Market Distress Research Articles

Unlocking the black box: Non-parametric option pricing before and during COVID-19.

This paper addresses the interpretability problem of non-parametric option pricing models by using the explainable artificial intelligence (XAI) approach. We study call options written on the S&P 500 stock market index across three market regimes: pre-COVID-19, COVID-19 market crash, and post-COVID-19 recovery. Our comparative option pricing exercise demonstrates the superiority of the random forest and extreme gradient boosting models for each market regime. We also show that the model’s pricing accuracy has worsened from the pre-COVID-19 to the recovery period. Moneyness was the most important price determinants across the market regimes, while the implied volatility and time-to-maturity inputs contributed intermittently to a lesser extent. During the COVID-19 crash, open interest gained more economic importance due to the increased behavioral tendencies of traders consistent with market distress.

Time‐varying neural network for stock return prediction

AbstractWe consider the problem of neural network training in a time‐varying context. Machine learning algorithms have excelled in problems that do not change over time. However, problems encountered in financial markets are often time varying. We propose the online early stopping algorithm and show that a neural network trained using this algorithm can track a function changing with unknown dynamics. We compare the proposed algorithm to current approaches on predicting monthly US stock returns and show its superiority. We also show that prominent factors (such as the size and momentum effects) and industry indicators exhibit time‐varying predictive power on stock returns. We find that during market distress, industry indicators experience an increase in importance at the expense of firm level features. This indicates that industries play a role in explaining stock returns during periods of heightened risk.

Stablecoins: Growth Potential and Impact on Banking

<ns2:p>Stablecoins have experienced tremendous growth in the past year, serving as a possible breakthrough innovation in the future of payments. In this paper, we discuss the current use cases and growth opportunities of stablecoins, and we analyze the potential for stablecoins to broadly impact the banking system. The impact of stablecoin adoption on traditional banking and credit provision can vary depending on the sources of inflow and the composition of stablecoin reserves. Among the various scenarios, a two-tiered banking system can both support stablecoin issuance and maintain traditional forms of credit creation. In contrast, a narrow bank approach for digital currencies can lead to disintermediation of traditional banking, but may provide the most stable peg to fiat currencies. Additionally, dollar-pegged stablecoins backed by adequately safe and liquid collateral can potentially serve as a digital safe haven currency during periods of crypto market distress.</ns2:p>

The role of precious metals in portfolio diversification during the Covid19 pandemic: A wavelet-based quantile approach

In this study the relation between stock markets and precious metals during first wave of Covid-19 pandemic are investigated. We use a wavelet-based quantile procedure to investigate correlation between major stock markets of emerging countries (BRIC) and the United States. Our procedure reveals that precious metals offer market diversification opportunities during the period under consideration. In particular, it is found the gold, silver, platinum, and palladium, all serve as safe-haven assets during periods of market distress across short, medium and long investment horizons.

Forced Entrepreneurs

ABSTRACTConventional wisdom suggests that labor market distress drives workers into temporary self‐employment, lowering entrepreneurial quality. Analyzing employment histories for 640,000 U.S. workers, we document that graduating college during a period of high unemployment does increase entry to entrepreneurship. However, compared to voluntary entrepreneurs, firms founded by forced entrepreneurs are more likely to survive, innovate, and receive venture backing. Explaining these results, we confirm that labor shocks disproportionately impact high earners, with these workers starting more successful firms. Overall, we document untapped entrepreneurial potential across the top of the income distribution and the role of recessions in reversing this missing entrepreneurship.

Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning

Unsupervised machine learning can interpret logarithmic returns and conditional volatility in commodity markets. This article applies machine learning in order to visualize and interpret log returns and conditional volatility in commodities trading. We emphasize two classes of unsupervised learning methods: clustering and manifold learning for the reduction of dimensionality. We source daily prices from September 18, 2000 through July 31, 2020, for precious metals, base metals), energy commodities and agricultural commodities. Our results highlight that at the very least, returns-based clusters conform more closely to traditional boundaries between precious metals, base metals, fuels, temperate-climate agricultural commodities, and tropical agricultural commodities. On the other hand, volatility-based clustering succeeds in identifying periods of extreme market distress, such as the global financial crisis of 2008–09 and the Covid-19 pandemic.

