Non-synchronous Trading Research Articles

Tail risk aversion and backwardation of index futures

We show that tail risk aversion, proxied by the skewness risk premium implied from the SSE 50 ETF options market, explains a significant proportion of the unusually deep backwardation of index futures during the 2015 Chinese stock market crash, while traditional factors such as non-synchronous trading, spot return, volatility and liquidity, all fail to explain the backwardation. These empirical results imply that investors' concern over the crash risk causes speculators to charge a high ‘insurance premium’ on hedgers. On the other hand, short-sale constraints, namely high security borrowing costs and other barriers, prevent reverse arbitrage such that the deep backwardation of index futures persists.

High frequency correlation dynamics and day-of-the-week effect: A score-driven approach in an emerging market stock exchange

Intraday correlation dynamics poses challenges to financial econometricians, especially in recently popular high frequency domain, due to non-synchronous trading and market microstructure noise. Traditional models fail to address the issues inherent to the nature of the data, which is riddled with noisy signals and missing values. We employ a recently developed method based on Generalized Autoregressive Score (GAS) framework and State-Space Modeling to remedy these characteristics of high frequency data and estimate intraday correlations in Turkish equity market Borsa Istanbul. Our findings reveal that average intraday conditional correlation rises as trading commences and lingers around certain altitude for some time, with the eigenvalues associated with market factor becoming progressively more dominant. An upward trend closes out the trading day on Mondays, which we attribute to the US market opening, whereas the rest of the week does not show a generalizable closing-time effect. Assessment of the findings across different market conditions and days of the week reveals elevated correlation levels in volatile markets as well as a distinguishable path for the beginning of the week. Beyond the scholarly contribution, the methodology can be used as a nowcasting tool and the findings are of interest to various parties like high-frequency traders, risk and portfolio managers and regulatory agencies in formulating their high frequency trading practices, hedging, portfolio construction schemes and margin requirements, respectively.

Transactions of the National Pension Service of Korea in the KOSPI200 futures market

It is known that the National Pension Service (NPS) of Korea contributes to the market stability because it tends to pursue the negative feedback trading strategy in the Korean stock market. While many studies deal with institutional investors’ trading in the financial derivatives market, the NPS’s trading in the derivatives market is rarely studied. Using the NPS’s trading data for the period from January 2010 to March, 2020, the authors examine the transactions of the NPS in the KOSPI200 futures market. We find that the NPS’s net investment flow (NIF) in KOSPI200 futures is negatively associated with the past returns of KOSPI200 futures and the KOPI200 index. However, we also find that the NPS’s NIF of KOSPI200 futures is positively associated with its NIF in KOSPI200 stocks. Along with the legal restriction on the NPS’s trading in the derivatives market, the result suggests that the NPS uses KOSPI200 futures to deviate the problems related to non-synchronous trading in the spot market. To the best of our knowledge, this paper is the first study of the NPS’s transactions of KOSPI200 futures. The paper suggests that the NPS does not trade KOSPI200 futures for hedging or arbitrage profit but for complementing its transactions in the spot market of KOSPI200 stocks.

Features of the ripple effect in the US regional housing markets: a viewpoint of nonsynchronous trading

ABSTRACT The present study used a new perspective that integrates the commonly observed market characteristics to analyse why the ripple effect is formed. This study proposed a model to infer that in a regional housing market with a relatively low trading probability, low trading volume, high price volatility, and low price efficiency coexist and cause the market to lag behind other regions in information response, forming a ripple effect. The empirical tests used data cover four regions of the United States (Northeast, Midwest, South, and West) between January 1999 and May 2020, and showed that the housing market in the South had the greatest liquidity and the lowest housing price volatility, however, housing prices were more informative in the South region. The empirical results also revealed the characteristics of the ripple effect in the US housing market. Changes in house prices occur earlier in the South, and information is transmitted from the South to the Northeast.

