M.P.E. Martens (Martin)
http://repub.eur.nl/ppl/2889/
List of Publicationsenhttp://repub.eur.nl/logo.jpg
http://repub.eur.nl/
RePub, Erasmus University RepositoryForecasting volatility with the realized range in the presence of noise and non-trading
http://repub.eur.nl/pub/39623/
Mon, 01 Apr 2013 00:00:01 GMT<div>K. Bannouh</div><div>M.P.E. Martens</div><div>D.J.C. van Dijk</div>
We introduce a heuristic bias-adjustment for the transaction price-based realized range estimator of daily volatility in the presence of bid-ask bounce and non-trading. The adjustment is an extension of the estimator proposed in Christensen et al. (2009). We relax the assumption that all intraday high (low) transaction prices are at the ask (bid) quote. Using data-based simulations we obtain estimates of the probability that a given intraday range is (upward or downward) biased or not, which we use for a more refined bias-adjustment of the realized range estimator. Both Monte Carlo simulations and an empirical application involving a liquid and a relatively illiquid S&P500 constituent demonstrate that ex post measures and ex ante forecasts based on the heuristically adjusted realized range compare favorably to existing bias-adjusted (two time scales) realized range and (two time scales) realized variance estimators.Forecasting Volatility with the Realized Range in the Presence of Noise and Non-Trading
http://repub.eur.nl/pub/37538/
Thu, 25 Oct 2012 00:00:01 GMT<div>K. Bannouh</div><div>M.P.E. Martens</div><div>D.J.C. van Dijk</div>
We introduce a heuristic bias-adjustment for the transaction price-based realized range estimator of daily volatility in the presence of bid-ask bounce and non-trading. The adjustment is an extension of the estimator proposed in Christensen et al. (2009). We relax the assumption that all intra-day high (low) transaction prices are at the ask (bid) quote. Using data-based simulations we obtain estimates of the probability that a given intraday range is (upward or downward) biased or not, which we use for a more refined bias-adjustment of the realized range estimator. Both Monte Carlo simulations and an empirical application involving a liquid and a relatively illiquid S&P500 constituent demonstrate that ex post measures and ex ante forecasts based on the heuristically adjusted realized range compare favorably to existing bias-adjusted (two time scales) realized range and (two time scales) realized variance estimators.Realized mixed-frequency factor models for vast dimensional covariance estimation
http://repub.eur.nl/pub/37470/
Tue, 23 Oct 2012 00:00:01 GMT<div>K. Bannouh</div><div>M.P.E. Martens</div><div>R.C.A. Oomen</div><div>D.J.C. van Dijk</div>
We introduce a Mixed-Frequency Factor Model (MFFM) to estimate vast dimensional covari- ance matrices of asset returns. The MFFM uses high-frequency (intraday) data to estimate factor (co)variances and idiosyncratic risk and low-frequency (daily) data to estimate the factor loadings. We propose the use of highly liquid assets such as exchange traded funds (ETFs) as factors. Prices for these contracts are observed essentially free of microstructure noise at high frequencies, allowing us to obtain precise estimates of the factor covariances. The factor loadings instead are estimated from daily data to avoid biases due to market microstructure effects such as the relative illiquidity of individual stocks and non-synchronicity between the returns on factors and stocks. Our theoretical, simulation and empirical results illustrate that the performance of the MFFM is excellent, both compared to conventional factor models based solely on low-frequency data and to popular realized covariance estimators based on high-frequency data.Residual Momentum
http://repub.eur.nl/pub/22252/
Wed, 01 Jun 2011 00:00:01 GMT<div>D.C. Blitz</div><div>J.J. Huij</div><div>M.P.E. Martens</div>
Conventional momentum strategies exhibit substantial time-varying exposures to the Fama and French factors. We show that these exposures can be reduced by ranking stocks on residual stock returns instead of total returns. As a consequence, residual momentum earns risk-adjusted profits that are about twice as large as those associated with total return momentum; is more consistent over time; and less concentrated in the extremes of the cross-section of stocks. Our results are inconsistent with the notion that the momentum phenomenon can be attributed to a priced risk factor or market microstructure effects.Residual momentum
http://repub.eur.nl/pub/23275/
Sat, 01 Jan 2011 00:00:01 GMT<div>D.C. Blitz</div><div>J.J. Huij</div><div>M.P.E. Martens</div>
Conventional momentum strategies exhibit substantial time-varying exposures to the Fama and French factors. We show that these exposures can be reduced by ranking stocks on residual stock returns instead of total returns. As a consequence, residual momentum earns risk-adjusted profits that are about twice as large as those associated with total return momentum; is more consistent over time; and less concentrated in the extremes of the cross-section of stocks. Our results are inconsistent with the notion that the momentum phenomenon can be attributed to a priced risk factor or market microstructure effects.Mutual Funds |Selection based on Fund Characteristics
http://repub.eur.nl/pub/21590/
