Times Of Financial Turbulence Research Articles

Too hot and too close. Bitcoin and gold dynamics during COVID times

Purpose This paper aims to analyze the connectedness between bitcoin (BTC) and other traditional assets (e.g. metals) in times of financial turbulence like the COVID pandemic. The purpose is to see to what extent BTC is mimicking the role precious metals are known for, that of being a reliable store of value. Design/methodology/approach The author relies on vector autoregressive modeling, as it yields a very flexible framework for forecasting and interpreting the interdependencies among variables, while providing a very intuitive framework when the underlying structural model is unknown. The author performs the analysis first for the whole sample and then for a “COVID-19 subsample.” Findings The author finds evidence supporting a stronger link between BTC and gold in COVID-19 times, with BTC the main driving force. The author quantifies the contribution of BTC to the surge in gold’s price during those early months of COVID-19 in the order of 28%, thus lending support to the idea of intrinsic or fundamental value in BTC. Practical implications Investors might consider including BTC in their portfolios as a long-term investment, very much like they do with gold. Social implications Cryptos at large represent an important check on the ever-expanding monetary policy pursued by central banks in recent times. Originality/value The COVID-19 pandemic has taken its toll on the world economy. It is thus timely to reevaluate the relationship between BTC and other assets such as gold and silver, which are traditionally seen as safe havens against uncertainty.

Rotationally invariant estimators on portfolio optimization to unveil financial risk’s states

Rotationally Invariant Estimators (RIE) are a new family of covariance matrix estimators based on random matrix theory and free probability. The family RIE has been proposed to improve the performance of an investment portfolio in the Markowitz model’s framework. Here, we apply state-of-the-art RIE techniques to improve the estimation of financial states via the correlation matrix. The Synthesized Clustering (SYNCLUS) and a dynamic programming algorithm for optimal one-dimensional clustering were employed to that aim. We found that the RIE estimations of the minimum portfolio risk increase the Active Information Storage (AIS) in the American and European markets. AIS’s local dynamic also mimics financial states’ behavior when estimating under the one-dimensional clustering algorithm. Our results suggest that in times of financial turbulence, RIE estimates can be of great advantage in minimizing risk exposure.

Instability spillovers in the banking sector: A spatial econometrics approach

In the study, we employ a dynamic spatial panel model to identify factors that are the sources of instability spillover in the banking sector. Using a sample of publicly listed banks, we document that the stability of a bank depends not only on its own characteristics and the macroeconomic conditions of its home country, but also on the stability of other banks in the same and other countries. We find weak evidence that the spillover effects are greater from domestic banks than from foreign ones. This study has significant implications for market regulators in terms of the role of country interdependence as a spillover mechanism during times of financial turbulence.

Optimize your Investment Portfolio in Bearish Markets

Since the 2008-2009 global financial crisis, VaR (Value-at-Risk) techniques have become critical tools for monitoring and predicting the market risk and liquidity of financial assets. These financial risk modeling techniques, which have been recognized by the Bank for International Settlements (BIS) or the Basel Committee on capital adequacy and bank regulations, measure and prevent any potential losses that arise, not only from securities’ price changes and the interdependence between the different types of assets (stocks, currencies, interest rates or commodities), but also from their negative tail co-movements in bearish market conditions. In the event of a financial crisis or market downturn, adequate liquidity risk modeling is advisable. In fact, the main advantage of VaR models is their focus on downside risk (i.e., the impact of the results of negative tails) and their direct interpretation in monetary terms. Nevertheless, particularly in times of financial turbulence, traditional VaR models do not properly consider nonlinear dependence between portfolio assets and become inefficient in illiquid market scenarios. Despite the advances in measurement models, obtaining precise market liquidity risk estimations and applying them to optimize portfolios continues to be a challenge for financial institutions.

New Moneys Under the New Normal? Bitcoin and Gold Interdependence During Covid Times

Bitcoin in particular and so-called cryptocurrencies in general have shaken up the financial world and seem to be claiming an increasing size of the market share. These new virtual assets present investors with significant opportunities, but also with significant risks. This paper analyzes the connection between one such crypto, bitcoin, and other traditional assets (e.g. metals) in times of financial turbulence. Our impulse-response function and variance decomposition analyses indicate that, as of late, bitcoin has become increasingly interdependent with gold, and seems just as suitable to hedge against market uncertainty—we believe this is a very timely conclusion given the pervasive uncertainty that dominates post-pandemic life.