The Night and Day of Amihud’s (2002) Liquidity Measure

Abstract Amihud’s stock (il)liquidity measure averages daily ratios of the absolute close-to-close return to dollar volume, including overnight returns. Our modified measure uses open-to-close returns matching return and trading volume measurement windows. It is more strongly correlated with trading-cost measures (by 8%–37%) and better explains cross-sections of returns, doubling estimated liquidity premiums. Using nonsynchronous trading near close, we show overnight returns are primarily information driven: including them in Amihud’s proxy for price impacts of trading magnifies measurement error, understating liquidity premiums. Our modification helps wherever Amihud’s measure is required. Our measures are publicly available for 1964–2019 and can be updated. (JEL G12, G14) Received June 2, 2020; editorial decision September 11, 2020 by Editor Jeffrey Pontiff.

Inference for time-varying lead\u2013lag relationships from ultra-high-frequency data

A new approach for modeling lead–lag relationships in high-frequency financial markets is proposed. The model accommodates non-synchronous trading and market microstructure noise as well as intraday variations of lead–lag relationships, which are essential for empirical applications. A simple statistical methodology for analyzing the proposed model is presented, as well. The methodology is illustrated by an empirical study to detect lead–lag relationships between the S&P 500 index and its two derivative products.

Country governance and international equity returns

Monthly returns in countries with strong governance lead monthly returns in weak governance countries. This predictability holds in and out-of-sample at both the group and individual country levels. Moreover, the predictability is not fully explained by other related possible sources of cross-country predictability such as differences in country development, political risk, size, liquidity, short-selling constraints, the predictive ability of U.S. equity returns, or non-synchronous trading. It appears that equity returns in different countries react to value-relevant world information at different speeds based on their levels of country governance.

Quantile LASSO in arbitrage-free option markets

The option price function and the implied volatility surface are both key tools for the derivative pricing strategies and the financial market analysis. Modern and sophisticated methods are used but their credibility suffered due to the financial crisis in 2007–2010. Instead, a method based on a standard semiparametric smoothing is proposed and the overall complexity and robustness (with respect to various anomalies, such as bid-ask spreads, discrete ticks in price, non-synchronous trading, or even heavy tailed error distributions) is achieved by using the conditional quantile estimation. The overestimation and the sparsity principle are adopted to introduce additional flexibility and the LASSO-type penalty and the set of well-defined linear constraints are employed to produce the final estimate which complies with the arbitrage-free criteria dictated by the financial theory. The theoretical results of the model are discussed, finite sample properties are investigated via a simulation study and a practical application of the proposed method is illustrated for the Apple Inc. (AAPL) call options.

Even in Emerging Markets, Technical Trading is Hazardous to Your Wealth

ABSTRACT This paper tests for the ability of a variety of technical indicators to generate excess returns at the individual stock level in the seven emerging and frontier markets of the Gulf region. While technical indicators show some early profitability promise, after controlling for the data snooping bias using the False Discovery Rate (FDR) methodology and non-synchronous trading, we fail to find any predictive ability or profitability for technical analysis. We arrive at a similar finding when assessing the risk-adjusted performance of a portfolio composed of stocks chosen based on technical indicators. The findings go to show the failure of technical analysis on the stock level. The findings are also evidence of the Gulf region markets being at least weak-form efficient and carry implications for investors choice of investment tools.

Time-Varying Spillovers between Currency and Stock Markets in the USA: Historical Evidence From More than Two Centuries

In this paper, we analyze time-varying causality between the dollar-pound exchange rate and S&P 500 returns over the monthly period of September, 1791 to September, 2019. Based on a Dynamic Conditional Correlation-Multivariate Generalised Autoregressive Conditional Heterosckedasticity (DCC-MGARCH) framework, we find that evidence of unidirectional causality between the two returns is in general weak, and primarily restricted to the period following the breakdown of the Bretton Woods agreement. However, instantaneous spillovers across the returns of these two markets is quite strong, which in turn tends to suggest the existence of nonsynchronous trading and also high-frequency causal dependency, with the latter confirmed based on daily data covering 3 January 1900 – 4 October 2019. Moreover, the underlying DCC reveals that there is actually portfolio diversification opportunities for investors. Finally, an analysis of the second moments reveal much stronger evidence of volatility spillovers between these two assets, when compared to the return linkages. This result has important implications from the perspective of policy making aiming to reduce the impact of uncertainty on the real economy.

