Downside Market Risk Research Articles

The heterogeneous reaction of green and conventional bonds to exogenous shocks and the hedging implications

Unlike most previous studies considering the yields on green bonds versus conventional bonds or the hedging ability of green bonds against downside market risk, the main purpose of this paper is to paper examine the short-term response of green and conventional bonds to the Russia-Ukraine conflict shock and the US Federal monetary policy tightening. Using daily data from August 3, 2021 to March 29, 2022, this paper conducts an event-based study (Cumulative Abnormal Returns, CAR) and then applies a hedging analysis in the context of increasing geopolitical risk and financial stress. The analysis reveals that green bonds exhibit a stronger reaction to the Russia-Ukraine conflict and the US Federal rate hike than conventional, municipal, and treasury bonds in different time frames. Compared to conventional, municipal, and treasury bonds, green bonds offer lower negative CAR responses during the event window and the [-5, +5] period, suggesting a rigidity feature. The dynamic correlation and hedging analysis indicate that green bonds, unlike the other bonds indices, have a negative dynamic correlation with both geopolitical risks and financial stress, implying a hedging ability around the conflict shock and the Federal tightening cycle. These findings enrich the existing literature on green bonds, offering a wide range of applications for investment managers and policymakers.

Valuation of guaranteed minimum accumulation benefits (GMABs) with physics-inspired neural networks

Abstract Guaranteed minimum accumulation benefits (GMABs) are retirement savings vehicles that protect the policyholder against downside market risk. This article proposes a valuation method for these contracts based on physics-inspired neural networks (PINNs), in the presence of multiple financial and biometric risk factors. A PINN integrates principles from physics into its learning process to enhance its efficiency in solving complex problems. In this article, the driving principle is the Feynman–Kac (FK) equation, which is a partial differential equation (PDE) governing the GMAB price in an arbitrage-free market. In our context, the FK PDE depends on multiple variables and is difficult to solve using classical finite difference approximations. In comparison, PINNs constitute an efficient alternative that can evaluate GMABs with various specifications without the need for retraining. To illustrate this, we consider a market with four risk factors. We first derive a closed-form expression for the GMAB that serves as a benchmark for the PINN. Next, we propose a scaled version of the FK equation that we solve using a PINN. Pricing errors are analyzed in a numerical illustration.

The effects of high uncertainty risk on international stock markets

AbstractWe conduct an international analysis of the cross-sectional risk premiums of uncertainty risk factors in addition to traditional risk factors. We consider international stock markets in five regions separately. We measure uncertainty by the local and US economic policy uncertainty indices. Economic policy uncertainty risk has negative risk premiums. This implies that investors get lower returns for assets with high uncertainty betas. We further analyze a nonlinear relationship between excess returns and uncertainty risk by adding the downside economic policy uncertainty risk factor which captures high levels of uncertainty, similar to downside market risk. The downside uncertainty risk factor has negative risk premiums.

Is downside risk priced in cryptocurrency market?

This paper explores the cryptocurrency market through the prism of traditional multifactor asset-pricing models with particular attention to the downside market risk. The analysis for about 2000 cryptocurrencies reveals that there is a significant heterogeneity in the exposure to the downside market risk, and that a higher downside risk exposure is associated with higher average returns. This finding is irrespective of whether we use cryptocurrency market or equity market as the benchmark. The extra downside risk is priced with a statistically significant premium in cross-sectional regressions. Adding the downside risk component to the CAPM and the 3-factor Liu, Tsyvinski, and Wu (2022) model for cryptocurrencies improves the explanatory power of the models significantly. The downside risk is orthogonal to the size and momentum risks and constitutes an important component in the multifactor cryptocurrency pricing model. The result is robust in sub-samples of cryptocurrencies with different market capitalizations.

Corporate Social Responsibility Disclosure, Market Supervision, and Green Investment

ABSTRACT As a crucial business practice, corporate social responsibility (CSR) has attracted the attention of companies and market participants worldwide. This study examines the effects of CSR disclosure on green investment and analyses whether effects vary during China’s stock market crash. The results reveal a mismatch between green investment and CSR disclosure and that the market supervisions act as a moderator. Furthermore, we observe that the nature of CSR disclosure as a self-interested tool becomes highly pronounced during extreme risk events. This observation suggests that a company facing downside market risk will compromise many of its environmental performances. Accordingly, we propose improved measures for regulators and investors in response to the gap between CSR disclosure and actual environmental performance.

