nep-rmg New Economics Papers
on Risk Management
Issue of 2023‒01‒02
twenty-two papers chosen by
Stan Miles
Thompson Rivers University

  1. When uncertainty decouples expected and unexpected losses By Juselius, Mikael; Tarashev, Nikola A.
  2. How CBO Analyzes Public-Private Risk Sharing in Insurance Markets By Congressional Budget Office
  3. Fire Sales and Ex Ante Valuation of Systemic Risk: A Financial Equilibrium Networks Approach By Spiros Bougheas; Adam Spencer
  4. Mod-Poisson approximation schemes: Applications to credit risk By Pierre-Lo\"ic M\'eliot; Ashkan Nikeghbali; Gabriele Visentin
  5. The Market-Based Asset Price Probability By Olkhov, Victor
  6. Investor base and idiosyncratic volatility of cryptocurrencies By Amin Izadyar; Shiva Zamani
  7. An Analysis of Financial Conglomerate Resilience: A Perspective on bancassurance in France By Cyril Pouvelle.
  8. Introduction of the composite indicator of cyclical systemic risk in Croatia: possibilities and limitations By Tihana Škrinjarić
  9. Counterparty choice, maturity shifts and market freezes: lessons from the e-MID interbank market By Saroyan, Susanna
  10. Estimation of optimal portfolio compositions for small sampleand singular covariance matrix By Bodnar, Taras; Mazur, Stepan; Nguyen, Hoang
  11. A Quantity-Based Approach to Constructing Climate Risk Hedge Portfolios By Georgij Alekseev; Stefano Giglio; Quinn Maingi; Julia Selgrad; Johannes Stroebel
  12. Optimal disclosure risk assessment By Camerlenghi, Federico; Favaro, Stefano; Naulet, Zacharie; Panero, Francesca
  13. Prospect theory and asset allocation By Fortin, Ines; Hlouskova, Jaroslava
  14. Understanding Cryptocoins Trends Correlations By Pasquale De Rosa; Valerio Schiavoni
  15. How Can We Learn from Borrowers’ Online Behaviors? The Signal Effect of Borrowers’ Platform Involvement on Their Credit Risk By Tang, Xinyin; Feng, Chong; Zhu, Jianping; He, Minna
  16. An Infinite Hidden Markov Model with Stochastic Volatility By Li, Chenxing; Maheu, John M; Yang, Qiao
  17. Last passage American cancellable option in L\'evy models By Zbigniew Palmowski; Pawe{\l} St\k{e}pniak
  18. Why does risk matter more in recessions than in expansions? By Andreasen, Martin Møller; Caggiano, Giovanni; Castelnuovo, Efrem; Pellegrino, Giovanni
  19. Inequality and risk preference By Harry Pickard; Thomas Dohmen; Bert Van Landeghem
  20. Conditional density forecasting: a tempered importance sampling approach By Montes-Galdón, Carlos; Paredes, Joan; Wolf, Elias
  21. Gold and Silver as Safe Havens: A Fractional Integration and Cointegration Analysis By Guglielmo Maria Caporale; Luis A. Gil-Alana
  22. Bond convenience curves and funding costs By Nissinen, Juuso; Sihvonen, Markus

  1. By: Juselius, Mikael; Tarashev, Nikola A.
    Abstract: A parsimonious extension of a well-known portfolio credit-risk model allows us to study a salient stylized fact - abrupt switches between high- and low-loss phases - from a risk-management perspective. As uncertainty about phase switches increases, expected losses decouple from unexpected losses, which reflect a high percentile of the loss distribution. Banks that ignore this decoupling have shortfalls of loss-absorbing resources, which is more detrimental if the portfolio is more diversified within a phase. Likewise, the risk-management benefits of improving phase-switch forecasts increase with diversification. The analysis of these findings leads us to an empirical method for comparing the degree of within-phase default clustering across portfolios.
    Keywords: Expected loss provisioning,Bank capital,Unexpected losses,Credit cycles,Portfolio credit risk
    JEL: G21 G28 G32
    Date: 2022
  2. By: Congressional Budget Office
    Abstract: In some insurance markets, the federal government and private insurance companies share the financial risk of covering insured parties. In this report, CBO outlines how it analyzes three different forms of public-private risk sharing that are used to provide terrorism insurance, crop insurance, and flood insurance. The agency also describes how each form of risk sharing affects the federal budget.
