arbitrage Stochastic Volatility Strategy Tradestation Trading Volatility Volatility Dynamics Volatility Metrics White Noise

# Category Archives: Forecasting

## 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 Market Timing, Mutual Funds, Volatility Modeling
Comments Off

## Measuring Toxic Flow for Trading & Risk Management

A common theme of microstructure modeling is that trade flow is often predictive of market direction. One concept in particular that has gained traction is flow toxicity, i.e. flow where resting orders tend to be filled more quickly than expected, while … Continue reading

Posted in Algorithmic Trading, ARMA, Direction Prediction, Econometrics, Econophysics, Forecasting, High Frequency Finance, Market Microstructure, Order Flow, Risk Management, Time Series Modeling, Toxic Flow
Tagged Forecasting, Market Microstructure, Order Flow, Toxic Flow
Comments Off

## Alpha Spectral Analysis

One of the questions of interest is the optimal sampling frequency to use for extracting the alpha signal from an alpha generation function. We can use Fourier transforms to help identify the cyclical behavior of the strategy alpha and hence … Continue reading

Posted in Forecasting, Fourier Transforms, High Frequency Finance, Pairs Trading, Principal Components Analysis, Signal Processing, Statistical Arbitrage
Tagged Fourier Transforms, High Frequency Trading, Pairs Trading, Principal Components Analysis, Signal Processing, Statistical Arbitrage
Comments Off

## Market Microstructure Models for High Frequency Trading Strategies

This note summarizes some of the key research in the field of market microstructure and considers some of the models proposed by the researchers. Many of the ideas presented here have become widely adopted by high frequency trading firms and … Continue reading

Posted in Econophysics, Forecasting, High Frequency Finance, High Frequency Trading, Market Microstructure
Tagged Econophysics, Forecasting, High Frequency Finanance, High Frequency Trading, Market Microstructure
Comments Off

## Forecasting Financial Markets – Part 1: Time Series Analysis

The presentation in this post covers a number of important topics in forecasting, including: Stationary processes and random walks Unit roots and autocorrelation ARMA models Seasonality Model testing Forecasting Dickey-Fuller and Phillips-Perron tests for unit roots Also included are a number … Continue reading

Posted in ARMA, Econometrics, Forecasting, Purchasing Power Parity, Time Series Modeling, Unit Roots, White Noise
Tagged ARMA Models, Box Jenkins, Direction Prediction, Forecasting, Purchasing Power Parity, Time Series Analysis
Comments Off

## A Practical Application of Regime Switching Models to Pairs Trading

In the previous post I outlined some of the available techniques used for modeling market states. The following is an illustration of how these techniques can be applied in practice. You can download this post in pdf format here. The chart … Continue reading

Posted in ARMA, Econometrics, ETFs, Markov Model, Mean Reversion, Pairs Trading, Regime Switching, Statistical Arbitrage
Tagged ETFs, Kalman Filter, Markov Model, Pairs Trading, Regime Switching, Statistical Arbitrage
Comments Off

## Regime-Switching & Market State Modeling

The Excel workbook referred to in this post can be downloaded here. Market state models are amongst the most useful analytical techniques that can be helpful in developing alpha-signal generators. That term covers a great deal of ground, with ideas … Continue reading

Posted in ARMA, Econometrics, Fat Tails, Forecasting, Markov State Models, Regime Shifts
Tagged ARMA Models, Forecasting, Markov State Models, Regime Shifts
Comments Off

## 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
Comments Off

## Resources for Quantitative Analysts

Two of the smartest econometricians I know are Prof. Stephen Taylor of Lancaster University, and Prof. James Davidson of Exeter University. I recall spending many profitable hours in the 1980′s with Stephen’s book Modelling Financial Time Series, which I am … Continue reading

Posted in Econometrics, Forecasting, Time Series Modeling
Tagged Econometrics, Forecasting
Comments Off

## Can Machine Learning Techniques Be Used To Predict Market Direction? The 1,000,000 Model Test.

During the 1990′s the advent of Neural Networks unleashed a torrent of research on their applications in financial markets, accompanied by some rather extravagant claims about their predicative abilities. Sadly, much of the research proved to be sub-standard and the … Continue reading

Posted in Direction Prediction, Forecasting, Logit Regression, Machine Learning, Matlab, Modeling, Nearest Neighbor, Neural Networks, Nonlinear Classification, Nonlinear Dynamics, Random Forrests, S&P500 Index, Support Vector Machines
Tagged Direction Prediction, Forecasting, Machine Learning, Nearest Neighbor, Neural Networks, Nonlinear Classification, Random Forrests, Support Vector Machines
Comments Off