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on Financial Markets |
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Issue of 2023‒06‒19
eight papers chosen by |
| By: | Andrew C. Meldrum; Oleg Sokolinskiy |
| Abstract: | We study the relationship between volatility and liquidity in the market for on-the-run Treasury securities using a novel framework for quantifying price impact. We show that at times of relatively low volatility, marginal trades that go with the flow of existing trades tend to have a smaller price impact than trades that go against the flow. However, this difference tends to diminish at times of high volatility, indicating that the perceived information content of going against the flow is less when volatility is high. We also show that market participants executing trades aggressively using market orders will experience larger increases in price impact than those executing trades passively using limit orders as volatility increases. And times of low market depth are associated with increased risk of high price impact and high sensitivity to volatility in future, perhaps because liquidity is more reliant on high-speed quote replenishment and is therefore more fragile. |
| Keywords: | Liquidity; Treasury market; Market depth; Volatility; Order execution; Hidden Markov model |
| JEL: | G01 G10 G12 C51 C58 |
| Date: | 2023–05–05 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2023-28&r=fmk |
| By: | Luca Benzoni; Christian Cabanilla; Alessandro Cocco; Cullen Kavoussi |
| Abstract: | As the debt ceiling episode unfolds, we highlight a sharp increase in trading activity and liquidity in the U.S. credit default swaps (CDS) market, as well as a spike in U.S. CDS premiums. Compared with the periods leading up to the 2011 and 2013 debt ceiling episodes, we show that elevated CDS spreads in the current environment are partially explained by the cheapening of deliverable Treasury collateral to CDS contracts. We infer the likelihood of a U.S. default from these CDS premiums, and estimate an increase in the market-implied default probability from about 0.3–0.4% in 2022, to around 4% in April 2023, which is lower than it was in July 2011 and about where it was in October 2013. Finally, we document changes in Treasury bills trading activity as market participant update their expectations for a U.S. default. |
| JEL: | E32 E43 E44 G10 G12 G18 G28 |
| Date: | 2023–05–17 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedhwp:96219&r=fmk |
| By: | René M. Stulz |
| Abstract: | This paper assesses the current state of knowledge about crisis risk and its implications for risk management. Better data that became available since the Global Financial Crisis (GFC) has improved our understanding of crisis risk. These data have been used to show that some types of crises become predictable when one accounts for interactions between risks. Specifically, a financial crisis is much more likely in the years following both high credit growth and high asset valuations. However, some other types of crises do not seem predictable. There is no evidence that the frequency of economic and financial crises is increasing. The existing data show that political crises make economic crises more likely, so that, as suggested by the concept of polycrisis, feedback between non-economic crises and economic crises can be important, but there is no comparable evidence for climate events. Strategies that increase firm operational and financial flexibility appear successful at reducing the adverse impact of crises on firms. |
| JEL: | G01 G21 G32 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31252&r=fmk |
| By: | Maxime Markov; Vladimir Markov |
| Abstract: | In this paper, we revisit the relationship between investors' utility functions and portfolio allocation rules. We derive portfolio allocation rules for asymmetric Laplace distributed $ALD(\mu, \sigma, \kappa)$ returns and compare them with the mean-variance approach, which is based on Gaussian returns. We reveal that in the limit of small $\frac{\mu}{\sigma}$, the Markowitz contribution is accompanied by a skewness term. We also obtain the allocation rules when the expected return is a random normal variable in an average and worst-case scenarios, which allows us to take into account uncertainty of the predicted returns. An optimal worst-case scenario solution smoothly approximates between equal weights and minimum variance portfolio, presenting an attractive convex alternative to the risk parity portfolio. Utilizing a microscopic portfolio model with random drift and analytical expression for the expected utility function with log-normal distributed cross-sectional returns, we demonstrate the influence of model parameters on portfolio construction. Finally, we address the issue of handling singular covariance matrices by imposing block structure constraints on the precision matrix directly. This comprehensive approach enhances allocation weight stability, mitigates instabilities associated with the mean-variance approach, and can prove valuable for both short-term traders and long-term investors. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.08530&r=fmk |
| By: | Choi, Jaewon; Dasgupta, Amil; Oh, Ji |
| Abstract: | We show that supply-side effects arising from the bond holdings of open-end mutual funds affect corporate credit risk. In our model, funds exposed to flow-performance relationships are reluctant to roll over bonds of companies with weak cash flow prospects fearing future outflows. This lowers rollover prices, enhancing equityholders' strategic default incentives, engendering a positive association between bond funds' presence and credit risk. Empirically, we find that in firms with weak cash flow prospects, fund holding shares increase CDS spreads, and more so when flows are more sensitive to performance. We use instrumental variables and quasi-experiments to address endogeneity concerns. |