Default risk, macroeconomic conditions, and the market skewness risk premium

Previous literature finds that stocks with low market skewness risk outperform stocks with high market skewness risk. Using the portfolio sort approach, we show that this market skewness risk premium is much more pronounced among stocks with low default risk or under good economic conditions. The premium vanishes among stocks with high default risk or under poor economic conditions. Further, the market skewness risk is negatively priced only for stocks with low default risk or in good economic times. It is not priced when firm-level default risk is high or when macroeconomic conditions are bad. Our findings suggest that the market skewness risk premium and the pricing of market skewness risk are conditional on both firm-level default risk and country-level macroeconomic conditions. This is because investors’ aversion to default risk and downside market risk changes their attitudes towards positive market skewness risk.

Market risk factors analysis for an international mining company. Multi-dimensional, heavy-tailed-based modelling

Mining companies to properly manage their operations and be ready to make business decisions, are required to analyse potential scenarios for main market risk factors. The most important risk factors for KGHM, one of the biggest companies active in the metals and mining industry, are the price of copper (Cu), traded in US dollars, and the Polish zloty (PLN) exchange rate (USDPLN). The main scope of the paper is to understand the mid- and long-term dynamics of these two risk factors. For a mining company it might help to properly evaluate potential downside market risk and optimize hedging instruments. From the market risk management perspective, it is also important to analyse the dynamics of these two factors combined with the price of copper in Polish zloty (Cu in PLN), which jointly drive the revenues, cash flows, and financial results of the company. Based on the relation between analysed risk factors and distribution analysis, we propose to use two-dimensional vector autoregressive (VAR) model with the α-stable distribution. The non-homogeneity of the data is reflected in two identified regimes: first – corresponding to the 2008 crisis and second – to the stable market situation. As a natural implication of the model fitted to market assets, we derive the dynamics of the copper price in PLN, which is not a traded asset but is crucial for the KGHM company risk exposure. A comparative study is performed to demonstrate the effect of including dependencies of the assets and the implications of the regime change. Since for various international companies, risk factors are given rather in the national than the market currency, the approach is universal and can be used in different market contexts, like mining or oil companies, but also other commodities involved in the global trading system.

Revisiting Covered Calls and Protective Puts: A Tale of Two Strategies

This paper examines the historical risk-adjusted returns of two hedging strategies designed to minimize downside market risk: Protective-puts and covered-calls, using US market data from 1993 to 2020. Here, we find that covered-call strategies significantly outperform the buy-and-hold strategy on a raw and risk-adjusted basis over the entire sample and these excess returns appear to remain persistent over time. We also find the opposite results hold for the protective put strategy: This strategy not only significantly underperforms the buy-and-hold strategy from a raw and risk-adjusted return standpoint, it actually significantly increases the probability of incurring losses each month. Finally, we evaluate the overall utility of various covered call strategies for loss averse investors, using the standard prospect theory utility function. Here, we find that out-of-the-money covered-call options yield the highest utilities for investors with less than average loss aversion, while in-the-money covered call options become more favorable as loss aversion increases.

Cryptocurrencies Meet Equities: Risk Factors and Asset Pricing Relationships

We consider a variety of cryptocurrency and equity risk factors as potential forces that drive cryptocurrency returns and carry risk premiums. In a cross-section of 2,000 biggest cryptocurrencies, only downside market risk, cryptocurrency size and policy uncertainty factors are systematically priced with significant premiums. Momentum premium vanished in the recent years. Equity market risk appears to be more important than cryptocurrency market risk, suggesting greater linkages between cryptocurrency and equity markets than we used to think.

Downside Risk in Cryptocurrency Market

I look at the cryptocurrency market through the prism of standard multi-factor asset-pricing models with particular attention to the downside market risk. The analysis for 1,700 coins reveals that there is a significant heterogeneity in the exposure to the downside market risk, and that a higher downside risk exposure is associated with higher average returns. The extra downside risk is priced with a statistically significant premium in cross-sectional regressions. Adding the downside risk component to the CAPM and the 3-factor model for cryptocurrencies improves the explanatory power of the models significantly. The downside risk is orthogonal to the size and momentum risks and constitutes an important forth component in the multi-factor cryptocurrency pricing model.

Is Downside Risk Priced In Cryptocurrency Market?

