This paper studies the size and source of exchange-traded funds’ (ETFs) price impact in the most ETF-dominated asset classes: volatility (VIX) and commodities. I show that the introduction of ETFs increased futures prices and decreased realized returns. To identify ETF-induced price distortions, I propose a model-independent approach to replicate the value of a VIX futures contract. This allows me to isolate a non-fundamental component in VIX futures prices, of 18.5% per year, that is strongly related to the rebalancing of ETFs. To understand the source of that component, I decompose trading demand from ETFs into three main parts: leverage rebalancing, calendar rebalancing, and flow rebalancing. Leverage rebalancing has the largest effects. It amplifies price changes and introduces unhedgeable risks for ETF counterparties. Surprisingly, providing liquidity to leveraged ETFs turns out to be a bet on variance, even in a market with a zero net share of ETFs. Trading against leverage rebalancing delivers large abnormal returns and Sharpe ratios above two across markets. The returns jump up in times of market distress.
This paper studies the impact of the ECB's Corporate Sector Purchase Programme (CSPP) announcement on prices, liquidity and debt issuance in the European corporate bond market using a dataset on bond transactions from Euroclear. I find that the QE programme increased prices and liquidity of bonds eligible to be purchased substantially. Bond yields dropped on average by 30 bps (8%) after the CSPP announcement. Tri-party repo turnover rose by 8.15 million USD (29%), and bilateral turnover went up by 7.05 million USD (72%). Bid-ask spreads also showed significant liquidity improvement in eligible bonds. QE was successful in boosting corporate debt issuance. Firms issued 2.19 billion EUR (25%) more in QE-eligible debt after the CSPP announcement, compared to other types of debt. Surprisingly, corporates used the attracted funds mostly to increase dividends. These effects were more pronounced for longer-maturity, lower-rated bonds, and for more credit-constrained, lower-rated firms.
We examine the determinants of repo haircuts using a regulatory transaction-level dataset of the UK market. We find that transaction maturity and collateral quality have first order importance. We also document that counterparties matter in determining haircuts. Hedge funds, as borrowers, receive significantly higher haircuts. Larger borrowers with higher ratings receive lower haircuts, but we find that these effects can be overshadowed by collateral quality. Repeated bilateral relationships also matter and generate lower haircuts. We find evidence supporting an adverse selection explanation of haircuts, but limited evidence in favor of lenders' liquidity position or default probabilities affecting haircuts. Finally, we show that banks with higher network centrality charge and pay lower haircuts.
Preliminary draft available on request.
In this paper we study one aspect of liquidity, namely trading activity, in the European bond market. We analyze a rich data set of individual bond issues which to our knowledge has not previously been the subject of systematic analysis. Our main analysis focuses on bilateral and tri-party repo turnover, number of trades, and average trade size in approximately 20,000 bonds that are actively traded in the European time zone. We break-down this activity using a variety of characteristics of the issue and of the issuer. The fact that these characteristic should matter is hardly surprising or controversial. However, very little is known in general about the impact of these characteristics on market liquidity. This paucity of "stylized facts" about the bond market makes it difficult to approach more complicated questions such as the effects of central bank interventions, regulations, or shift to electronic trading, on bond liquidity. We set out some basic facts contained in 22 million bond market transactions that shed light on some of these questions.
In this paper we analyze the trading of different players in the Bitcoin market. We identify large exchanges, miners, and other major market participants, and document key patterns of flows between them.