Hedge Fund Contagion and Liquidity Shocks BOYSON, NICOLE M.; STAHEL, CHRISTOF W.; STULZ, RENÉ M.
The Journal of finance (New York),
October 2010, Volume:
65, Issue:
5
Journal Article
Peer reviewed
Defining contagion as correlation over and above that expected from economic fundamentals, we find strong evidence of worst return contagion across hedge fund styles for 1990 to 2008. Large adverse ...shocks to asset and hedge fund liquidity strongly increase the probability of contagion. Specifically, large adverse shocks to credit spreads, the TED spread, prime broker and bank stock prices, stock market liquidity, and hedge fund flows are associated with a significant increase in the probability of hedge fund contagion. While shocks to liquidity are important determinants of performance, these shocks are not captured by commonly used models of hedge fund returns.
This study examines option market liquidity using Ivy DB's OptionMetrics data. We establish convincing evidence of commonality for various liquidity measures based on the bid–ask spread, volumes, and ...price impact. The commonality remains strong even after controlling for the underlying stock market's liquidity and other liquidity determinants such as volatility. Smaller firms and firms with a higher volatility exhibit stronger commonalities in option liquidity. Aside from commonality, we also uncover several other important properties of the option market's liquidity. First, information asymmetry plays a much more dominant role than inventory risk as a fundamental driving force of liquidity. Second, the market-wide option liquidity is closely linked to the underlying stock market's movements. Specifically, the options liquidity responds asymmetrically to upward and downward market movements, with calls reacting more in up markets and puts reacting more in down markets.
Asset pricing with liquidity risk Acharya, Viral V.; Pedersen, Lasse Heje
Journal of financial economics,
08/2005, Volume:
77, Issue:
2
Journal Article
Peer reviewed
Open access
This paper solves explicitly a simple equilibrium model with liquidity risk. In our liquidity-adjusted capital asset pricing model, a security's required return depends on its expected liquidity as ...well as on the covariances of its own return and liquidity with the market return and liquidity. In addition, a persistent negative shock to a security's liquidity results in low contemporaneous returns and high predicted future returns. The model provides a unified framework for understanding the various channels through which liquidity risk may affect asset prices. Our empirical results shed light on the total and relative economic significance of these channels and provide evidence of flight to liquidity.
The accurate classification of banks’ Liquidity Risk (LR) for regulatory supervision is hindered by limitations in the measures, such as Minimum Liquid Assets (MLA), Net-Stable Funding Ratio (NSFR), ...and Liquidity Coverage Ratio (LCR). This study addressed two limitations on data integrity vulnerabilities and the narrow composition of LR factors excluding practical LR determinants such as credit portfolio quality, market conditions, strategies of assets and funding. Theoretical gaps included the eight new LR factors in this study, benchmarking study results with measures to interpret the studies’ contributions and the selection of suitable prediction methods for non-linear, imbalanced, scaling, and near real-time data. We used data from 38 Tanzanian banks (2010-2021) from the Bank of Tanzania (BOT). Extensive factors experimentation using Random Forest (RF) and Multi-Layer Perceptron (MLP) models identified ten features for Machine Learning (ML) analysis and LR rating as output. A hybrid RF-MLP model with a 199-tree RF and 10-512-250-120-80-60-6 MLP was developed. It increased LR sensitivity and reduced RF and MLP model limitations through generalisation, and demonstrated statistical and practical performance. It minimised classification errors with Type I and II errors, and Negative Likelihood of 0.8%, 9.1%, and 1%; Discriminant Power of 2.61; and 90% to 96% Accuracy, Balanced Accuracy, Precision, Recall, F1 Score, G-mean, Cohen’s Kappa, Youden Index, and Area Under the Curve. Past LR scenarios confirmed RF-MLP performance improvement over MLA. The unavailability of LCR and NSFR data hindered a comprehensive evaluation. This study extended LR factors and proposed a model to complement LR classification.
•The proposed model solves BASEL’s and Liquidity Risk frameworks’ limitations in assessing Liquidity Risk from its relation with movements in the quality of credit portfolio, assets and funding strategies, and market conditions.•The proposed model maximises Liquidity Risk detection at performance and error rate better than benchmarked models.•The proposed model is suitable for application in non-linear, scaling, and a 2-min near real-time data in live detection of Liquidity Risk.•The model’s benchmark with industry metrics enables adoptions and implementation.•This study extends the application of Machine Learning in banking risk management.