Wed, 01 Sep 2010 00:00:01 GMT<div>D.P. Budiono</div><div>M.P.E. Martens</div>
The popular investment strategy in the literature is to use only past performance to select mutual funds. We investigate whether an investor can select superior funds by additionally using fund characteristics. After considering the fund fees, we find that combining information on past performance, turnover ratio, and ability produces a yearly excess net return of 8.0%, whereas an investment strategy that uses only past performance generates 7.1%. Adjusting for systematic risks, and then using fund characteristics, increases yearly alpha significantly from 0.8% to 1.7%. The strategy that also uses fund characteristics requires less turnover.Spread decomposition with common spread components
http://repub.eur.nl/pub/19655/
Tue, 18 May 2010 00:00:01 GMT<div>T. Henker</div><div>M.P.E. Martens</div>
Purpose: This paper aims to incorporate a market wide buying and selling pressure cost component into a spread decomposition model as spread cost component. Design/methodology/approach: The paper extends a commonly used trade indicator spread decomposition model to include a component common to all stocks of a specialist firm and a market wide component common to all stocks. Findings: Strong evidence is found that specialists consider this common factor cost component when they set bid and ask quotes. Some specialist firms also take the next logical step and specifically manage their firm wide stock inventories. The common factor is in percentage terms largest for securities with the highest trade frequencies. Research limitations/implications: The relative importance of the common factor spread component decreases as the pricing grid becomes finer, but remains highly significant under the decimal trading regime. Originality/value: This is the first study to document not-security-specific spread cost components that are common to all stocks for which a specialist firm makes markets and to all stocks in the market. Using the model it is shown that market wide uncertainty translates into spreads of individual securities.Asymmetric effects of federal funds target rate changes on S&P100 stock returns, volatilities and correlations
http://repub.eur.nl/pub/18568/
Thu, 01 Apr 2010 00:00:01 GMT<div>H. Chulia-Soler</div><div>M.P.E. Martens</div><div>D.J.C. van Dijk</div>
We study the effects of FOMC announcements of federal funds target rate decisions on individual stock returns, volatilities and correlations at the intraday level. For all three characteristics we find that the stock market responds differently to positive and negative target rate surprises. First, the average response to positive surprises (that is, bad news for stocks) is larger. Second, in case of bad news the mere occurrence of a surprise matters most, whereas for good news its magnitude is more important. These new insights are possible due to the use of high-frequency intraday data.Range-based covariance estimation using high-frequency data: The realized co-range
http://repub.eur.nl/pub/17214/
Thu, 08 Oct 2009 00:00:01 GMT<div>K. Bannouh</div><div>D.J.C. van Dijk</div><div>M.P.E. Martens</div>
We introduce the realized co-range, a novel estimator of the daily covariance between asset returns based on intraday high-low price ranges. In an ideal world, the co-range is five times more efficient than the realized covariance, which uses cross-products of intraday returns, when sampling at the same frequency. In Monte Carlo simulations, we find that for plausible levels of bid-ask bounce, infrequent trading and nonsynchronous trading, the realized co-range still improves upon the realized covariance. In a volatility timing strategy for S&P500, bond and gold futures, we find that the co-range estimates are less noisy, which results in lower transaction costs and higher Sharpe ratios.Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements
http://repub.eur.nl/pub/18152/
Wed, 01 Apr 2009 00:00:01 GMT<div>M.P.E. Martens</div><div>D.J.C. van Dijk</div><div>M.D. de Pooter</div>
We evaluate the forecasting performance of time series models for realized volatility, which accommodate long memory, level shifts, leverage effects, day-of-the-week and holiday effects, as well as macroeconomic news announcements. Applying the models to daily realized volatility for the S&P 500 futures index, we find that explicitly accounting for these stylized facts of volatility improves out-of-sample forecast accuracy for horizons up to 20 days ahead. Capturing the long memory feature of realized volatility by means of a flexible high-order AR-approximation instead of a parsimonious but stringent fractionally integrated specification also leads to improvements in forecast accuracy, especially for longer horizon forecasts.Robust Optimization of the Equity Momentum Strategy
http://repub.eur.nl/pub/14943/
Sun, 01 Feb 2009 00:00:01 GMT<div>A. van Oord</div><div>M.P.E. Martens</div><div>H.K. van Dijk</div>