    JEL: G10 G18 G22 H12 H50 H60 H84 Q54 Q58
    Date: 2022–11–30
  3. By: Spiros Bougheas; Adam Spencer
    Abstract: We introduce endogenous fire sales into a simple network model. For any given initial distribution of shocks across the network, we develop a clearing algorithm to solve for the financial equilibrium. We then utilise the results to perform ex ante risk assessment and derive risk premia for every balance sheet item where liabilities are differentiated according to priority rights. We find that risk premia reflect both idiosyncratic risk and risk of contagion (network risk). Moreover, we show that network risk magnifies the gap between the risk premia of equity and debt. We also perform comparative statics, showing that changes to the distribution of shocks and network structure can have substantial effects on the level of systemic losses.
    Keywords: Networks, Fire Sales, Systemic Risk Premia, Risk Assessment
    JEL: G33 G32 D85
    Date: 2022–11
  4. By: Pierre-Lo\"ic M\'eliot; Ashkan Nikeghbali; Gabriele Visentin
    Abstract: We introduce a new numerical approximation method for functionals of factor credit portfolio models based on the theory of mod-$\phi$ convergence and mod-$\phi$ approximation schemes. The method can be understood as providing correction terms to the classic Poisson approximation, where higher order corrections lead to asymptotically better approximations as the number of obligors increases. We test the model empirically on two tasks: the estimation of risk measures ($\mathrm{VaR}$ and $\mathrm{ES}$) and the computation of CDO tranche prices. We compare it to other commonly used methods -- such as the recursive method, the large deviations approximation, the Chen--Stein method and the Monte Carlo simulation technique (with and without importance sampling) -- and we show that it leads to more accurate estimates while requiring less computational time.
    Date: 2022–10
  5. By: Olkhov, Victor
    Abstract: We consider the time-series records of the market trade values and volumes as the origin of the asset price stochasticity and describe random price properties through statistical moments of the market trade values and volumes. The market-based price probability differs from the conventional price probability proportional to number of trades at price p. We show that the market-based price probability results no correlations between n-th degrees of price and trade volume but doesn’t cause statistical independence of price and trade volume. We derive the market-based correlation between price and squares of the trade volumes. Time-series records of the market trade values and volumes allow assess only finite number m of statistical moments and define first m price statistical moments. Approximations of the price characteristic function that match first m price statistical moments generate approximations of the market-based price probability. That approach unifies description of the asset price, returns, inflation and their volatilities as functions of the market trade values and volumes statistical moments. That describes the case when investor’s market trades impact asset price probability. Market-based approach uncovers vital fault of the Value-at-Risk (VaR) as most conventional hedging tool. We show that accuracy of VaR assessment at horizon T is determined by precision of predictions of the market trade values and volumes statistical moments and depends on accuracy of macroeconomic forecasting. The market-based approach to price probability establishes direct economic ties between the asset pricing, market stochasticity and economic theory.
    Keywords: asset price; price probability; returns; inflation; market trades
    JEL: C01 C58 E31 E37 G12 G17
    Date: 2022–05–15
  6. By: Amin Izadyar; Shiva Zamani
    Abstract: This paper investigates how changes in investor base is related to idiosyncratic volatility in cryptocurrency markets. For each cryptocurrency, we set change in its subreddit followers as a proxy for the change in its investor base, and find out that the latter can significantly increase cryptocurrencies idiosyncratic volatility. This finding is not subsumed by effects of size, momentum, liquidity and volume and is robust to various measures of idiosyncratic volatility.
