nep-fmk New Economics Papers
on Financial Markets
Issue of 2020‒08‒10
thirteen papers chosen by

  1. Cash-Forward Arbitrage and Dealer Capital in MBS Markets: COVID-19 and Beyond ves By Jiakai Chen; Haoyang Liu; Asani Sarkar; Zhaogang Song
  2. MBS Market Dysfunctions in the Time of COVID-19 By Jiakai Chen; Haoyang Liu; David Rubio; Asani Sarkar; Zhaogang Song
  3. Diversifying with cryptocurrencies during COVID-19 By John Goodell; Stéphane Goutte
  4. Systemic Risk: a Network Approach By Jean-Baptiste Hasse
  5. Identifying indicators of systemic risk By Hartwig, Benny; Meinerding, Christoph; Schüler, Yves
  6. Markowitz portfolio selection for multivariate affine and quadratic Volterra models By Eduardo Abi Jaber; Enzo Miller; Huyên Pham
  7. Portfolio choice with time horizon risk By Alexis Direr
  8. Predicting the global minimum variance portfolio By Reh, Laura; Krüger, Fabian; Liesenfeld, Roman
  9. The search theory of OTC markets By Pierre-Olivier Weill
  10. Global Liquidity,Offshore Bond Issuance and Shadow Banking in China By Shugo Yamamoto
  11. Dissecting Time-Varying Risk Exposures in Cryptocurrency Markets By Daniele Bianchi; Massimo Guidolin; Manuela Pedio
  12. Forecasting Financial Vulnerability in the US: A Factor Model Approach By Hyeongwoo Kim; Wen Shi
  13. Federal Reserve Agency CMBS Purchases By Julia Gouny; Haoyang Liu; Woojung Park

  1. By: Jiakai Chen; Haoyang Liu; Asani Sarkar; Zhaogang Song
    Abstract: We examine the economic mechanisms that limited arbitrage between the cash and forward markets of agency MBS, and whether asset purchases of the Federal Reserve (Fed) alleviated price dislocations. We find that the cash-forward basis, or the price difference between the cash and forward markets of agency MBS controlling for differences in fundamentals, widened significantly—by $0.9 per $100 face value during the height of the COVID-19 crisis. The widening basis was accompanied by a significant increase in selling by customers in the cash market, indicating a “scramble for cash” following the liquidity shock. Dealers provided liquidity by increasing both their long cash and short forward positions significantly, but the basis continued to widen, implying that balance sheet costs constrained dealers’ inventories. We estimate dealers’ average costs of holding inventory for five weeks as about $0.8. We also find that primary dealers affiliated with banks subject to Basel III liquidity regulations increased their positions more than others. The basis narrowed by about $0.7 following the Fed’s MBS purchases in the forward market. We attribute this effect to the faster settlement schedules of the Fed’s purchases, compared to the market convention, which allowed a faster deployment of capital. Overall, our results show that the combined liquidity constraints of investors and dealers led to severe price dislocations, and the Fed, in its role as the “dealer of last resort,” absorbed the liquidity demand that dealers lacked the capacity to meet.
    Keywords: arbitrage; basis; cohort; dealer; liquidity; MBS; specified pool; TBA; COVID-19
    JEL: G12 G14 G18
    Date: 2020–07–01
  2. By: Jiakai Chen; Haoyang Liu; David Rubio; Asani Sarkar; Zhaogang Song
    Abstract: The COVID-19 pandemic elevated financial market illiquidity and volatility, especially in March 2020. The mortgage-backed securities (MBS) market, which plays a critical role in the housing market by funding the vast majority of U.S. residential mortgages, also suffered a period of dysfunction. In this post, we study a particular aspect of MBS market disruptions by showing how a long-standing relationship between cash and forward markets broke down, in spite of MBS dealers increasing the provision of liquidity. (See our related staff report for greater detail.) We also highlight an innovative response by the Federal Reserve that seemed to have helped to normalize market functioning.
    Keywords: mortgage-backed securities (MBS) market; liquidity; COVID-19
    JEL: G1 E5
    Date: 2020–07–17
  3. By: John Goodell (University of Akron); Stéphane Goutte (Cemotev - Centre d'études sur la mondialisation, les conflits, les territoires et les vulnérabilités - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines)
    Abstract: Literature suggests assets become more correlated during economic downturns. The current COVID-19 crisis provides an unprecedented opportunity to investigate this considerably further. Further, whether cryptocur-rencies provide a diversification for equities is still an unsettled issue. Additionally , the question of whether cryptocurrency futures are safe havens has received very little attention. We employ several econometric procedures , including wavelet coherence, copula principal component, and neural network analyses to rigorously examine the role of COVID-19 on the paired co-movements of six cryptocurrencies, as well as bitcoin futures, with fourteen equity indices and the VIX. We find co-movements between cryptocurrencies and equity indices gradually increased as COVID-19 progressed. However, most of these co-movements are positively correlated, suggesting that cryptocurrencies do not provide a diversification benefit during downturns. Exceptions, however, are the co-movements of bitcoin futures and tether being negative with equities. Results are consistent with investment vehicles that attract either more informed or more speculative investors differentiating themselves as safe havens.
