Volatility Risk Research Articles

Quantitative Easing and Direct Lending in Response to the COVID‐19 Crisis

AbstractThis paper develops a dynamic general equilibrium model to study quantitative easing (QE) and direct lending to firms. QE works through three channels: expanding bank reserves raises liquidity and lowers the liquidity premium, purchasing assets withdraws risk and lowers the volatility risk premium, and the resulting economic stimulus lowers the credit risk premium. When bank reserves are higher, the liquidity premium channel is weaker, and QE is less expansionary. Direct lending is more expansionary than QE because it substitutes bank lending and mitigates the credit risk frictions associated with bank lending, while QE stimulates bank lending and worsens the frictions.

Bayesian estimation of stochastic volatility jump diffusion model parameters using S&P 500 and VIX data

The Stochastic Volatility Jump Diffusion (SVJD) model is a common tool used for option pricing. Existing literature has employed many techniques in order to estimate the parameters of this model, including both Bayesian and frequentist approaches. In 2010, Duan and Yeh [Jump and volatility risk premiums implied by VIX. J Econ Dyn Control. 2010;34(11):2232–2244] used a frequentist approach with maximum likelihood estimation that included the VIX, the Chicago Board Options Exchange (CBOE) Volatility Index, which carries real time S&P 500 option price information in addition to the S&P 500 data. In this paper, we combine the previous Bayesian approach in Cape et al. [Estimating Heston's and Bates' models parameters using Markov Chain Monte Carlo simulation. J Stat Comput Simul. 2015;85(11):2295–2314] with the inclusion of VIX by Duan and Yeh. We provide the derivations of the conditional posterior distributions of the SVJD model's parameters and a guide to a Markov Chain Monte Carlo (MCMC) algorithm used to estimate the parameters. We apply the algorithm to the historical S&P 500 and VIX data and provide the subsequent parameter estimates. We offer these tools for others performing similar research with our R code available upon request.

Optimal Market Completion through Financial Derivatives with Applications to Volatility Risk

This paper investigates the optimal choices of financial derivatives to complete a financial market in the framework of stochastic volatility (SV) models. We first introduce an efficient and accurate simulation-based method applicable to generalized diffusion models to approximate the optimal derivatives-based portfolio strategy. We build upon a double optimization approach, i.e., expected utility maximization and risk exposure minimization, already proposed in the literature, demonstrating that strangle options are the best choices for market completion within equity options. They lead to lower investors’ risk exposure for a wide range of strikes compared to the lesser flexibility of calls, puts, and strangles. Furthermore, we explore the benefit of using volatility index derivatives and conclude that they could be more convenient substitutes when short-term maturity equity options are not available.

Spillover and dependence between Chinese carbon and new energy stock markets: A cross-quantile perspective

The interplay between China's carbon emissions trading market and the new energy stock market has attracted increasing scholarly attention, given their alignment with green and sustainable development goals. This study utilizes the Cross-Quantilogram methodology to assess predictability and spillover dynamics in the Chinese carbon markets and the new energy stock markets across various quantiles. The analysis yields some significant findings: (1) Despite the small magnitude, bidirectional spillover effects are discernible between China's carbon market and the Chinese new energy market, with heterogeneous correlation among the three pilot carbon markets. (2) The cross-quantile correlation among markets exhibits varying dynamics with increasing lag orders. (3) During market downturns, the new energy market poses short-term abnormal extreme risks as a spillover source for the Guangdong carbon market, while exhibiting a stronger spillover effect on the Hubei carbon market during longer lag periods. (4) Certain sectors, such as the wind power, nuclear power, and photovoltaic sectors, stand out with a more pronounced cross-quantile in relation to the carbon market. (5) Both risk events and policy changes play a significant role in shaping market interactions. (6) Including Guangdong and Hubei carbon asset in a portfolio can effectively reduce the volatility risk of new energy stock portfolios. A valuable comprehension of the intricate interdependencies between China's carbon market and new energy assets is provided by this research, benefiting policymakers and investors in the energy and environmental sectors.