Transition Assessment of the Bangladeshi Financial Market Stress Regimes: A Markov Switching Modeling Approach

Purpose of this study: In view of the global financial crises and the ensuing consequences, this research presents the utility of demonstrating an assessment that can forecast the Bangladeshi financial market’s well-being by analysing episodes of economic crises which may prevent market distress. By graphically demonstrating eventual economic episodes in the financial sector, this study sets out to illustrate the chronological scenario of economic turning points. The scope of this research is to study the vulnerable aspects of financial instability in Bangladesh and seek possible remedies. Methodology: The Bangladeshi financial market regimes will be constructed based on Hamilton's Markov Switching Model (1989). This paper is the first attempt in utilising a standardised methodology found in business cycle literatures so as to determine the turning points of economic episodes in the Bangladeshi financial dynamic cycle. Main Findings: This study examines the financial crises and economic distress experienced by banks as forms of economic vulnerabilities. Thus, it describes the financial regimes of transition period movements in the context of the vulnerability of the Bangladeshi financial market sector using the Markov Switching Modeling (MSM) Approach and shows ways to possibly achieve recovery. Research Limitations/Implications: This research focuses on the current financial episodes of the economic sector’s dynamic movements in a condensed area, while the selection of a broad financial arena of parameters results in more significant and robust outcomes. Novelty/Originality: Further studies are needed to define and measure the financial cycle concept and its relationship with business cycles, as well as to delineate dynamic models that can offer substantial probabilistic assessments regarding changes in financial cycle regimes. This can significantly develop the capability of the financial market supervisory authorities to forecast macro-prudential systemic risks and to avoid or reduce the consequences of economic crises. This current study provides a platform for future studies in similar fields.

Bank distress in the European Union 2008–2015: A closer look at capital, size and revenue diversification

AbstractThe recent global financial crisis has clearly highlighted the importance of the timely identification of weak banks. This paper introduces and analyses a new sample of European Union (EU) banks, which faced distress during 2008–2015 and provides evidence regarding the relationship between distress and bank‐specific, macroeconomic, banking sector and stock market distress determinants, paying particular attention to issues that have been central following the crisis, such as capital, size and revenue diversification. Our findings regarding these variables seem to “connect” well with current supervisory actions. The paper also focuses on banks in EU countries that faced economic problems and documents that the probability of distress in these countries has been influenced by different factors, in comparison to the rest of the EU.

The Impact of COVID-19 Lockdowns on Sustainable Indexes

This paper analyzes the response of sustainable indexes to the pandemic lockdown orders in Europe and the USA, contributing to both the research on the effects of the global pandemic outbreak and the resiliency of sustainable investments under market distress. Our results demonstrate that sustainable indexes were negatively impacted by lockdown orders; however, they did not show statistically significant different abnormal returns compared to traditional indexes. Similarly, our empirical results confirm that sustainable screening strategies (negative, positive, best in class) did not have an influence during such announcements. These results are robust across several model specifications and robustness tests, including nonparametric tests, generalized autoregressive conditionally heteroskedastic (GARCH) estimation of abnormal returns, and alternative events. The findings suggest that investors do not have to pay the price for the investments in sustainable assets when a bear market occurs; consequently, ceteris paribus, these investments appear suitable for financial-first investors. Such results have relevant practical consequences in terms of sustainable investment attractiveness and market growth.

Dynamic analysis of counterparty exposures and netting efficiency of central counterparty clearing

Dynamic exposure and default contagion in the over-the-counter (OTC) market is considered in this paper to analyze the time-dependent priority of central counterparty (CCP) clearing. We investigate the asymptotic behavior of the netting efficiency which is measured as the average of total expected exposures (ATEE), by proving the convergence of empirical measure-valued processes associated with our dynamic CCP model when the number of entities tends to infinity. The limit of ATEE is supported by numerical evidence as an effective approximation to that of the finite number of entities by comparing it with Monte-Carlo simulation. The key insight revealed by our analysis is that one CCP can lose its superiority to bilateral clearing and multiple CCPs after a critical time after market distress happens, as observed in our extensive numerical experiments. We also find that both of the large initial exposure and default intensity of the entity can result in the premature failure of CCP clearing.

A non-elliptical orthogonal GARCH model for portfolio selection under transaction costs

Covariance matrix forecasts for portfolio optimization have to balance sensitivity to new data points with stability in order to avoid excessive rebalancing. To achieve this, a new orthogonal GARCH model for a multivariate set of non-Gaussian asset returns is proposed. The conditional return distribution is multivariate generalized hyperbolic and the dispersion matrix dynamics are driven by the leading factors in a principal component decomposition. Each of these leading factors is endowed with a univariate GARCH structure, while the remaining eigenvalues are kept constant over time. Joint maximum likelihood estimation of all model parameters is performed via an expectation maximization algorithm, and is applicable in high dimensions. The new model generates realistic correlation forecasts even for large asset universes and captures rising pairwise correlations in periods of market distress better than numerous competing models. When applied to portfolio optimization, it generates strategies with lower turnover and maximum drawdown, and superior risk-adjusted returns net of transaction costs. Moreover, unlike its competitors, it performs well in the sudden market downturn triggered by the global COVID-19 pandemic.

