Shock Transfer in Futures and Spot Markets: An Agent-Based Simulation Modelling Method

There have been heated debates about the role of stock index futures in the financial market, especially during the crash periods. In this paper, a multiagent spot-futures market model is developed to analyze the micromechanism of shock transfer across spot and futures markets. We assume that there are two stocks and one stock index futures contract in the spot-futures market. Agents are heterogeneous, including fundamentalists, chartists, noise traders, and arbitragers. The spot market and the futures market are linked by arbitragers. The simulation results show that our spot-futures market model can reproduce various important stylized facts, including the price co-movement between stock index prices and index futures prices and the fat-tailed distribution of the returns of risky assets and the basis. Further analysis shows that when we introduce an exogenous fundamental shock to one of the stocks, the backwardation phenomenon appears in the futures market and the shock is widespread across the whole market by means of index futures. Moreover, the backwardation gradually disappears when the number of arbitragers increases. Besides, when there are few arbitragers or when there are sufficient arbitragers, shocks cannot be transferred to other stocks via the futures market, while an intermediate level of arbitrage will amplify the shock transfer and hurt market stability. These findings underscore that arbitragers play an important role in spot-futures market interaction and shock transfer, and adequate arbitrage trading during crises may help eliminate the positive basis and halt the further spread of the crises.

Hedge Fund Treasury Trading and Funding Fragility: Evidence from the COVID-19 Crisis

Hedge fund gross U.S. Treasury (UST) exposures doubled from 2018 to February 2020 to $2.4 trillion, primarily driven by relative value arbitrage trading and supported by corresponding increases in repo borrowing. In March 2020, amid unprecedented UST market turmoil, the average UST trading hedge fund had a return of -7% and reduced its UST exposure by close to 20%, despite relatively unchanged bilateral repo volumes and haircuts. Analyzing hedge fund-creditor borrowing data, we find the large, more regulated dealers provided disproportionately more funding during the crisis than other creditors. Overall, the step back in hedge fund UST activity was primarily driven by fund-specific liquidity management rather than dealer regulatory constraints. Hedge funds exited the turmoil with 20% higher cash holdings and smaller, more liquid portfolios, despite low contemporaneous outflows. This precautionary flight to cash was more pronounced among funds exposed to greater redemption risk through shorter share restrictions. Hedge funds predominantly trading the cash-futures basis faced greater margin pressure and reduced UST exposures and repo borrowing the most. After the market turmoil subsided following Fed intervention, hedge fund returns recovered quickly, but UST exposures did not revert to pre-shock levels over the subsequent months.

Price discovery and pairs trading potentials: the case of metals markets

Purpose This study aims to validate the “expectancy theory” of asset pricing and explores the price discovery process in metals futures markets. Design/methodology/approach This paper adopts the Johansen cointegration and vector error correction model approach to investigate the potentials of Pairs trading in the metals market during the period 2008–2019. Findings The results find the price movements in metal markets are not random walk and the current “futures” prices are the reasonable estimate of the “spot” metal prices in future. This study does not notice any significant differences in the price efficiency across metals markets, which signal the effects of limited idiosyncratic forces in price transmission. Practical implications The research suggests the covert use of metal futures to make gains from arbitrage trading. Originality/value The study emphasizes the potential of “pair trading” in commodity market context that is seldom discussed in academic papers.

On cointegration and cryptocurrency dynamics

This paper aims to model the joint dynamics of cryptocurrencies in a nonstationary setting. In particular, we analyze the role of cointegration relationships within a large system of cryptocurrencies in a vector error correction model (VECM) framework. To enable analysis in a dynamic setting, we propose the COINtensity VECM, a nonlinear VECM specification accounting for a varying systemwide cointegration exposure. Our results show that cryptocurrencies are indeed cointegrated with a cointegration rank of four. We also find that all currencies are affected by these long term equilibrium relations. The nonlinearity in the error adjustment turned out to be stronger during the height of the cryptocurrency bubble. A simple statistical arbitrage trading strategy is proposed showing a great in-sample performance, whereas an out-of-sample analysis gives reason to treat the strategy with caution.

Optimal statistical arbitrage trading of Berkshire Hathaway stock and its replicating portfolio

In this paper, we make use of the replicating asset for statistical arbitrage trading, where the replicating asset is constructed by a portfolio that mimics the returns from a factor model. Using the replicating asset in the context of statistical arbitrage has never been done before in the literature. A novel optimal statistical arbitrage trading model is applied, and we derive the average transaction length and return for the Berkshire A stock and its replicating asset. The results show that the statistical arbitrage method proposed by Bertram (2010) is profitable by using the replicating asset. We also compute the average returns under different transaction costs. For the statistical arbitrage using the replicating asset of the factor model, average annual returns were at least 33%. Robustness is examined with the S&P500. Our results can provide hedge fund managers with a new technique for conducting statistical arbitrage.

The Pricing of Exchange Traded Funds and the Roles of Primary and Secondary Market Participants

We study the pricing of exchange traded funds (ETFs) and the associated arbitrage trading of them in the primary and secondary markets. We find a direct relation between primary and secondary market trading that is consistent with market-makers using the primary market to hedge their inventory risk in the secondary market, as well as to facilitate arbitrage. Such trading in both markets keeps ETF prices in line with their net asset value. We conclude that the existence of the primary market enhances secondary market efficiency.

Institutional Investors’ Trading Response to Stock Market Anomalies: Evidence from Korea

This study examines whether institutions are sophisticated investors that exploit stock characteristics known to predict future returns in Korea, using data from 2000 to 2018. We analyze the institutional demand, measured as a change in institutional ownership, for stocks with eight well-known anomalies as well as the future abnormal returns of institutional trading. We find that, generally, institutions do not trade consistently with stock anomaly predictions because they are reluctant to hold both highly overvalued and highly undervalued stocks. Although they use a few anomalies, they use these characteristics passively to avoid stocks known to underperform rather than to pick stocks known to outperform. Furthermore, the positive returns on long-legs are concentrated on stocks sold by institutions, while the negative returns on short-legs are concentrated on stocks bought by them. Our finding casts doubt on the widely-accepted notion that institutions are skilled investors and that institutional arbitrage trading corrects any mispricing in the market. To the contrary, institutions’ loss-averse trading behaviors cause or magnify mispricing.