    Keywords: Co-movement,COVID-19,Bitcoin,Wavelet,Safe haven JEL classification: C58
    Date: 2020–06–20
  4. By: Jean-Baptiste Hasse (Aix-Marseille Univ, CNRS, EHESS, Ecole Centrale, AMSE, Marseille, France)
    Abstract: We propose a new measure of systemic risk based on interconnectedness, defined as the level of direct and indirect links between financial institutions in a correlation-based network. Deriving interconnectedness in terms of risk, we empirically show that within a financial network, indirect links are strengthened during systemic events. The relevance of our measure is illustrated at both local and global levels. Our framework offers policymakers a useful toolbox for exploring the real-time topology of the complex structure of dependencies in financial systems and for measuring the consequences of regulatory decisions.
    Keywords: financial networks, interconnectedness, systemic risk, spillover
    JEL: G01 G15 G21
    Date: 2020–07
  5. By: Hartwig, Benny; Meinerding, Christoph; Schüler, Yves
    Abstract: We operationalize the definition of systemic risk provided by the IMF, BIS, and FSB and derive testable hypotheses to identify indicators of systemic risk. We map these hypotheses into a two-stage hierarchical testing framework, combining insights from the early-warning literature on financial crises with recent advances on growth-at-risk. Applying this framework to a set of candidate variables, we find that the Basel III credit-to-GDP gap does not indicate systemic risk coherently across G7 countries. Credit growth and house price growth also do not pass our test in many cases. By contrast, a composite financial cycle signals systemic risk consistently for all countries except Canada. Overall, our results suggest that systemic risk may be consistently measured only once the turning points of indicators have been observed. Therefore, pre-emptive countercyclical macroprudential policy may smooth the financial cycle in boom phases, which then indirectly mitigates the amount of systemic risk in the future.
    Keywords: systemic risk,macroprudential regulation,forecasting,growth-at-risk,financial cycles
    JEL: E37 E44 G17
    Date: 2020
  6. By: Eduardo Abi Jaber (CES - Centre d'économie de la Sorbonne - CNRS - Centre National de la Recherche Scientifique - UP1 - Université Panthéon-Sorbonne); Enzo Miller (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique); Huyên Pham (LPSM (UMR_8001) - Laboratoire de Probabilités, Statistiques et Modélisations - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)
    Abstract: This paper concerns portfolio selection with multiple assets under rough covariance matrix. We investigate the continuous-time Markowitz mean-variance problem for a multivariate class of affine and quadratic Volterra models. In this incomplete non-Markovian and non-semimartingale market framework with unbounded random coefficients, the optimal portfolio strategy is expressed by means of a Riccati backward stochastic differential equation (BSDE). In the case of affine Volterra models, we derive explicit solutions to this BSDE in terms of multi-dimensional Riccati-Volterra equations. This framework includes multivariate rough Heston models and extends the results of \cite{han2019mean}. In the quadratic case, we obtain new analytic formulae for the the Riccati BSDE and we establish their link with infinite dimensional Riccati equations. This covers rough Stein-Stein and Wishart type covariance models. Numerical results on a two dimensional rough Stein-Stein model illustrate the impact of rough volatilities and stochastic correlations on the optimal Markowitz strategy. In particular for positively correlated assets, we find that the optimal strategy in our model is a `buy rough sell smooth' one.
    Keywords: 60H10,rough volatility,Mean-variance portfolio theory,correlation matrices,multi- dimensional Volterra process,Riccati equations,non-Markovian Heston,Stein-Stein and Wishart models MSC Classification: 93E20,60G22
    Date: 2020–06–22
  7. By: Alexis Direr (LEO - Laboratoire d'Économie d'Orleans - UO - Université d'Orléans - Université de Tours - CNRS - Centre National de la Recherche Scientifique)
    Abstract: I study the allocation problem of investors who hold their portfolio until a target wealth is attained. The strategy suppresses final wealth uncertainty but creates an investment time horizon risk. I begin with a simple mean variance model transposed in the duration domain, then study a dynamic portfolio choice problem with Generalized Expected Discounted Utility preferences. Using long-term US return data, I show in the mean variance model that a large amount of time horizon risk can be diversified away by investing a significant share of equities. In the dynamic model, more impatient investors are also more averse to timing risk and invest less in equities. The equity share is downward trending with accumulated wealth relative to its target. J.E.L. codes: D8, E21
    Keywords: portfolio choice,risk aversion,timing risk
    Date: 2020–06–24
  8. By: Reh, Laura; Krüger, Fabian; Liesenfeld, Roman
    Abstract: We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP). The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss function from which we can infer the optimal GMVP weights without imposing any distributional assumptions on the returns. In order to capture time variation in the returns' conditional covariance structure, we model the portfolio weights through a recursive least squares (RLS) scheme as well as by generalized autoregressive score (GAS) type dynamics. Sparse parameterizations combined with targeting towards nonlinear shrinkage estimates of the long-run GMVP weights ensure scalability with respect to the number of assets. An empirical analysis of daily and monthly financial returns shows that the proposed models perform well in- and out-of-sample in comparison to existing approaches.