Bankruptcy & Bailouts; Subsidies & Stimulus: The Government Toolset for Responding to Market Distress

In this article, I attempt to clarify the appropriate government tools for responding to different forms of market distress in times of crisis. I provide my analysis in the context of the United States responses to market distress related to the COVID-19 pandemic.With the exception of measures related to financial markets, those responses formed a chaotic mix of disconnected half measures that neither stabilized the economy nor provided meaningful relief to those most affected. While that failure may be attributable in part to general government dysfunction and legislative gridlock, a large part of the problem arises from the lack of a clearly identified purpose and framework to guide government responses. The lesson here is that the tools deployed by governments to alleviate a crisis should depend on the nature of the specific problem at hand, and scattershot approaches are unlikely to work. As obvious as that principle may seem, it was largely ignored in 2020, and much of the confusion in the pandemic responses can be attributed to attempts to use the wrong tools and from implementation of measures that lacked any clear purpose. After reviewing the actual response, I lay out a framework for identifying the right tools. I suggest four types of market distress--specific economic, systemic economic, specific financial, and systemic financial--each with a corresponding category of appropriate government responses (respectively): direct subsidies, general stimulus, bankruptcy proceedings, and financial bailouts. I describe the relationship between the category of tools and the type of distress. I also provide a close look at and place special emphasis on the interaction between pandemic responses and bankruptcy law as bankruptcy’s role has been the most obviously misunderstood and provides the starkest example of confused analysis.

Measuring Corporate Bond Market Dislocations

We propose the Corporate Bond Market Distress Index (CMDI) to quantify corporate bond market dislocations in real time. The index takes a preponderance-of-metrics perspective to combine a broad set of measures of market functioning from primary and secondary markets but not driven by any one statistic. We document that the index correctly identifies periods of dislocations and predicts future realizations of commonly used measures of market distress, while the converse is not the case. Moreover, the CMDI is an economically and statistically significant predictor of future economic activity, even after controlling for standard predictors, including credit spreads.

Municipal Markets and the Municipal Liquidity Facility

Municipal bond markets experienced a significant amount of strain in response to the COVID-19 crisis, creating liquidity and credit concerns among market participants. During the economic shutdown resulting from the pandemic, income tax revenues were deferred and sales tax revenues decreased beginning in spring 2020, while the cost of borrowing significantly increased for municipal issuers. To aid municipal borrowing needs, the Federal Reserve implemented the Municipal Liquidity Facility (MLF) on April 9, 2020. In this analysis we describe the municipal market conditions as they evolved during 2020, we document the response by the Federal Reserve to municipal market distress with a focus on the MLF, and we conduct an event study to examine MLF-related impacts on market index yield spreads. We detail two case studies that compare yield spreads for two issuers that had sold debt to the MLF and find that yield spreads in secondary market transactions for these two issuers were notably reduced after a public announcement of intent to sell debt to the MLF. Our results present additional evidence that the MLF had a positive impact on municipal market functioning during the pandemic period.

Non-Bank Financial Intermediation in the Euro Area: Implications for Monetary Policy Transmission and Key Vulnerabilities

The financing structure of the euro area economy has evolved since the global financial crisis with non-bank financial intermediation taking a more prominent role. This shift affects the transmission of monetary policy. Compared with banks, non-bank financial intermediaries are more responsive to monetary policy measures that influence longer-term interest rates, such as asset purchases. The increasing role of debt securities in the financing structure of firms also leads to a stronger transmission of long-rate shocks. At the same time, short-term policy rates remain an effective tool to steer economic outcomes in the euro area, which is still highly reliant on bank loans. Amid a low interest rate environment, the growth of market-based finance has been accompanied by increased credit, liquidity and duration risk in the non-bank sector. Interconnections in the financial system can amplify contagion and impair the smooth transmission of monetary policy in periods of market distress. The growing importance of non-bank financial intermediaries has implications for the functioning of financial market segments relevant for monetary policy transmission, in particular the money markets and the bond markets.