The asymmetric high-frequency volatility transmission across international stock markets

We construct a MHAR-DCC model to investigate the high-frequency volatility transmission across international stock markets. We use the overnight volatility estimator to eliminate the effects of non-synchronous trading problem. We analyze the asymmetric volatility transmission effects across the international stock markets. The empirical results suggest that the periods when the total spillover index increases to high levels correspond to the periods when the market volatility is high. The volatility transmission effect in the US and Singapore stock markets exhibit the normal leverage effect, while the volatility transmissions of Japanese and Hong Kong stock markets exhibit the reverse leverage effect.

Cointegration, Price-Adjustment Delays, and Optimal Hedge Ratio in the Precious Metal Markets

Firms seeking to apply hedge accounting treatment under the Accounting Standards Codification Topic 815 must demonstrate higher hedge effectiveness, for which the regression analysis is commonly used as a testing method. An autoregressive distributed lag (ARDL) model is adopted in this article to examine the hedge effectiveness in the presence of nonsynchronous trading of spot and futures contracts as well as a long-run cointegrating relationship between their prices. Using precious metal market data, our study empirically demonstrates that a hedge ratio estimated with a conventional OLS model tends to be downwardly biased. Our finding also indicates that the omitted-variable bias becomes apparent only when the difference between the transaction frequencies in spot and futures markets is significantly large.

The Night and Day of Amihud's (2002) Liquidity Measure

Amihud's (2002) stock (il)liquidity measure averages daily ratios of absolute close-to-close return to dollar volume, including overnight returns, while trading volumes come from regular hours. Our modified measure addresses this mismatch by using open-to-close returns. It is more strongly correlated with standard trading-cost measures (by 8-37%) and it better explains cross-sections of returns, doubling estimated liquidity premia. Using non-synchronous trading near close as an instrument reveals that overnight returns are primarily information-driven and orthogonal to price impacts of trading: including them in liquidity proxies magnifies measurement error, understating liquidity premia. Our modification helps wherever use of Amihud's measure is required. Our measures are publicly available to researchers for the years 1964-2019, and can be updated in future years using CRSP and COMPUSTAT.

The Effect of Liquidity and Estimation Period on the Beta Bias: Evidence in the Korean Stock Market

Non-synchronous trading may cause a serious bias in the beta estimation using daily closing prices. This study empirically examines whether non-synchronous trading due to illiquidity induces a bias on estimated betas. We estimate betas using i) the ordinary least squares method and ii) the Scholes and Williams method. Defining the beta spread as the bias, we run the Fama-MacBeth type of regression model to investigate the determinants of the bias. The main findings are as follows. First, when the effect of non-synchronous trading is not controlled for, the beta tends to be biased upward. Second, we find that the shorter estimation period we use for the estimation, the more the upward bias tends to be magnified. Third, illiquidity, measured by turnover rate, Amihud’s illiquidity, or the number of zero returns, is one of the factors that cause the beta estimation bias. Fourth, we find that the bias for stocks with large market capitalization is high. Our results suggest that particular care needs to be done when estimating market models with illiquid daily data.

A nonparametric eigenvalue-regularized integrated covariance matrix estimator for asset return data

In high-frequency data analysis, the extreme eigenvalues of a realized covariance matrix are biased when its dimension p is large relative to the sample size n. Furthermore, with non-synchronous trading and contamination of microstructure noise, we propose a nonparametrically eigenvalue-regularized integrated covariance matrix estimator (NERIVE) which does not assume specific structures for the underlying integrated covariance matrix. We show that NERIVE is positive definite in probability, with extreme eigenvalues shrunk nonlinearly under the high dimensional framework p∕n→c>0. We also prove that in portfolio allocation, the minimum variance optimal weight vector constructed using NERIVE has maximum exposure and actual risk upper bounds of order p−1∕2. Incidentally, the same maximum exposure bound is also satisfied by the theoretical minimum variance portfolio weights. All these results hold true also under a jump–diffusion model for the log-price processes with jumps removed using the wavelet method proposed in Fan and Wang (2007). They are further extended to accommodate the existence of pervasive factors such as a market factor under the setting p3∕2∕n→c>0. The practical performance of NERIVE is illustrated by comparing to the usual two-scale realized covariance matrix as well as some other nonparametric alternatives using different simulation settings and a real data set.