Dependence of green energy markets on big data and other fourth industrial revolution technologies

This paper analyzes the dependence and connectedness among fourth-industrial revolution technology markets (including big data and artificial intelligence, blockchain, and financial technology) and global and regional (US, Europe, and Asia) green energy markets. In particular, we consider the dynamic dependence among these markets in terms of both returns and volatility across different market conditions and investment horizons using the cross-spectral coherence and Quantile-VAR connectedness approach. Three main results emerge from our analysis. First, the return dependence is relatively stronger than volatility dependence and is stronger across most time scales among the technology markets and the European and Asian regional green energy indexes. Second, the return and volatility connectedness is stronger during extreme than normal market conditions. Unless under bullish market times, volatility connectedness appears smaller than return connectedness, implying that market volatility risks spread less forcefully among these markets than return risks under normal and bearish market periods. Third, geopolitical risks, business environment, economic policy, fixed-income, and oil and gold markets’ uncertainties are significant predictors of the degree of return and volatility connectedness. Overall, our findings offer crucial insights for short- and long-term investors interested in portfolios with modern technology and green assets. They also emphasize the roles of market and macroeconomic factors in shock propagation and their implications for low-carbon transition.

Pricing and Hedging Contingent Claims by Entropy Segmentation and Fenchel Duality

We present a new approach to the problem of characterizing and choosing equivalent martingale pricing measures for a contingent claim, in a finite-state incomplete market. This is the entropy segmentation method achieved by means of convex programming, thanks to which we divide the claim no-arbitrage prices interval into two halves, the buyer’s and the seller’s prices at successive entropy levels. Classical buyer’s and seller’s prices arise when the entropy level approaches 0. Next, we apply Fenchel duality to these primal programs to characterize the hedging positions, unifying in the same expression the cases of super (resp. sub) replication (arising when the entropy approaches 0) and partial replication (when entropy tends to its maximal value). We finally apply linear programming to our hedging problem to find in a price slice of the dual feasible set an optimal partial replicating portfolio with minimal CVaR. We apply our methodology to a cliquet style guarantee, using Heston’s dynamic with parameters calibrated on EUROSTOXX50 index quoted prices of European calls. This way prices and hedging positions take into account the volatility risk.

Can corporate social performance mitigate the risk of extreme stock returns?

It is commonly believed that there exists a strong negative association between corporate social performance (CSP) and firm risk.11The authors would like to thank an anonymous reviewer for his careful reading of our manuscript and his many insightful comments and suggestions which have substantially improved the initial paper. The authors would alsolike to thank Lionel Tangy Malca and Alexandre Coureaud from HOMA Capital for fruitful and constructive discussions around the link between ESG and extremes stocks returns. To investigate the structure of this relationship, we decompose the dynamics of large U.S. company stock returns into two components: Gaussian and non-Gaussian innovations. Our findings indicate that CSP affects firm risk mainly through the non-Gaussian risk channel. In particular, it significantly reduces the magnitude of extreme returns. We find no consistent nor robust effect of CSP on the frequency of price boom and crash probability as it varies widely across industries. Last, we find no statistically significant impact of CSP on standard Gaussian volatility risk.

The Role of Activating Joint Audit Programs in Reducing The Exchange Rate Volatility Risks "A Field study"

The Role of Activating Joint Audit Programs in Reducing The Exchange Rate Volatility Risks "A Field study"

Do investors care about inflation risk? Evidence from global bond portfolio allocation

We investigate whether mutual funds adjust their portfolio allocations in global bond markets in response to country-level measures of inflation risk, specifically inflation volatility risk and inflation cyclicality. Our findings indicate that portfolio managers decrease allocations in countries with higher inflation volatility risk, relative to their overall portfolio exposure to inflation volatility risk. In contrast, inflation cyclicality does not exert a significant influence on global portfolio allocation decisions. We strengthen our results by analyzing funds according to their geographic investment mandates, fund characteristics, and portfolio composition.