    Date: 2022–11
  7. By: Cyril Pouvelle.
    Abstract: The objective of the paper is to shed light on the effects of financial conglomerate membership on banks' profitability, risk-taking, default risk and intragroup funding resilience. To that end, we estimate models of the entities' Return on Assets (ROA), the standard deviation of the ROA, the Z-score, a measure of banks' default risk, and intragroup funding standard deviation, using quarterly supervisory data available at the ACPR on a solo basis for French banks. We find that the financial conglomerate membership has a dampening effect on the volatility of the ROA and of intragroup funding growth, an effect that is even stronger in periods of financial stress. Moreover, financial conglomerate membership is found to reduce banks' default risk as it has a positive effect on a bank's Z-score overall. By contrast, no significant effect is shown on the ROA. By and large, these results invalidate the moral hazard assumption associated with financial conglomerates but rather highlight financial solidarity mechanisms within conglomerates. <p> L'objectif de ce papier est de mettre en lumière les effets pour une entité de l'appartenance à un conglomérat financier en matière de rentabilité, de prise de risque, de risque de défaut et résilience du financement intragroupe. À cette fin, nous estimons plusieurs modèles du rendement des actifs (ROA), de l'écart-type du ROA, du Z-score, une mesure du risque de défaut d'une entreprise, et de l'écart-type de la croissance du financement intragroupe, en utilisant les données de supervision disponibles à l'ACPR sur base sociale pour les banques françaises, afin de comparer les entités appartenant à un conglomérat financier par rapport à celles des autres banques. Nos résultats indiquent que la participation à un conglomérat financier a un effet stabilisateur sur la volatilité du ROA et la croissance du financement intragroupe et que cet effet est même plus fort en période de stress financier. Par ailleurs, la participation à un conglomérat financier réduit le risque de défaut des banques car elle a un effet positif sur leur Z-score. En revanche, aucun effet significatif n'est trouvé sur le niveau du ROA. Au total, ces résultats invalident l'hypothèse d'aléa moral associée à l'appartenance à un conglomérat financier mais illustrent au contraire les bénéfices de l'appartenance à un conglomérat du fait des mécanismes de solidaritéfinancière intra-groupe.
    Keywords: Banks, Financial Policy and Risk Management, Financial Stability; Banques, politique financière et gestion des risques, stabilité financière
    JEL: G21 G32
    Date: 2022
  8. By: Tihana Škrinjarić (Hrvatska narodna banka, Hrvatska)
    Abstract: Macroprudential policy has the important task of monitoring the accumulation of cyclical systemic risks, using a wide range of indicators. Decisions on the use of instruments that seek to mitigate the pro-cyclicality of the system should be made according to properly defined and stable indicators that signal future trends in the cycle itself. In its Recommendation, the European Systemic Risk Board considers several important categories of indicators for monitoring cyclical risks. Since the credit gap, the main indicator of cyclical risks, has shown numerous shortcomings in practice over the years, composite indicators have been developed in the literature. As there has been no such composite indicator in Croatia so far, this research considers several popular approaches to constructing composite indicators of cyclical risks for Croatia. As there are several different approaches currently available, this research considers their characteristics, advantages and shortcomings, with special reference to Croatian data. Comparing the composite financial cycle indicator, the cyclogram, the systemic cyclical risk indicator, as well as additional possibilities of data aggregation in terms of principal component analysis and the overheating index, the results indicate that the issue of defining an adequate indicator for Croatia is a demanding task. This is due to the short time series, the absence of characteristics of other types of crises that are available for other countries, the instability of certain variables relevant for monitoring cyclical risks, complexity of communication with the public, etc. Finally, based on the discussion, the best indicator is chosen, and the possibilities of calibrating the countercyclical capital buffer are considered. This paper provides an overview of different approaches, with a special focus on a comparison of them, which has not been dealt with in the literature. It provides proposals for improving individual indicators and analyses the possibility of calibrating the countercyclical capital buffer.
    Keywords: systemic risk, macroprudential policy, countercyclical capital buffer, composite indicators, cyclical risks
    JEL: C14 C32 E32
    Date: 2022–11–29
  9. By: Saroyan, Susanna
    Abstract: We explore the impact of relationship lending on the interbank debt maturity structure of banks using data from the e-MID market covering both pre- and post-Lehman periods. We study the term structure and maturity shortening of interbank lending as an indicator of risk in times of stress. We identify bank-level and pair-level variables which are shown to contain information about the behaviour of lending relations during times of stress. Using a two-part fractional response model we show that durable liquidity relationships increase the probability of contracting term loans, but do not prevent maturity shortening during periods of acute stress. Finally, we find that lenders with concentrated short-term interbank liability structure tend to reduce their own long term lending, which confirms the roll-over risk viewpoint of term interbank market freeze. Our findings are relevant for the modeling of interbank networks under stress and the design of forward looking stress tests for the banking system.
    Keywords: Interbank markets; liquidity; market freeze; maturity shift; relationship lending; roll-over risk; interbank networks; network dynamics; counterparty risk
    JEL: E44 E58 G01 G21 G28 C25
    Date: 2022–11
  10. By: Bodnar, Taras (Stockholm University); Mazur, Stepan (Örebro University School of Business); Nguyen, Hoang (Örebro University School of Business)
    Abstract: In the paper we consider the optimal portfolio choice problem under parameter uncertainty when the covariance matrix of asset returns is singular. Very useful stochastic representations are deduced for the characteristics of the expected utility optimal portfolio. Using these stochastic representations, we derive the moments of higher order of the estimated expected return and the estimated variance of the expected utility optimal portfolio. Another line of applications leads to their asymptotic distributions obtained in the high-dimensional setting. Via a simulation study, it is shown that the derived high-dimensional asymptotic distributions provide good approximations of the exact ones even for moderate sample sizes.