Quadratic optimization for asset portfolios often leads to error maximization, with optimizers zooming in on large errors in the predicted inputs, that is, expected returns and risks. The consequence in most cases is a poor real-time performance. In this paper we show how to improve real-time performance of the popular equity momentum strategy with robust optimization in an empirical application involving 1500-2500 US stocks over the period 1963-2006. We also show that popular procedures like Bayes-Stein estimated expected returns, shrinking the covariance matrix and adding weight constraints fail in such a practical caseRange-based covariance estimation using high-frequency data: The realized co-range
http://repub.eur.nl/pub/10904/
Tue, 15 Jan 2008 00:00:01 GMT<div>K. Bannouh</div><div>D.J.C. van Dijk</div><div>M.P.E. Martens</div>
We introduce the realized co-range, utilizing intraday high-low
price ranges to estimate asset return covariances. Using simulations
we find that for plausible levels of bid-ask bounce and infrequent
and non-synchronous trading the realized co-range improves upon the
realized covariance, which uses cross-products of intraday returns.
One advantage of the co-range is that in an ideal world it is five
times more efficient than the realized covariance when sampling at
the same frequency. The second advantage is that the upward bias due
to bid-ask bounce and the downward bias due to infrequent and
non-synchronous trading partially offset each other. In a volatility
timing strategy for S\\&P500, bond and gold futures we find that the
co-range estimates are less noisy as exemplified by lower
transaction costs and also higher Sharpe ratios when using more
weight on recent data for predicting covariances.Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?
http://repub.eur.nl/pub/18655/
Tue, 01 Jan 2008 00:00:01 GMT<div>M.D. de Pooter</div><div>M.P.E. Martens</div><div>D.J.C. van Dijk</div>
This article investigates the merits of high-frequency intraday data when forming mean-variance efficient stock portfolios with daily rebalancing from the individual constituents of the S&P 100 index. We focus on the issue of determining the optimal sampling frequency as judged by the performance of these portfolios. The optimal sampling frequency ranges between 30 and 65 minutes, considerably lower than the popular five-minute frequency, which typically is motivated by the aim of striking a balance between the variance and bias in covariance matrix estimates due to market microstructure effects such as non-synchronous trading and bid-ask bounce. Bias-correction procedures, based on combining low-frequency and high-frequency covariance matrix estimates and on the addition of leads and lags do not substantially affect the optimal sampling frequency or the portfolio performance. Our findings are also robust to the presence of transaction costs and to the portfolio rebalancing frequency.The Effects of Federal Funds Target Rate Changes on S&P100 Stock Returns, Volatilities, and Correlations
http://repub.eur.nl/pub/10610/
Thu, 25 Oct 2007 00:00:01 GMT<div>H. Chulia-Soler</div><div>M.P.E. Martens</div><div>D.J.C. van Dijk</div>
We study the impact of FOMC announcements of Federal funds target rate decisions on individual stock prices at the intraday level. We find that the returns, volatilities and correlations of the S&P100 index constituents only respond to the surprise component in the announcement, as measured by the change in the Federal funds futures rate. For example, an unexpected 25 basis points increase of the target rate leads on average to a 113 basis points negative market return within five minutes after the announcement. It also increases market volatility during the 60-minute window around the announcement with 147 basis points. Positive surprises, meaning bad news for stocks, provoke a stronger reaction than negative surprises. Market participants also respond differently to good and bad news. In case of bad news for stocks the fact that there is a surprise matters most, whereas in case of good news the magnitude of the surprise is more important. Across sectors, Financials and IT show the strongest response to target rate surprises.Measuring volatility with the realized range
http://repub.eur.nl/pub/11121/
Tue, 01 May 2007 00:00:01 GMT<div>D.J.C. van Dijk</div><div>M.P.E. Martens</div>
Realized variance, being the summation of squared intra-day returns, has quickly gained popularity as a measure of daily volatility. Following Parkinson [1980. The extreme value method for estimating the variance of the rate of return. Journal of Business 53, 61–65] we replace each squared intra-day return by the high–low range for that period to create a novel and more efficient estimator called the realized range. In addition, we suggest a bias-correction procedure to account for the effects of microstructure frictions based upon scaling the realized range with the average level of the daily range. Simulation experiments demonstrate that for plausible levels of non-trading and bid–ask bounce the realized range has a lower mean-squared error than the realized variance, including variants thereof that are robust to microstructure noise. Empirical analysis of the S&P500 index-futures and the S&P100 constituents confirms the potential of the realized range.Measuring volatility with the realized range
http://repub.eur.nl/pub/7582/
Tue, 28 Feb 2006 00:00:01 GMT<div>M.P.E. Martens</div><div>D.J.C. van Dijk</div>
Realized variance, being the summation of squared intra-day returns,
has quickly gained popularity as a measure of daily volatility.