    Keywords: Consistent loss function,Elicitability,Forecasting,Generalized autoregressivescore,Nonlinear shrinkage,Recursive least squares
    JEL: C14 C32 C51 C53 C58 G11 G17
    Date: 2020
  9. By: Pierre-Olivier Weill
    Abstract: I review the recent literature that applies search-and-matching theory to the study of Over-the-Counter (OTC) financial markets. I formulate and solve a simple model in order to illustrate the typical assumptions and economic forces at play in existing work. I then offer thematic tours of the literature and, in the process, discuss avenues for future research.
    JEL: G0 G12
    Date: 2020–06
  10. By: Shugo Yamamoto (Graduate School of Economics, Kobe University / The Graduate School of East Asian Studies,Yamaguchi University)
    Abstract: In China, to benefit from abundant global liquidity, offshore affiliates of non-financial companies have been increasingly used as financing vehicles for accumulating low-yield US dollar liabilities. To elucidate this issue and its implications, we specifically examine offshore bond issuance, within-company flows, and shadow banking. Results indicate that a global liquidity shock will increase shadow banking, as represented by entrusted loans in China. In spite of strict financial restrictions, about 20% of the variance of shadow banking is explained by global liquidity shocks.
    Date: 2020–07
  11. By: Daniele Bianchi; Massimo Guidolin; Manuela Pedio
    Abstract: In this paper we take an empirical asset pricing perspective and investigate the dominant view (possibly, an instinctive reflection of the media hype surrounding the surge of Bitcoin valuations) that cryptocurrencies represent a new asset class, spanning risks and payoffs sufficiently different from the traditional ones. Methodologically, we rely on a flexible dynamic econometric model that allows not only time-varying coefficients, but also allow that the entire forecasting model be changing over time. We estimate such model by looking at the time variation in the exposures of major cryptocurrencies to stock market risk factors (namely, the six Fama French factors), to precious metal commodity returns, and to cryptocurrency-specific risk-factors (namely, crypto-momentum, a sentiment index based on Google searches, and supply factors, i.e., electricity and computer power). The main empirical results suggest that cryptocurrencies are not systematically exposed to stock market factors, precious metal commodities or supply factors with the exception of some occasional spikes of the coecients during our sample. On the contrary, crypto assets are characterized by a time-varying but significant exposure to a sentiment index and to crypto-momentum. Despite the lack of predictability compared to traditional asset classes, cryptocurrencies display considerable diversification power in a portfolio perspective and as such they can lead to a moderate improvement in the realized Sharpe ratios and certainty equivalent returns within the context of a typical portfolio problem.
    Keywords: Cryptocurrencies, predictability, portfolio diversification, dynamic model averaging, time-varying, parameter regressions.
    JEL: E40 E52
    Date: 2020
  12. By: Hyeongwoo Kim; Wen Shi
    Abstract: This paper presents a factor-based forecasting model for the financial market vulnerability, measured by changes in the Cleveland Financial Stress Index (CFSI). We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data in order to out-of-sample forecast the CFSI. Our factor models outperform both the random walk and the autoregressive benchmark models in out-of-sample predictability at least for the short-term forecast horizons, which is a desirable feature since financial crises often come to a surprise realization. Interestingly, the first common factor, which plays a key role in predicting the financial vulnerability index, seems to be more closely related with real activity variables rather than nominal variables. We also present a binary choice version factor model that estimates the probability of the high stress regime successfully.
    Keywords: Financial Stress Index; Method of the Principal Component; Out-of-Sample Forecast; Relative Root Mean Square Prediction Error; Diebold-Mariano-West Statistic; Ordered Probit Model
    JEL: E44 E47 G01 G17
    Date: 2020–07
  13. By: Julia Gouny; Haoyang Liu; Woojung Park
    Abstract: On March 23, the Open Market Trading Desk (the Desk) at the Federal Reserve Bank of New York initiated plans to purchase agency commercial mortgage-backed securities (agency CMBS) at the direction of the FOMC in order to support smooth market functioning of the markets for these securities. This post describes the deterioration in market conditions that led to agency CMBS purchases, how the Desk conducts these operations, and how market functioning has improved since the start of the purchase operations.
    Keywords: SOMA; agency; agency commercial mortgage-backed securities (agency CMBS); Open Market Trading Desk; COVID-19; the Desk
    JEL: E5 G1
    Date: 2020–07–16

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