I look at the cryptocurrency market through the prism of standard multifactor asset-pricing models with particular attention to the downside market risk. The analysis for 1,700 coins reveals that there is a significant heterogeneity in the exposure to the downside market risk, and that a higher downside risk exposure is associated with higher average returns. The extra downside risk is priced with a statistically significant premium in cross-sectional regressions. Adding the downside risk component to the CAPM and the 3-factor model for cryptocurrencies improves the explanatory power of the models significantly. The downside risk is orthogonal to the size and momentum risks and constitutes an important forth component in the multifactor cryptocurrency pricing model.

Risk transmission mechanism between energy markets: A VAR for VaR approach

The Global Financial Crisis (GFC) of 2007–2009 that originated in the US has revealed the need for measuring and monitoring the transmission of extreme downside market risk. This paper investigates the risk transmission mechanism between the oil and natural gas markets. We apply the recently introduced test statistics based on cross-quantilogram function and the multivariate quantile regression model (VAR for VaR) to the US oil and natural gas prices, which are independently formed. Our results show two asymmetric patterns. First, the shocks in the oil market substantially increase the Value at Risk (VaR) in the natural gas market, but the reverse impact does not exist. Second, we highlight the significant asymmetric response of gains and losses transmission in energy markets, cautioning about the underlying weakness of adopting volatility to measure risk in the energy market. Moreover, an extreme market risk is more easily transmitted across markets than moderate risks. Our results are in general robust in application to other regional energy markets, such as Europe and Asia, but the heterogeneities in responses are underpinned by the different role of natural gas in regions. The findings in this paper have important implications for academic researchers, policy makers in gas-dependent economies, and business practitioners in light of projected increases in the use of natural gas worldwide as well as the development of independent gas-on-gas competitive (hub) prices in Asia and Europe.

Mutual Fund Flows and Downside Risk

There is little empirical evidence regarding downside risk in asset pricing, due in part to problems inherent in estimating downside risk. We argue that Berk and van Binsbergen (2016)’s approach to testing asset pricing models using the relation between investor flows and risk-adjusted fund returns is well suited for examining the merits of downside risk. We extend the analysis of Berk and van Binsbergen (2016) and Barber et al. (2016) by showing that investors care more about downside market risk than unconditional market risk when choosing mutual funds. Our study provides novel insights regarding investors’ preferences. We find that investors’ sensitivity to downside risk increases following market crises and is more pronounced among funds with conservative investment objectives. Finally, we find that investors’ response to downside risk is not subsumed by information/influence from Morningstar ratings.

International risk transmission of stock market movements

The Great Recession of 2007-2009 originated in the United States has brought to the surface the need for measuring and monitoring the transmission of extreme downside market risk. This paper investigates the international risk transmission mechanism between the US and major Asian stock markets. By applying two recently introduced test statistics based on cross-quantilogram function, we confirm the existence of risk spillover from the US to major Asian stock markets, as well as the feedback effect. Furthermore, we apply the multivariate quantile regression model (VAR for VaR) to quantitively uncover these tail-interdependency patterns, showing how the extreme downside risk transmits between the US and Asian markets. Our results suggest these stock markets are highly integrated in terms of risk transmission. The shocks in the US market substantially increase the Value at Risk (VaR) in the Asian markets, except China and Russia. On the other hand, price drops in the Asian markets also have a weaker but significant predictive power for risk in the US market. By investigating the time-varying pattern of the risk interdependency structure, we further show a rising trend of cross-country risk linkages over time. We also document considerable asymmetric patterns in the international transmission mechanism of stock market movements, highlighting the underlying weakness of adopting volatility to measure the market risk.

Asset Pricing with Downside Liquidity Risks

We develop a parsimonious liquidity-adjusted downside capital asset pricing model to investigate whether phenomena such as downward liquidity spirals and flights to liquidity impact expected asset returns. We find strong empirical support for the model. Downside liquidity risk (sensitivity of stock liquidity to negative market returns) has an economically meaningful return premium that is 10 times larger than its symmetric analogue. The expected liquidity level and downside market risk are also associated with meaningful return premiums. Downside liquidity risk and its associated premium are higher during periods of low marketwide liquidity and for stocks that are relatively small, illiquid, volatile, and have high book-to-market ratios. These results are consistent with investors requiring compensation for holding assets susceptible to adverse liquidity phenomena. Our findings suggest that mitigation of downside liquidity risk can lower firms’ cost of capital. This paper was accepted by Lauren Cohen, finance.