An application of Regular Vine copula in portfolio risk forecasting: evidence from Istanbul stock exchange

<abstract> In times of financial turbulence, it is a well-documented fact that the co-movement of financial returns tends to increase leading to unexpected portfolio losses. The magnitude of the losses can severely be underestimated when the characteristics of univariate return series and the dependence structure between the returns are not represented well by a risk forecasting model. From a growing literature on the available multivariate modelling tools, this paper aims to investigate daily portfolio Value at Risk and Expected Shortfall forecasting performance of elliptical as well as Regular Vine copulas. For this purpose, return series of twelve stocks that are listed in Istanbul Stock Exchange are obtained for the period of June 2010 to December 2018. The series are modelled with univariate Generalized Auto-Regressive Conditional Heteroskedasticity models with Normal and Student's t innovations. Equally weighted portfolio returns are forecasted depending on the univariate GARCH marginals and their multivariate dependence structure. Estimated daily portfolio Value at Risk and Expected Shortfall values with varying levels are compared with the traditional Variance-Covariance and Dynamic Conditional Correlation Multivariate GARCH model estimates. While the models performed well at the Value at Risk backtests, according to the applied ES backtests R-vine copula GARCH found better at yielding more accurate Expected Shortfall forecasts. </abstract>

Does uncertainty predict cryptocurrency returns? A copula-based approach

ABSTRACTThis study is confined in analysing how the economic policy uncertainty (EPU) effects affect exchange rates on cryptocurrency assets in times of financial turbulence characterized by low confidence in the financial stock markets, and tranquil periods where the financial stock markets behave smoothly. Our research employs the D-Vine pair-copula method on daily selected cryptocurrency (Bitcoin, Ethereum and Ripple) prices within the period of the 10 August 2016 to the 23 February 2018. Our findings document the presence of the dependence between the US EPU and cryptocurrencies and indicate a significant correlation with Ethereum which exhibits a much better return.

Does financial inclusion mitigate credit boom-bust cycles?

Following up on claims that high and rising levels of financial inclusion might contribute to financial stability, we test whether level and progress in financial inclusion has an effect on the magnitude of a financial bust after a crisis. We do this for the global financial crisis and a sample of crisis episodes covering the period 2004–2017. We find some evidence that countries with more inclusive banking sectors show less pronounced credit busts in times of financial turbulence. However, higher borrower growth rates in the years preceding a crisis have no mitigating effect on the depth of the bust. Thus, it remains a policy challenge to expand financial inclusion without contributing a potentially destabilizing credit boom.

Change Detection and the Causal Impact of the Yield Curve

Causal relationships in econometrics are typically based on the concept of predictability and are established by testing Granger causality. Such relationships are susceptible to change, especially during times of financial turbulence, making the real‐time detection of instability an important practical issue. This article develops a test for detecting changes in causal relationships based on a recursive evolving window, which is analogous to a procedure used in recent work on financial bubble detection. The limiting distribution of the test takes a simple form under the null hypothesis and is easy to implement in conditions of homoskedasticity and conditional heteroskedasticity of an unknown form. Bootstrap methods are used to control family‐wise size in implementation. Simulation experiments compare the efficacy of the proposed test with two other commonly used tests, the forward recursive and the rolling window tests. The results indicate that the recursive evolving approach offers the best finite sample performance, followed by the rolling window algorithm. The testing strategies are illustrated in an empirical application that explores the causal relationship between the slope of the yield curve and real economic activity in the United States over the period 1980–2015.

Determinants of equity mutual fund flows – Evidence from the fund flow dynamics between Hong Kong and global markets

This paper identifies major determinants of equity mutual fund flows, which is essential for financial regulators and investors to understand potential sources of instability in domestic financial markets. Using a novel dataset of individual fund data and a fixed-effect quantile panel data regression, we find that fund flows to global equities outweigh other fund-specific factors, suggesting that, other things being equal, mutual funds’ portfolio rebalancing could strongly determine the direction and magnitude of mutual fund flows. Moreover, there are signs that the return-chasing behaviours of fund managers and investors amplify fund flows’ volatility in times of financial turbulence, resulting in a much stronger redemption of equity funds during market downturns. These findings underscore the importance of portfolio diversification and hedging strategies for fund managers and investors of mutual funds to avoid international financial contagion. They also draw an implication for introducing relevant macro-prudential tools to the asset management sector in Hong Kong to maintain financial stability.

