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on Market Microstructure |
| By: | Alvaro Cartea; Dimitrios Karyampas |
| Abstract: | The contribution of this paper is two-fold. First we show how to estimate the volatility of high frequency log-returns where the estimates are not a affected by microstructure noise and the presence of Lévy-type jumps in prices. The second contribution focuses on the relationship between the number of jumps and the volatility of log-returns of the SPY, which is the fund that tracks the S&P 500. We employ SPY high frequency data (minute-by-minute) to obtain estimates of the volatility of the SPY log-returns to show that: (i) The number of jumps in the SPY is an important variable in explaining the daily volatility of the SPY log-returns; (ii) The number of jumps in the SPY prices has more explanatory power with respect to daily volatility than other variables based on: volume, number of trades, open and close, and other jump activity measures based on Bipower Variation; (iii) The number of jumps in the SPY prices has a similar explanatory power to that of the VIX, and slightly less explanatory power than measures based on high and low prices, when it comes to explaining volatility; (iv) Forecasts of the average number of jumps are important variables when producing monthly volatility forecasts and, furthermore, they contain information that is not impounded in the VIX. |
| Keywords: | Volatility forecasts, High-frequency data, Implied volatility, VIX, Jumps, Microstructure noise |
| JEL: | C53 G12 G14 C22 |
| Date: | 2009–12 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wbrepe:wb097508&r=mst |
| By: | Alvaro Cartea; Dimitrios Karyampas |
| Abstract: | Using high frequency data for the price dynamics of equities we measure the impact that market microstructure noise has on estimates of the: (i) volatility of returns; and (ii) variance-covariance matrix of n assets. We propose a Kalman-filter-based methodology that allows us to deconstruct price series into the true efficient price and the microstructure noise. This approach allows us to employ volatility estimators that achieve very low Root Mean Squared Errors (RMSEs) compared to other estimators that have been proposed to deal with market microstructure noise at high frequencies. Furthermore, this price series decomposition allows us to estimate the variance covariance matrix of $n$ assets in a more efficient way than the methods so far proposed in the literature. We illustrate our results by calculating how microstructure noise affects portfolio decisions and calculations of the equity beta in a CAPM setting. |
| Keywords: | Volatility estimation, High-frequency data, Market microstructure theory, Covariation of assets, Matrix process, Kalman filter |
| JEL: | G12 G14 C22 |
| Date: | 2009–12 |
| URL: | http://d.repec.org/n?u=RePEc:cte:wbrepe:wp097609&r=mst |
| By: | Huimin Chung; Jie Lu; Bruce Mizrach |
| Abstract: | This paper investigates the market microstructure of the Shanghai and Shenzhen Stock Ex- changes. The two major Chinese stock markets are pure order-driven trading mechanisms without market makers, and we analyze empirically both limit order books. We begin our empirical model- ing using the vector autoregressive model of Hasbrouck and extend the model to incorporate other information in the limit order book. We also study the market impact on A shares, B shares and H shares, and analyze how the market impact of stocks varies cross sectionally with market capital- ization, tick frequencies, and turnover. Furthermore, we distinguish the market impacts of small, average and block trades, and conclude that the market impacts of small trades are signi?cantly lower than those of other trades. |
| Keywords: | limit order book; Chinese stock market; microstructure; VAR model |
| JEL: | A |
| Date: | 2009–09 |
| URL: | http://d.repec.org/n?u=RePEc:cqe:wpaper:0109&r=mst |
| By: | Neil Shephard (Oxford-Man Institute and Department of Economics, University of Oxford); Kevin Sheppard (Department of Economics and Oxford-Man Institute, University of Oxford) |
| Abstract: | This paper studies in some detail a class of high frequency based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realized measures constructed from high frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analyse a model based bootstrap which allow us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models. |
| Keywords: | ARCH models; bootstrap; missing data; multiplicative error model; multistep ahead prediction; non-nested likelihood ratio test; realised kernel; realised volatility. |
| Date: | 2009–07–10 |
| URL: | http://d.repec.org/n?u=RePEc:nuf:econwp:0903&r=mst |
| By: | Nathaniel Frank (Oxford-Man Institute and Department of Economics, University of Oxford) |
| Abstract: | In this paper we analyse market co-movements during the global financial crisis. Using high frequency data and accounting for market microstructure noise and non-synchronous trading, interdependencies between differing as-set classes such as equity, FX, fixed income, commodity and energy securities are quantified. To this end multivariate realised kernels and GARCH models are employed. We find that during the current period of market dislocations and times of increased risk aversion, assets have become more correlated when applying these intra-day measures. FX pairs seemingly lead the other variables, but commodities remain entirely unaffected. |
| Keywords: | Financial crisis, high frequency data, kernel based estimation |
| JEL: | C32 E44 |
| Date: | 2009–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:nuf:econwp:0904&r=mst |
| By: | McAleer, M.; Medeiros, M.C. (Erasmus Econometric Institute) |
| Abstract: | In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed in the paper. |
| Keywords: | financial econometrics;volatility forecasting;neural networks;nonlinear models;realized volatility;bagging |
| Date: | 2009–11–24 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureir:1765017303&r=mst |
| By: | Silvia Muzzioli |
| Abstract: | The aim of this paper is twofold: to investigate how the information content of implied volatility varies according to moneyness and option type, and to compare option-based forecasts with historical volatility in order to see if they subsume all the information contained in historical volatility. The different information content of implied volatility is examined for the most liquid at-the-money and out-of-the-money options: put (call) options for strikes below (above) the current underlying asset price, i.e. the ones that are usually used as inputs for the computation of the smile function. In particular, since at-the-money implied volatilities are usually inserted in the smile function by computing some average of both call and put implied ones, we investigate the performance of a weighted average of at-the-money call and put implied volatilities with weights proportional to trading volume. Two hypotheses are tested: unbiasedness and efficiency of the different volatility forecasts. The investigation is pursued in the Dax index options market, by using synchronous prices matched in a one-minute interval. It was found that the information content of implied volatility has a humped shape, with out-of-the-money options being less informative than at-the-money ones. Overall, the best forecast is at-the-money put implied volatility: it is unbiased (after a constant adjustment) and efficient, in that it subsumes all the information contained in historical volatility. |
| Keywords: | Implied Volatility; Volatility Smile; Volatility forecasting; Option type |
| JEL: | G13 G14 |
| Date: | 2009–12 |
| URL: | http://d.repec.org/n?u=RePEc:mod:wcefin:09122&r=mst |
| By: | Martin T. Bohl; Christian A. Salm; Bernd Wilfling |
| Abstract: | This paper investigates the impact of introducing index futures trading on the volatility of the underlying stock market. We exploit a unique institutional setting in which presumably uninformed individuals are the dominant trader type in the futures markets. This enables us to investigate the destabilization hypothesis more accurately than previous studies do and to provide evidence for or against the in uence of individuals trading in index futures on spot market volatility. To overcome econometric shortcomings of the existing literature we employ a Markov-switching-GARCH approach to endogenously identify distinct volatility regimes. Our empirical evidence for Poland surprisingly suggests that the introduction of index futures trading does not destabilize the spot market. This nding is robust across 3 stock market indices and is corroborated by further analysis of a control group. |
| Keywords: | Individual Investors, Uninformed Trading, Stock Index Futures, Emerging Capital Markets, Stock Market Volatility, Markov-Switching-GARCH Model |
| JEL: | C32 G10 G14 G20 |
| Date: | 2009–10 |
| URL: | http://d.repec.org/n?u=RePEc:cqe:wpaper:0609&r=mst |