The use of artificial intelligence in the management of investment portfolios

The ever-increasing complexity of financial markets, characterized by high market volatility, economic uncertainty, and the massive influx of various data pose a significant challenge in investment portfolio management. Traditional methods can fall short in addressing these challenges, requiring too much from an individual managing investment portfolios. The ability of AI to process a tremendous number of large datasets and to detect patterns offers a significant advantage for a manager, providing for more informed and timely investment decisions. The purpose of this annex is to encapsulate findings of the primary paper on the use of AI in investment portfolio management, highlighting the benefits and challenges, and also identifying areas for improvement and further research. The paper explores the possibility of using AI to enhance the effectiveness of an investment portfolio manager, through the improvement of analysis’ quality, help in data collection and processing, recognition of complex patterns, and faster decision-making. This could help optimize investment strategies through risk reduction, improved forecasting, and faster adaptation to market fluctuations. In addition, the paper aims to identify potential risks associated with AI integration, for example quality of data, algorithm transparency, and cybersecurity issues. Literature analysis demonstrated that application of AI can increase the efficiency of investment portfolio management. This is possible because of AI systems' ability to conduct advanced data search and analysis, providing for better decision-making. The use of AI algorithms allows investment portfolio managers to identify market trends more easily, optimize asset allocations, as well as minimize market volatility risks. А good example is BlackRock's Aladdin expert system, which provides comprehensive data insights, providing a competitive advantage. However, the paper also demonstrates, that implementation of AI systems is associated with several challenges, particularly - quality of provided data and opacity of AI algorithms, which can raise doubts regarding their effectiveness. Moreover, cybersecurity remains a significant concern. Overall, the use of AI presents opportunities for investment portfolio managers to improve results by providing competitive advantages. However, certain challenges need to be overcome. AI holds great promise for investment portfolio management, and future studies need to focus on AI integration, cybersecurity, and improvement of data quality.

Persistent and transient variance components in option pricing models with variance-dependent Kernel

This paper examines theoretically and empirically a variance-dependent pricing kernel in the continuous-time two-factor stochastic volatility (SV) model. We investigate the relevance of such a kernel in the joint modeling of index returns and option prices. We contrast the pricing performance of this model in capturing the term structure effects and smile/smirk patterns to discrete-time GARCH models with similar variance-dependent kernels. We find negative and significant risk premium for both volatility factors, implying that investors are willing to pay for insurance against increases in volatility risk, even if it has little persistence. In-sample, the component GARCH model exhibits a slightly better fit overall and across all maturity buckets than the two-factor SV model. However, the two-factor SV model reduces strike price bias, giving rise to the model’s ability in reconciling the physical and risk-neutral distribution. Out-of-sample, the two-factor SV model has better fit to data.

Real Estate Economy in the Post-epidemic Era

The COVID-19 outbreak has had a significant impact on China's real estate market, with the sector's recovery lagging behind the overall economy. This study evaluates the pandemic's influence on consumer behavior, housing demand, and preferences, highlighting substantial declines in real estate development investment and commercial housing sales. Key factors contributing to reduced demand include a shift towards remote working models, heightened health and safety concerns, and evolving perceptions of urban living. To stimulate the market, government incentives such as tax breaks and housing subsidies are recommended. Additionally, developers are encouraged to embrace smart city technologies for optimizing energy consumption and providing convenient community services. Emphasizing health-conscious design is crucial for improving air quality and increasing green space while investors should diversify their portfolios to mitigate market volatility risks and enhance resilience. The objective of this study is to devise an appropriate real estate economic recovery plan that aligns with the changing urban to sustain economic growth.

