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Through the lens of market participants' objective to minimize counterparty risk, we provide an explanation for the reluctance to clear derivative trades in the absence of a central clearing obligation. We develop a comprehensive understanding of the benefits and potential pitfalls with respect to a single market participant's counterparty risk exposure when moving from a bilateral to a clearing architecture for derivative markets. Previous studies suggest that central clearing is beneficial for single market participants in the presence of a sufficiently large number of clearing members. We show that three elements can render central clearing harmful for a market participant's counterparty risk exposure regardless of the number of its counterparties: 1) correlation across and within derivative classes (i.e., systematic risk), 2) collateralization of derivative claims, and 3) loss sharing among clearing members. Our results have substantial implications for the design of derivatives markets, and highlight that recent central clearing reforms might not incentivize market participants to clear derivatives.
Central clearing counterparties (CCPs) were established to mitigate default losses resulting from counterparty risk in derivatives markets. In a parsimonious model, we show that clearing benefits are distributed unevenly across market participants. Loss sharing rules determine who wins or loses from clearing. Current rules disproportionately benefit market participants with flat portfolios. Instead, those with directional portfolios are relatively worse off, consistent with their reluctance to voluntarily use central clearing. Alternative loss sharing rules can address cross-sectional disparities in clearing benefits. However, we show that CCPs may favor current rules to maximize fee income, with externalities on clearing participation.
We study the role mutual funds play in the recovery from fast intraday crashes based on data from the National Stock Exchange of India for a single large stock. During normal times, trading activity and liquidity provision by mutual funds is negligible compared to other traders at around 4% of overall activity. Nevertheless, for the two intraday market-wide crashes in our sample, price recovery took place only after mutual funds moved in. Market stability may require the presence of well-capitalized standby liquidity providers for recovery from fast crashes.
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.
The centrality of the United States in the global financial system is taken for granted, but its response to recent political and epidemiological events has suggested that China now holds a comparable position. Using minute-by-minute data from 2012 to 2020 on the financial performance of twelve country-specific exchange-traded funds, we construct daily snapshots of the global financial network and analyze them for the centrality and connectedness of each country in our sample. We find evidence that the U.S. was central to the global financial system into 2018, but that the U.S.-China trade war of 2018–2019 diminished its centrality, and the Covid-19 outbreak of 2019–2020 increased the centrality of China. These indicators may be the first signals that the global financial system is moving from a unipolar to a bipolar world.
Through the lens of market participants' objective to minimize counterparty risk, we provide an explanation for the reluctance to clear derivative trades in the absence of a central clearing obligation. We develop a comprehensive understanding of the benefits and potential pitfalls with respect to a single market participant's counterparty risk exposure when moving from a bilateral to a clearing architecture for derivative markets. Previous studies suggest that central clearing is beneficial for single market participants in the presence of a sufficiently large number of clearing members. We show that three elements can render central clearing harmful for a market participant's counterparty risk exposure regardless of the number of its counterparties: 1) correlation across and within derivative classes (i.e., systematic risk), 2) collateralization of derivative claims, and 3) loss sharing among clearing members. Our results have substantial implications for the design of derivatives markets, and highlight that recent central clearing reforms might not incentivize market participants to clear derivatives.
We study the role of various trader types in providing liquidity in spot and futures markets based on complete order-book and transactions data as well as cross-market trader identifiers from the National Stock Exchange of India for a single large stock. During normal times, short-term traders who carry little inventory overnight are the primary intermediaries in both spot and futures markets, and changes in futures prices Granger-cause changes in spot prices. However, during two days of fast crashes, Granger-causality ran both ways. Both crashes were due to large-scale selling by foreign institutional investors in the spot market. Buying by short-term traders and cross-market traders was insufficient to stop the crashes. Mutual funds, patient traders with better trade-execution quality who were initially slow to move in, eventually bought sufficient quantities leading to price recovery in both markets. Our findings suggest that market stability requires the presence of well-capitalized standby liquidity providers.
An important assumption underlying the designation of some insurers as systemically important is that their overlapping portfolio holdings can result in common selling. We measure the overlap in holdings using cosine similarity, and show that insurers with more similar portfolios have larger subsequent common sales. This relationship can be magnified for some insurers when they are regulatory capital constrained or markets are under stress. When faced with an exogenous liquidity shock, insurers with greater portfolio similarity have even larger common sales that impact prices. Our measure can be used by regulators to predict which institutions may contribute most to financial instability through the asset liquidation channel of risk transmission.