How Long-Run Effects of Local Demand Shocks on Employment Rates Vary with Local Labor Market Distress

This paper estimates that long-run changes in a county’s prime-age employment rate are significantly affected by labor demand shocks to both the county and its overlying commuting zone (CZ). The overall benefits of labor demand shocks are due more to CZ demand shocks than county demand shocks. A lower preexisting county employment rate increases the effects of CZ demand shocks. Simulations suggest that low prior employment rate CZs, versus higher-rate CZs, will have much larger employment rate effects from demand shocks. Targeting jobs at more distressed counties within a CZ has modest effects, much lower than the effects of targeting jobs at more distressed CZs.

Capital Market Volatility MGARCH Analysis: Evidence from Southeast Asia

This paper is aimed to explore the co-movement capital market in Southeast Asia and analysis the correlation of conventional and Islamic Index in the regional and global equity. This research become necessary to represent the risk on the capital market and measure market performance, as investor considers the volatility before investing. The time series daily data use from April 2012 to April 2020 both conventional and Islamic stock index in Malaysia and Indonesia. This paper examines the dynamics of conditional volatilities and correlations between those markets by using Multivariate Generalized Autoregressive Conditional Heteroscedasticity (MGARCH). Our result shows that conventional or composite index in Malaysia less volatile than Islamic, but on the other hand, both drive correlation movement. The other output captures that Islamic Index in Indonesian capital market more gradual volatilities than the Composite Index that tends to be low in risk so that investors intend to keep the shares. Generally, the result shows a correlation in each country for conventional and the Islamic index. However, Internationally Indonesia and Malaysia composite and Islamic is low correlated. Regionally Indonesia's indices movement looks to be more correlated and it's similar to Malaysian Capital Market counterparts. In the global market distress condition, the diversification portfolio between Indonesia and Malaysia does not give many benefits.

Board Effectiveness and Firm Risk

This study provides empirical evidence on the impact of board effectiveness on firm risk. The author considers the Board Shareholder Confidence Index (BSCI) to proxy for board effectiveness of firms included in the S&amp;P TSX Composite. Risk measures considered in this study include total risk, idiosyncratic risk, systematic risk, and volatility-of-volatility. He reports statistically significant negative relationships between board effectiveness and alternative firm risk measures, except for systematic risk. This relationship is particularly strong during periods of market distress. Considering analyst coverage and strategic ownership as competing monitoring channels for board effectiveness, the author observes a substitution effect during normal market periods and a supplementary effect during periods of market distress when an investor’s level of uncertainty is high and “<i>every little bit helps</i>.” The findings also note the importance of corporate governance aspects in an ESG risk management framework. <b>TOPICS:</b>ESG investing, VAR and use of alternative risk measures of trading risk, risk management <b>Key Findings</b> • Board effectiveness is associated with lower levels of firm risk. • Analyst coverage acts as a substitute for board effectiveness with respect to total risk, idiosyncratic risk, and volatility-of-volatility. • Strategic ownership and ownership concentration act as a substitute for board effectiveness with respect to systematic risk. • In general, alternative monitoring mechanisms act as substitutes during normal times but are supplements during periods of distress when uncertainty is high.

How Did Depositors Respond to COVID-19?

Why did banks experience massive deposit inflows during the first months of the pandemic? Using weekly branch-level data on interest rates and county-level data on COVID-19 cases, we discover that interest rates at bank branches in counties with higher COVID-19 infection rates fell by more than rates at other branches—even branches of the same bank in different counties. When differentiating weeks by the degree of stock market distress and counties by the likely impact of COVID-19 cases on economic anxiety, the evidence suggests that the deposit inflows were triggered by a surge in the supply of precautionary savings.

Stock returns are not always from the same distribution: Evidence from the Great Recession

Portfolio allocation strategies, and notably the mean-variance approach, use past returns to assign optimal weights. Even though both past and expected returns should come from the same distribution, a formal test of whether this holds in practice has not been conducted yet. Thus, the study examines if the daily returns of 242 companies with continuous trading in the S&amp;amp;amp;P index come from the same distribution using the Kolmogorov-Smirnov, Cramér-Von Mises, and Wilcoxon rank-sum tests. The tests suggest that generally stock returns do come from the same distribution. However, the hypothesis is rejected during the Great Recession, with the rejection rate increasing as the forecast horizon increased. The rejection rate, using an array of macroeconomic variables, is found to record high levels of persistence. Although macroeconomic variables were not found to be statistically significant determinants of the rejection rate, market distress has a small but significant effect.