Determinants of Mutual Fund Flows to Hong Kong Equities

This paper identifies major determinants of mutual fund flows to Hong Kong equities, which is essential for financial regulators and investors to understand potential sources of instability in domestic financial markets. We find that fund flows to global equities outweigh other fund-specific factors, suggesting that, other things being equal, mutual funds’ portfolio rebalancing could strongly determine the direction and magnitude of mutual fund flows in Hong Kong. Moreover, there are signs that the return-chasing behaviours of fund managers and investors amplify fund flows’ volatility in times of financial turbulence, resulting in a much stronger redemption of Hong Kong-invested funds during market downturns. We also find that fund outflows require more than eight quarters in extreme cases to be fully replenished. These findings underscore the importance of portfolio diversification and hedging strategies for fund managers and investors of mutual funds to avoid international financial contagion. They also draw an implication for introducing necessary macro-prudential tools to the asset management sector in Hong Kong to maintain financial stability.

Stability of cross-market bivariate return distributions during financial turbulence

The paper examines the stability of the bivariate stock return distributions across the G5 and five emerging markets in times of financial crisis using copula models. We find that the volatility dynamics as well as the dependency structures appear to be both country- and period-specific. Neither the bivariate distributions nor the associated parameters appear to be stable over time. It implies that the usefulness of the copula techniques may be limited particularly in times of financial turbulence. Our results strike a note of caution for the practitioners and policy makers in dealing with the phenomenon of financialization which draws much strength from the quantitative financial models.

Change Detection and the Causal Impact of the Yield Curve

Causal relationships in econometrics are typically based on the concept of predictability and are established in terms of tests for Granger causality. These causal relationships are susceptible to change, especially during times of financial turbulence, making the real-time detection of instability an important practical issue. This paper develops a test for detecting changes in causal relationships based on a recursive rolling window, which is analogous to the procedure used in recent work on financial bubble detection. The limiting distribution of the test takes a simple form under the null hypothesis and is easy to implement in conditions of homoskedasticity, conditional heteroskedasticity and unconditional heteroskedasticity. Simulation experiments compare the efficacy of the proposed test with two other commonly used tests, the forward recursive and the rolling window tests. The results indicate that both the rolling and the recursive rolling approaches offer good finite sample performance in situations where there are one or two changes in the causal relationship over the sample period, although the performance of the rolling window algorithm seems to be the best. The testing strategies are illustrated in an empirical application that explores the causal impact of the slope of the yield curve on real economic activity in the United States over the period 1985–2013.

Performance drivers of shipping loans: An empirical investigation

Credit risk is a major issue for lenders and borrowers, threatening the reliability of global logistics operations. Enhanced mechanisms of credit risk analysis are needed to safeguard banks and the flow of goods in supply chains. Little emphasis has been given to the contextual examination of such factors, either in terms of market conditions or the particular characteristics of different industries. This paper investigates the varying importance of a number of factors connected with the performance of corporate bank loans during times of financial turbulence in the shipping industry. Little extant literature exists on default risk drivers for loans made to shipping companies for new build vessels or second-hand ship purchases. A binary logit model is used to examine the criteria for assessing the security of shipping loans issued by banks. Thirty shipping loans made during the period 2005–2009 are examined. Results suggest that financial factors, non-financial factors, shipowners׳ experience, and employability and market risk indicators are the best criteria for evaluating the performance of shipping loans during turbulent market conditions and periods when financing options are restricted. The paper makes a specific contribution to the literature on risk management with regard to credit risk analysis by highlighting shipping specific factors and their importance for risk measurement. The results are of interest to banks seeking to accurately assess the credibility of shipping loans; shipowners, who can identify credit risk factors on which to focus; and supply chain participants where unfulfilled bank financing can cause disruptions to their logistics operations.