Following Parkinson (1980) we replace each squared intra-day return
by the high-low range for that period to create a novel and more
efficient estimator called the realized range. In addition we
suggest a bias-correction procedure to account for the effects of
microstructure frictions based upon scaling the realized range with
the average level of the daily range. Simulation experiments
demonstrate that for plausible levels of non-trading and bid-ask
bounce the realized range has a lower mean squared error than the
realized variance, including variants thereof that are robust to
microstructure noise. Empirical analysis of the S&P500
index-futures and the S&P100 constituents confirm the potential of
the realized range.Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data - But Which Frequency To Use?
http://repub.eur.nl/pub/6959/
Wed, 21 Sep 2005 00:00:01 GMT<div>M.D. de Pooter</div><div>M.P.E. Martens</div><div>D.J.C. van Dijk</div>
This paper investigates the merits of high-frequency intraday data when forming minimum variance portfolios and minimum tracking error portfolios with daily rebalancing from the individual constituents of the S&P 100 index. We focus on the issue of determining the optimal sampling frequency, which strikes a balance between variance and bias in covariance matrix estimates due to market microstructure effects such as non-synchronous trading and bid-ask bounce. The optimal sampling frequency typically ranges between 30- and 65-minutes, considerably lower than the popular five-minute frequency. We also examine how bias-correction procedures, based on the addition of leads and lags and on scaling, and a variance-reduction technique, based on subsampling, affect the performance.Index futures arbitrage before and after the introduction of sixteenths on the NYSE
http://repub.eur.nl/pub/61853/
Wed, 01 Jun 2005 00:00:01 GMT<div>T. Henker</div><div>M.P.E. Martens</div>
We find that market efficiency increased and the arbitrage link between index futures and the stock market strengthened after June 24, 1997, when the New York Stock Exchange reduced the minimum change for stock prices and quotes from an eighth to a sixteenth of a dollar. There has been a substantial increase in the number of arbitrage trades reported to the Securities and Exchange Commission (SEC) since the reduction in the minimum price increment. The average number of stocks traded and the average dollar amount underlying each arbitrage trade increases and decreases, respectively. The average index futures mispricing error (MPE) that triggers arbitrage is lower and reverts to zero more quickly.Predicting financial volatility: High-frequency time-series forecasts vis-à-vis implied volatility
http://repub.eur.nl/pub/56691/
Mon, 01 Nov 2004 00:00:01 GMT<div>M.P.E. Martens</div><div>J. Zein</div>
Recent evidence suggests option implied volatilities provide better forecasts of financial volatility than time-series models based on historical daily returns. In this study both the measurement and the forecasting of financial volatility is improved using high-frequency data and long memory modeling, the latest proposed method to model volatility. This is the first study to extract results for three separate asset classes, equity, foreign exchange, and commodities. The results for the S&P 500, YEN/USD, and Light, Sweet Crude Oil provide a robust indication that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with and even outperform implied volatility.Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity
http://repub.eur.nl/pub/6630/
Sat, 05 Jun 2004 00:00:01 GMT<div>M.P.E. Martens</div><div>D.J.C. van Dijk</div><div>M.D. de Pooter</div>
The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model to realized volatilities of the S&P 500 stock index and three exchange rates produces forecasts that clearly improve upon the ones obtained from a linear ARFIMA model and from conventional time-series models based on daily returns, treating volatility as a latent variable.