| Keywords: | fund flows; credit risk; flow concerns; bond rollover; default-liquidity loop |
| JEL: | G23 G32 |
| Date: | 2022–05–16 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:118856&r=fmk |
| By: | Pierre Durand (Université Paris Est Créteil, ERUDITE, 94010 Créteil Cedex, France); Gaëtan Le Quang (Univ Lyon, Université Lumière Lyon 2, GATE UMR 5824, F-69130 Ecully, France); Arnold Vialfont (Université Paris Est Créteil, ERUDITE, 94010 Créteil Cedex, France) |
| Abstract: | Using a database comprising US bank balance sheet variables covering the 2000-2018 period and the list of failed banks as provided by the FDIC, we run various models to exhibit the main determinants of bank default. Among these models, Logistic Regression, Random Forest, Histogram-based Gradient Boosting Classification and Gradient Boosting Classification perform the best. Relying on various machine learning interpretation tools, we manage to provide evidence that 1) capital is a stronger predictor of default than liquidity, 2) Basel III capital requirements are set at a too low level. More precisely, having a look at the impact of the interaction between capital ratios (the risk-weighted ratio and the simple leverage ratio) and the liquidity ratio (liquid assets over total assets) on the probability of default, we show that the influence of capital on this latter completely outweighs that of liquidity, which is in fact very limited. From a prudential perspective, this questions the recent stress put on liquidity regulation. Concerning capital requirements, we provide evidence that setting the risk-weighted ratio at 15% and the simple leverage ratio at 10% would significantly decrease the probability of default without hampering banks'activities. Overall, these results therefore call for strengthening capital requirements while at the same time releasing the regulatory pressure put on liquidity. |
| Keywords: | Basel III; capital requirements ; liquidity regulation ; bankruptcy prediction models ; statistical learning ; classification |
| JEL: | C44 G21 G28 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:gat:wpaper:2308&r=fmk |
| By: | Riza Demirer (Department of Economics & Finance, Southern Illinois University Edwardsville, Alumni Hall 3145, Edwardsville IL, 62026-1102, USA); David Gabauer (Data Analysis Systems, Software Competence Center Hagenberg, Hagenberg, Austria); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Joshua Nielsen (Boulder Investment Technologies, LLC, 1942 Broadway Suite 314C, Boulder, CO, 80302, USA) |
| Abstract: | This paper examines the predictability of bubbles across global stock markets and whether or not synchronicity in bubble formation across markets can be predicted via metrics of market risk that are readily available. Utilizing the gold to platinum price ratio (LGP) as an easy to implement risk metric and the Log-Periodic Power Law Singularity (LPPLS) model to detect positive and negative bubble formation at different time scales, we document evidence of synchronized boom and bust cycles of the seven developed equity markets in the G7 bloc. More importantly, our analysis shows that bubbles and their comovements are predictable by the gold to platinum price ratio although the predictive relationship is only detectible via models that account for non-linearities in the data. We find that predictability is generally stronger for negative bubbles than their positive counterparts and the predictive impact of LGP is strongest for the long-term for negative bubbles, while it is strongest in the short-run for positive bubbles, meaning that the gold to platinum price ratio serves as a more robust predictor of deeper downward accelerating price formations followed by a rally. The predictability results for the U.S. also carries over to bubble formation in the remaining stock markets of the G7 bloc, to the extent that the gold to platinum price ratio also helps to explain the synchronicity of bubbles across the G7. Our findings provide a valuable opening for market regulators as the results show that readily available metrics of market risk can be used to model and monitor the occurrence of bubbles in financial markets as well as the connectedness of bubbles across the global markets. |
| Keywords: | Multi-Scale Positive and Negative Bubbles, Gold-to-Platinum Price-Ratio, Nonparametric Causality-in-Quantiles Test, G7 |
| JEL: | C22 G15 Q02 |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202317&r=fmk |
| By: | Chniguir Mounira; Henchiri Jamel Eddine |
| Abstract: | This paper aims to test the relationship between investor sentiment and the profitability of stocks listed on two emergent financial markets, the Moroccan and Tunisian ones. Two indirect measures of investor sentiment are used, SENT and ARMS. These sentiment indicators show that there is an important relationship between the stocks returns and investor sentiment. Indeed, the results of modeling investor sentiment by past observations show that sentiment has weak memory; on the other hand, series of changes in sentiment have significant memory. The results of the Granger causality test between stock return and investor sentiment show us that profitability causes investor sentiment and not the other way around for the two financial markets studied.Thanks to four autoregressive relationships estimated between investor sentiment, change in sentiment, stock return and change in stock return, we find firstly that the returns predict the changes in sentiments which confirms with our hypothesis and secondly, the variation in profitability negatively affects investor sentiment.We conclude that whatever sentiment measure is used there is a positive and significant relationship between investor sentiment and profitability, but sentiment cannot be predicted from our various variables. |
| Date: | 2023–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2305.16632&r=fmk |