    Keywords: singular Wishart distribution; mean-variance portfolio; Moore-Penrose inverse
    JEL: G11
    Date: 2022–12–06
  11. By: Georgij Alekseev; Stefano Giglio; Quinn Maingi; Julia Selgrad; Johannes Stroebel
    Abstract: We propose a new methodology to build portfolios that hedge the economic and financial risks from climate change. Our quantity-based approach exploits information on how mutual fund managers trade in response to idiosyncratic changes in their climate risk beliefs. We exploit two types of idiosyncratic belief shocks: (i) instances when fund advisers experience local extreme heat events that are known to shift climate change beliefs, and (ii) instances when fund managers change the language in shareholder disclosures to express concerns about climate risks. We use the funds’ observed portfolio changes around such idiosyncratic belief shocks to predict how investors will reallocate their capital in response to aggregate climate news shocks that shift the beliefs and asset demands of many investors and thus move equilibrium prices. We show that a portfolio that is long stocks that investors tend to buy after experiencing negative idiosyncratic climate belief shocks, and short stocks that investors tend to sell, appreciates in value in periods with negative aggregate climate news shocks. Our quantity-based portfolios have superior out-of-sample hedge performance compared to portfolios constructed using existing alternative methods. The key advantage of the quantity-based approach is that it learns from rich cross-sectional trading responses rather than time-series price information, which is particularly limited in the case of newly emerging risks such as those from climate change. We also demonstrate the versatility of the quantity-based approach by constructing successful hedge portfolios for aggregate unemployment and house price risk.
    JEL: G10 G11 G12 G14 Q50 Q54
    Date: 2022–12
  12. By: Camerlenghi, Federico; Favaro, Stefano; Naulet, Zacharie; Panero, Francesca
    Abstract: Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample of size n from a finite population of size n¯ = n + λn, with λ > 0, such that each sample record contains two disjoint types of information: identifying categorical information and sensitive information. Any decision about releasing data is supported by the estimation of measures of disclosure risk, which are defined as discrete functionals of the number of sample records with a unique combination of values of identifying variables. The most common measure is arguably the number τ 1 of sample unique records that are population uniques. In this paper, we first study nonparametric estimation of τ 1 under the Poisson abundance model for sample records. We introduce a class of linear estimators of τ 1 that are simple, computationally efficient and scalable to massive datasets, and we give uniform theoretical guarantees for them. In particular, we show that they provably estimate τ 1 all of the way up to the sampling fraction (λ + 1) −1 ∝ (log n) −1, with vanishing normalized mean-square error (NMSE) for large n. We then establish a lower bound for the minimax NMSE for the estimation of τ 1, which allows us to show that: (i) (λ + 1) −1 ∝ (log n) −1 is the smallest possible sampling fraction for consistently estimating τ 1; (ii) estimators' NMSE is near optimal, in the sense of matching the minimax lower bound, for large n. This is the main result of our paper, and it provides a rigorous answer to an open question about the feasibility of nonparametric estimation of τ 1 under the Poisson abundance model and for a sampling fraction (λ + 1) −1
    Keywords: Disclosure risk assessment; microdata sample; nonparametric inference; optimal minimax procedure; Poisson abundance model; polynomial approximation
    JEL: C1
    Date: 2021–04–01
  13. By: Fortin, Ines (Macroeconomics and Business Cycles, Institute for Advanced Studies, Vienna, Austria); Hlouskova, Jaroslava (Macroeconomics and Business Cycles, Institute for Advanced Studies, Vienna, Austria and Department of Economics, Faculty of National Economy, University of Economics in Bratislava, Slovak Republic)
    Abstract: We study the asset allocation of an investor with prospect theory (PT) preferences. First, we solve analytically the two-asset problem of the PT investor for one risk-free and one risky asset and find that loss aversion and the reference return affect differently less ambitious investors and more ambitious investors. Second, we empirically investigate the performance of a PT portfolio when diversifying among a stock market index, a government bond and gold, in Europe and the US. We focus on investors with PT preferences under different scenarios regarding the reference return and the degree of loss aversion and compare their portfolio performance with the performance of investors under CVaR, risk neutral, linear loss averse and in particular mean-variance (MV) preferences. We find that, in the US, PT portfolios signiffcantly outperform (in terms of returns) mean-variance portfolios in the majority of cases. Also with respect to riskadjusted performance, PT investment outperforms MV investment in the US. Similar results, however, can not be observed in Europe. Finally, we analyze asymmetric effects along economic uncertainty and observe that PT investment leads to higher returns than MV investment in times of larger economic uncertainty, especially in the US.