An evolving possibilistic fuzzy modeling approach for Value-at-Risk estimation

Market risk exposure plays a key role in risk management. A way to measure risk exposure is to evaluate the losses likely to incur when the assets prices of a portfolio decline. Most financial institutions rely on Value-at-Risk (VaR) estimates to measure downside market risk. This paper suggests an evolving possibilistic fuzzy modeling (ePFM) approach to estimate VaR. The approach is an extension of the possibilistic fuzzy c-means clustering and functional fuzzy rule-based modeling within the framework of incremental learning. Evolving possibilistic modeling employs memberships and typicalities to update the cluster structure and corresponding fuzzy rules using a statistical control distance-based criterion. A utility measure evaluates the quality of the current cluster structure and associated model. Data from the main global equity market indexes of United States, United Kingdom, Germany, Spain, and Brazil from January 2000 to December 2012 are used to estimate VaR using ePFM. The performance of ePFM is evaluated and compared with traditional VaR benchmarks such as Historical Simulation, GARCH, EWMA, and Extreme Value Theory based VaR, as well as with state of the art evolving approaches. The results suggest that ePFM is a potential candidate for VaR modeling because it achieves better results than the alternative approaches.

Upside and Downside Risks in Momentum Returns

I provide a novel risk-based explanation for the profitability of momentum strategies. I show that the past winners and the past losers are differently exposed to the upside and downside market risks. Winners systematically have higher relative downside market betas and lower relative upside market betas than losers. As a result, the winner-minus-loser momentum portfolios are exposed to extra downside market risk, but hedge against the upside market risk. Such asymmetry in the upside and downside risks is a mechanical consequence of rebalancing momentum portfolios. But it is unattractive for an investor because both positive relative downside betas and negative relative upside betas carry positive risk premiums according to the Downside-Risk CAPM. Hence, the high returns to momentum strategies are a mere compensation for their upside and downside risks. The Downside Risk-CAPM is a robust unifying explanation of returns to momentum portfolios, constructed for different geographical and asset markets, and it outperforms alternative multi-factor models.

Is risk higher during non-trading periods? The risk trade-off for intraday versus overnight market returns

We study the magnitude of tail risk – particularly lower tail downside risk – that is present in intraday versus overnight market returns and thereby examine the nature of the respective market risk borne by market participants. Using the Generalized Pareto Distribution for the return innovations, we use a GARCH model for the conditional market return components of major stock markets covering the U.S., France, Germany and Japan. Testing for fat-tails and tail index equality, we find that overnight return innovations exhibit significant tail risk, while intraday innovations do not. We illustrate this volatility versus tail risk trade-off based on conditional Value-at-Risk calculations. Our results show that overnight downside market risk is composed of a moderate volatility risk component and a significant tail risk component. We conclude that market participants face different intraday versus overnight risk profiles and that a risk assessment based on volatility only will severely underestimate overnight downside risk.

Is Risk Higher During Non-Trading Periods? Tail Risk Evidence from Overnight Market Returns

We study the magnitude of tail risk --- particularly lower tail downside risk --- that is present in intraday versus overnight market returns and thereby examine the nature of the respective market risk borne by market participants. Using the Generalized Pareto Distribution for the return innovations, we use a GARCH model for the conditional market return components of major stock markets covering the U.S., France, Germany and Japan. Testing for fat-tails and tail index equality, we find that overnight return innovations exhibit significant tail risk, while intraday innovations do not. We illustrate this volatility versus tail risk trade-off based on conditional Value-at-Risk calculations. Our results show that overnight downside market risk is composed of a moderate volatility risk component and a significant tail risk component. We conclude that market participants face different intraday versus overnight risk profiles and that a risk assessment based on volatility only will severely underestimate overnight downside risk.

Currency Exposure to Downside Risk: Which Fundamentals Matter?

AbstractI study whether or not countries' macroeconomic characteristics are systematically related to their currencies' exposure to the downside market risk. I find that the currency downside risk is strongly associated with the local inflation rate, real interest rate and net foreign asset position. Currencies of countries with high inflation and real interest rates and negative net foreign asset position (debtor countries) are more exposed to the downside risk whereas currencies of countries with low inflation and real interest rates and positive net foreign asset position (creditor countries) exhibit “safe haven” properties. The local real interest rate has the highest explanatory power in accounting for the cross‐section of currency exposure to the downside risk. This suggests that the high currency exposure to the downside risk is a consequence of investments in high‐yield risky countries and flight from them in “hard times”.