Funding liquidity creation by banks Thakor, Anjan; Yu, Edison G.
Journal of financial stability,
August 2024, 2024-08-00, Volume:
73
Journal Article
Peer reviewed
Relying on theories in which bank create private money by making loans that create deposits—a process we call “funding liquidity creation”—we measure how much funding liquidity the U.S. banking ...system creates. Private money creation by banks enables lending to not be constrained by the supply of cash deposits. During the 2001–2020 period, 92 percent of bank deposits were due to funding liquidity creation, and during 2011–2020 funding liquidity creation averaged $10.7 trillion per year, or 57 percent of GDP. Using natural disasters data, we provide causal evidence that better-capitalized banks create more funding liquidity and lend more even during times when cash deposit balances are falling or unchanged. Large banks as well as the top banks in Federal Reserve districts create more liquidity.
We analyze the role of loan maturity and collateral eligibility in the transmission of central bank liquidity provisions to banks following a wholesale funding dry-up. We analyze the transmission of ...the three-year LTRO, which substantially extended the ECB liquidity maturity, in Italy, where banks benefited from a government guarantee program that effectively relaxed the ECB collateral requirements. Combining the national credit register with banks securities holdings, we find that (i) the maturity extension supported banks’ credit supply and (ii) banks used most liquidity to buy domestic government bonds and substitute missing wholesale funding, two possibly unstated goals of the intervention.
This paper demonstrates that liquidity risk helps explain the return patterns of stocks with high book-to-market ratios and low intangible returns. We document empirical evidence that (1) liquidity ...shocks, the unexpected variation in liquidity factors that are orthogonal to the firm's past accounting performance, predict stock returns, (2) stocks with higher book-to-market ratios or lower intangible returns have higher exposure to aggregate capital constraint measures (i.e. these stocks possess higher liquidity risk) and (3) the returns of long-term contrarian strategies based on liquidity shocks, book-to-market ratios and intangible returns are highly correlated and serve as proxies for returns from liquidity provision. Moreover, liquidity-providing returns are stronger in declining markets as well as when the market volatility is high, indicating that liquidity providers are capital-constrained in providing liquidity under such conditions.
Can hedge funds time market liquidity? Cao, Charles; Chen, Yong; Liang, Bing ...
Journal of financial economics,
08/2013, Volume:
109, Issue:
2
Journal Article
Peer reviewed
Open access
We explore a new dimension of fund managers' timing ability by examining whether they can time market liquidity through adjusting their portfolios' market exposure as aggregate liquidity conditions ...change. Using a large sample of hedge funds, we find strong evidence of liquidity timing. A bootstrap analysis suggests that top-ranked liquidity timers cannot be attributed to pure luck. In out-of-sample tests, top liquidity timers outperform bottom timers by 4.0–5.5% annually on a risk-adjusted basis. We also find that it is important to distinguish liquidity timing from liquidity reaction, which primarily relies on public information. Our results are robust to alternative explanations, hedge fund data biases, and the use of alternative timing models, risk factors, and liquidity measures. The findings highlight the importance of understanding and incorporating market liquidity conditions in investment decision making.
Funding liquidity risk has played a key role in all historical banking crises. Nevertheless, a measure for funding liquidity risk based on publicly available data remains so far elusive. We address ...this gap by showing that aggressive bidding at central bank auctions reveals funding liquidity risk. We can extract an insurance premium from banks’ bids which we propose as a measure of funding liquidity risk. Using a unique data set consisting of all bids in all auctions for the main refinancing operation conducted at the ECB between June 2005 and October 2008 we find that funding liquidity risk is typically stable and low, with occasional spikes especially around key events during the recent crisis. We also document downward spirals between funding liquidity risk and market liquidity. As measurement without clear definitions is impossible, we initially provide definitions of funding liquidity and funding liquidity risk.
I exploit the 1998 Russian default as a negative liquidity shock to international banks and analyze its transmission to Peru. I find that after the shock international banks reduce bank-to-bank ...lending to Peruvian banks and Peruvian banks reduce lending to Peruvian firms. The effect is strongest for domestically owned banks that borrow internationally, intermediate for foreign-owned banks, and weakest for locally funded banks. I control for credit demand by examining firms that borrow from several banks. These results suggest that international banks transmit liquidity shocks across countries and that negative liquidity shocks reduce bank lending in affected countries.
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