    Keywords: prospect theory, loss aversion, portfolio allocation, mean-variance portfolios, investment strategy
    JEL: D81 G02 G11 G15
    Date: 2022–12
  14. By: Pasquale De Rosa; Valerio Schiavoni
    Abstract: Crypto-coins (also known as cryptocurrencies) are tradable digital assets. Notable examples include Bitcoin, Ether and Litecoin. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins across owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices across all different sets of crypto-assets, it remains unclear what and if there are tight relations between the trading prices of different cryptocoins. Major coin exchanges (i.e., Coinbase) provide trend correlation indicators to coin owners, suggesting possible acquisitions or sells. However, these correlations remain largely unvalidated. In this paper, we shed lights on the trend correlations across a large variety of cryptocoins, by investigating their coin-price correlation trends over a period of two years. Our experimental results suggest strong correlation patterns between main coins (Ethereum, Bitcoin) and alt-coins. We believe our study can support forecasting techniques for time-series modeling in the context of crypto-coins. We release our dataset and code to reproduce our analysis to the research community.
    Date: 2022–11
  15. By: Tang, Xinyin; Feng, Chong; Zhu, Jianping; He, Minna
    Abstract: A growing number of borrowers are applying for digital credit through Internet platforms due to the integration of digital credit services the Internet. However, further empirical evidence is needed to explore how a borrower’s platform behaviors affect its credit risk. As such, our study uses signaling theory as the theoretical foundation to explore the overall effects of a borrower's platform involvement intensity on its credit risk based on a large consumer credit application dataset. The main finding shows the increase in a borrower’s involvement intensity reduces its likelihood of defaulting. We attribute it to the platform's belief that borrowers with high involvement intensity have the higher value to the platform. In addition, we examine how a borrower's involvement intensity is moderated by several factors, such as the stability of its platform involvement intensity and its credit history. Due to the importance of digital credit services in microfinance, we have provided useful implications for achieving win-win outcomes in the credit market for the stakeholders.
    Date: 2022–12–01
  16. By: Li, Chenxing; Maheu, John M; Yang, Qiao
    Abstract: This paper extends the Bayesian semiparametric stochastic volatility (SV-DPM) model of Jensen and Maheu (2010). Instead of using a Dirichlet process mixture (DPM) to model return innovations, we use an infinite hidden Markov model (IHMM). This allows for time variation in the return density beyond that attributed to parametric latent volatility. The new model nests several special cases as well as the SV-DPM. We also discuss posterior and predictive density simulation methods for the model. Applied to equity returns, foreign exchange rates, oil price growth and industrial production growth, the new model improves density forecasts, compared to the SV-DPM, a stochastic volatility with Student-t innovations and other fat-tailed volatility models.
    Keywords: stochastic volatility; Markov-switching; MCMC; Bayesian; nonparametric; semiparametric
    JEL: C11 C14 C22 C53 C58
    Date: 2022–11–25
  17. By: Zbigniew Palmowski; Pawe{\l} St\k{e}pniak
    Abstract: We derive the explicit price of the perpetual American put option cancelled at the last passage time of the underlying above some fixed level. We assume the asset process is governed by a geometric spectrally negative L\'evy process. We show that the optimal exercise time is the first epoch when asset price process drops below an optimal threshold. We perform numerical analysis as well considering classical Black-Scholes models and the model where logarithm of the asset price has additional exponential downward shocks. The proof is based on some martingale arguments and fluctuation theory of L\'evy processes.
    Date: 2022–12
  18. By: Andreasen, Martin Møller; Caggiano, Giovanni; Castelnuovo, Efrem; Pellegrino, Giovanni
    Abstract: This paper uses a nonlinear vector autoregression and a non-recursive identiÖcation strategy to show that an equal-sized uncertainty shock generates a larger contraction in real activity when growth is low (as in recessions) than when growth is high (as in expansions). An estimated New Keynesian model with recursive preferences and approximated to third order around its risky steady state replicates these state-dependent responses. The key mechanism behind this result is that Örms display a stronger upward nominal pricing bias in recessions than in expansions, because recessions imply higher ináation volatility and higher marginal utility of consumption than expansions.