Extreme loss risk in financial turbulence – evidence from the global financial crisis

PurposeThis study aims to examine the stock returns distributions in ten countries in the periods before and after the global financial crisis (GFC) to evaluate how well the empirical distributions conformed to the extreme value theory (EVT) which underlies a family of risk management models.Design/methodology/approachThe authors’ sample consists of the G5 countries and the five leading emerging economies. Parameters of the General Pareto Distribution (GPD) for each country are estimated for the pre‐ and the crisis period. The authors follow a two‐step procedure: a GARCH(1,1) model is fitted to the historical return data by pseudo maximum likelihood method; Hill's GPD tail estimation procedure is employed on the residuals from the first step. Goodness‐of‐fit is evaluated for the empirical distributions.FindingsThe authors find that the EVT explains the observed distributions well in both the pre‐GFC and the GFC periods, with the important exceptions of the US and the UK markets in the crisis period. Moreover, the estimated distribution parameters are quite different for the two periods. The results underscore the inadequacy of the quantitative risk models in times of financial turbulence, and the need for prudential exercise of judgment in risk management.Originality/valueThe global financial crisis (GFC) provides a unique and historical experiment to evaluate the models of tail distributions. Although the EVT provides a sound basis for modeling extreme risks, the study highlights the fundamental problem of dealing with uncertainty.

Dynamic Factor Models With Macro, Frailty, and Industry Effects for U.S. Default Counts: The Credit Crisis of 2008

We develop a high-dimensional, nonlinear, and non-Gaussian dynamic factor model for the decomposition of systematic default risk conditions into latent components for (1) macroeconomic/financial risk, (2) autonomous default dynamics (frailty), and (3) industry-specific effects. We analyze discrete U.S. corporate default counts together with macroeconomic and financial variables in one unifying framework. We find that approximately 35% of default rate variation is due to systematic and industry factors. Approximately one-third of this systematic variation is captured by the macroeconomic and financial factors. The remainder is captured by frailty (40%) and industry (25%) effects. The default-specific effects are particularly relevant before and during times of financial turbulence. We detect a build-up of systematic risk over the period preceding the 2008 credit crisis. This article has online supplementary material.

Dynamic Factor Models with Macro, Frailty and Industry Effects for US Default Counts: The Credit Crisis of 2008

We develop a high-dimensional and partly nonlinear non-Gaussian dynamic factor model for the decomposition of systematic default risk conditions into a set of latent components that correspond with macroeconomic/financial, default-specific (frailty), and industry-specific effects. Discrete default counts together with macroeconomic and financial variables are modeled simultaneously in this framework. In our empirical study based on defaults of U.S. firms, we find that approximately 35 percent of default rate variation is due to systematic and industry factors. Approximately one third of systematic variation is captured by macroeconomic/financial factors. The remainder is captured by frailty (about 40 percent) and industry (about 25 percent) effects. The default-specific effects are particularly relevant before and during times of financial turbulence. For example, we detect a build-up of systematic risk over the period preceding the 2008 credit crisis.

P06.03 MRSA PCR screening: is it still a cost effective option during times of financial turbulence in NHS hospitals?

P06.03 MRSA PCR screening: is it still a cost effective option during times of financial turbulence in NHS hospitals?

Market and Model Credit Default Swap Spreads: Mind the Gap!

Structural models of default establish a relation across the fair values of various asset classes (equity, bonds, credit derivatives) referring to the same company. In most circumstances such relation is verified in practice, as different financial assets tend to move in the same direction at similar speed. However, occasional deviations from the theoretical fair values occur, especially in times of financial turmoil. Understanding how the dynamics of the theoretical fair values of various assets compares to that of their market values is crucial to a number of market participants. This paper investigates whether a popular structural model, the CreditGrades approach proposed by Finger (2002), Stamicar and Finger (2005), succeeds in explaining the dynamic relation between equity/option variables and Credit Default Swap (CDS) premia at individual company level. We find that CDS model spreads display a significant correlation with CDS market spreads. However, the gap between the two is time varying and widens substantially in times of financial turbulence. The analysis of the gap dynamics reveals that this is partly due to episodes of decoupling between equity and credit markets, and partly due to shortcomings of the model. Finally, we observe that model spreads tend to predict market spreads.