A Storm But No Damage? A Two-Country Equity and Currency Market Perspective of Brexit

This paper analyses the impact of the June 23, 2016 Brexit vote on the British and Australian currency and equity markets. The two markets are particularly interesting as they were both strongly impacted by the Brexit vote, have cross-listed stocks and non-overlapping trading hours. Whilst the currency changes were immediate in both countries due to continuous 24-hour trading, the equity market effects occurred sequentially, led by the ASX and followed by the LSE. The analysis demonstrates that equity markets (in contrast to the currency markets) overreacted to the Brexit vote as the initial market reaction was fully corrected within five trading days. More importantly perhaps, the findings also indicate that non-synchronous trading can distort and delay price adjustments.

Return spillovers around the globe: A network approach

We study the connectedness of a sample of 40 stock markets across five continents using daily closing prices and return spillovers based on Granger causality. All possible 1560 return spillovers between 40 markets create a complex network of relationships between equity markets around the world. Apart from analyzing the topological and time-varying properties of the created networks, we also identify the determinants of the connectedness of equity markets over time. Adjusting for non-synchronous trading, our modelling approach leads to evidence that the probability of return spillover from a given stock market to other markets increases with market volatility and market size and decreases with higher foreign exchange volatility. We empirically show that the temporal proximity between closing hours is important for information propagation; therefore, choosing markets that trade during similar hours bears an additional risk to investors because the probability of return spillovers increases.

Degree of Integration Between Brent Oil Spot and Futures Markets: Intraday Evidence

ABSTRACTWe investigate the integration of oil spot and futures markets using matched, intraday data to avoid nonsynchronous trading issues. Our evidence indicates highly integrated spot and futures markets. Economic shocks that arise in spot markets are quickly transmitted to the futures markets approximately one-for-one. Most of the reaction occurs within minutes. Similarly, economic shocks arriving in futures markets are transmitted to spot markets one-for-one, once again, within minutes consistent with market efficiency. In general, our findings indicate well-functioning, well-integrated spot and futures oil markets that are informationally efficient and that perform the functions of both price discovery and risk transfer. To the best of our knowledge, this is the first article to work with precisely matched customized data in futures markets, specifically oil futures markets.

Inference for Time-Varying Lead-Lag Relationships from Ultra High Frequency Data

A new approach for modeling lead-lag relationships in high frequency financial markets is proposed. The model is accommodated to non-synchronous trading and market microstructure noise as well as intraday variations of lead-lag relationships, which are essential for empirical applications. A simple statistical methodology for analyzing the proposed model is presented as well. The methodology is illustrated by an empirical study to detect lead-lag relationships between the S&P 500 index and its two derivative products.

Asymmetry Effects in Volatility on the Major European Stock Markets: the EGARCH Based Approach

The main goal of this paper is to investigate the asymmetric impact of innovations on volatility in the case of the largest European stock markets in the United Kingdom, France and Germany by using the EGARCH based approach. The sample period begins in January 2003 and ends in December 2016, and it includes the 2007 U.S. subprime crisis. The robustness analysis of empirical results is provided with respect to the whole sample and three adjacent subsamples, each of equal size: 1) the pre-crisis, 2) the Global Financial Crisis (GFC) and 3) the post-crisis periods. The GFC periods are formally detected by using a statistical method of dividing market states into bullish and bearish markets. Moreover, the common trading window procedure is employed to avoid the nonsynchronous trading problem in the group of investigated markets and to get the overlapping information set. We estimate univariate EGARCH models based on daily percentage logarithmic returns of major stock market indexes: FTSE100 (London), CAC40 (Paris), and DAX (Frankfurt). Pronounced negative asymmetry effects in volatility are presented in the case of all markets and are rather robust to the choice of the period. Our findings are consistent with the literature and suggest that the major European stock markets are more sensitive to ’bad’ than ‘good’ news.