    Keywords: New Keynesian Model,Nonlinear SVAR,Non-recursive identiÖcation,State-dependent uncertainty shock,Risky steady state
    Date: 2021
  19. By: Harry Pickard (: Newcastle University Business School, Newcastle University, United Kingdom); Thomas Dohmen (Economics Department, University of Bonn, Germany); Bert Van Landeghem (Department of Economics, University of Sheffield, S1 4DT, UK)
    Abstract: This paper studies the relationship between income inequality and risk taking. Increased income inequality is likely to enlarge the scope for upward comparisons and, in the presence of reference-dependent preferences, to increase willingness to take risks. Using a globally representative dataset on risk preference in 76 countries, we empirically document that the distribution of income in a country has a positive and significant link with the preference for risk. This relationship is remarkably precise and holds across countries and individuals, as well as alternate measures of inequality. We find evidence that individuals who are more able to understand inequality and individuals who fall behind their inherent point of reference increase their preference for risk. Two complementary instrumental variable approaches support a causal interpretation of our results.
    Keywords: Income inequality; risk preference; risk sensitivity
    JEL: D91 O15 D81 D01
    Date: 2022–12
  20. By: Montes-Galdón, Carlos; Paredes, Joan; Wolf, Elias
    Abstract: This paper proposes a new and robust methodology to obtain conditional density forecasts, based on information not contained in an initial econometric model. The methodology allows to condition on expected marginal densities for a selection of variables in the model, rather than just on future paths as it is usually done in the conditional forecasting literature. The proposed algorithm, which is based on tempered importance sampling, adapts the model-based density forecasts to target distributions the researcher has access to. As an example, this paper shows how to implement the algorithm by conditioning the forecasting densities of a BVAR and a DSGE model on information about the marginal densities of future oil prices. The results show that increased asymmetric upside risks to oil prices result in upside risks to inflation as well as higher core-inflation over the considered forecasting horizon. Finally, a real-time forecasting exercise yields that introducing market-based information on the oil price improves inflation and GDP forecasts during crises times such as the COVID pandemic. JEL Classification: C11, C53, E31, E37
    Keywords: Bayesian analysis, forecasting, importance sampling, inflation-at-risk
    Date: 2022–12
  21. By: Guglielmo Maria Caporale; Luis A. Gil-Alana
    Abstract: This paper investigates whether gold and silver can be considered safe havens by examining their long-run linkages with 22 stock price indices. More specifically, the stochastic properties of the differential between gold/silver prices and 22 stock indices are analysed applying fractional integration/cointegration methods to daily data, first for a sample from January 2010 until December 2019, then for one from January 2020 until July 2022 which includes the Covid-19 pandemic. The results can be summarised as follows. In the case of the pre-Covid-19 sample ending in December 2019, mean reversion is found for the gold price differential vis-à-vis BEF, BSE, CAC, DOW, KLS, KS1, MXX, N100, NAS, NYA and SP5 and for both differentials vis-à-vis CAC, KLS and N100, i.e. the evidence is mixed on whether these precious metals can be seen as safe havens, though it appears that this property characterises gold in a slightly higher number of cases. By contrast, when using the sample starting in January 2020, the evidence in favour of gold and silver as possible safe havens is pretty conclusive since mean reversion is only found in a single case, namely that of the gold differential vis-à-vis NZX.
    Keywords: gold and silver, hedge, safe heaven, fractional integration and cointegration
    JEL: C22 C32 F30 F36 G01 G15
    Date: 2022
  22. By: Nissinen, Juuso; Sihvonen, Markus
    Abstract: A convenience yield represents a difference between yield on a safe bond and yield on a synthetic safe bond, constructed by combining a risky bond with a CDS contract. We explain the shapes of eurozone sovereign convenience curves using a model in which arbitrageurs face higher funding costs on bonds with credit risk and bond demand shocks induce funding risk. We provide novel causal evidence for our mechanism using variation in funding costs generated through exogenous haircut category changes. Changes in convenience yields represent a key transmission channel of unconventional monetary policy to bond yields.
    Keywords: Sovereign bond convenience yields,money markets,asset pricing with frictions,monetary policy
    JEL: G12 G15
    Date: 2022

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