Financial Risk Identification and Control in the Openness Context: A System Dynamics Approach to Renminbi Internationalization

In an attempt to control a series of domestic and foreign financial risks amid the internationalization of Renminbi (RMB), China’s currency, this article categorizes these risks into five types: excessive volatility risk of exchange rate, balance of payments risks, policy risks, crisis contagion risks, and excessive volatility risk of financial asset prices. By employing system dynamics simulation models and sensitivity analysis, this paper examines strategies for financial risk control. Results indicate that the impacts of crisis contagion risk and balance of payments risk are the most significant, and that financial risks are extremely sensitive in such aspects as the degree of capital openness, exchange rate volatility, and foreign trade dependency. The paper suggests adopting strategies like gradually opening China’s capital accounts, maintaining its exchange rate fluctuations within a reasonable range, stabilizing its dependency on foreign trade, and ensuring that its growth rate of M2 matches that of the nominal GDP – a combined effort to control the complicated financial risks arising from various sources.

Very Noisy Option Prices and Inference Regarding the Volatility Risk Premium

ABSTRACTThe stylized fact that volatility is not priced in individual equity options does not withstand scrutiny. First, we show that the average return of heavily traded deep out‐of‐the‐money call options on stocks is −116 basis points per day. Second, Fama‐MacBeth estimates of the volatility risk premium in stock options are similar to those in S&P 500 Index call options. Third, the mean return of heavily traded delta‐hedged at‐the‐money calls (puts) is −23 (−30) basis points. Fourth, the variance risk premium in stock options is negative. Our analysis highlights the importance of microstructure biases and robustness in empirical work with options.

Exchange Rate Pass-Through to Domestic Prices: Is Inflation Targeting an Optimal Policy Choice for Pakistan?

This study investigates the effect of exchange rate dynamics and its impact on consumer prices in Pakistan. Using monthly data for the period 2004- 2022, the Recursive Vector Autoregressive (VAR) technique has been employed for the empirical analysis. The recursive VAR estimation technique yields impulse response functions that demonstrate the incompleteness of exchange rate pass-through (ERPT). The results of the VAR reflect that the consumer prices bring most of the error variance in itself for the period under consideration. The exchange rate has been established to be the second most significant source of variations in consumer prices. The empirical results suggest that the lower ERPT reduces the risk of exchange rate volatility. Hence the monetary authorities may take steps to adopt inflation targeting in Pakistan.

Firm Networks and Asset Returns

Abstract Changes in the propagation of shocks along firm networks are important to understanding aggregate and cross-sectional features of stock returns. When calibrated to match key characteristics of supplier–customer networks in the United States, a model in which firms are interlinked via enduring relationships generates long-run consumption risks, high and volatile risk premiums, and a small and stable risk-free rate. The model also matches cross-sectional patterns of portfolio returns sorted by firm centrality, a feature unaccounted for by standard asset pricing models. (JEL C67, E30, G12, L14)

International crash risk premium

This paper investigates the international crash risk and the cross-section of stock index returns. We use the ex-ante model-free negative skewness measured by country-specific index options, proposed in Bakshi et al. (2003), as a proxy of the crash risk. We find that a country’s stock index with a high crash risk relates to a higher stock return as a risk premium across countries. The international crash risk premium exists robustly after controlling for volatility risk, macroeconomic variables, sensitivities to the international risk factors, and realized return moments. In contrast, other international risk premiums do not exist based on the exposure of such control variables. Based on the crash risk premium, we construct international stock trading strategies by sorting option-implied skewness across countries that outperform benchmark strategies by sorting the above control variables.

Volatility risk premium, good volatility and bad volatility: Evidence from SSE 50 ETF options

This paper studies the impact of volatility risk premium of SSE 50 ETF options on the price volatility of the underlying securities. After dividing options into different attributes including different types, market conditions and moneyness, we find that options contain implicit volatility information for the underlying securities and volatility risk premium has a significantly positive impact on the realized volatility. We further discuss the effect of options with different attributes on good and bad volatilities. The empirical results show that volatility risk premium has significant influence on both of them. In particular, the impact of volatility risk premium on good volatility is significantly stronger than that of bad volatility. In addition, we investigate the out-of-sample forecast performance of the volatility risk premium on realized volatility. The results show that the implied information content of deep out-of-the-money options has the highest prediction accuracy for weekly good realized volatility.

IPO comment letter responses and stock price volatility in the STAR market

ABSTRACT The public disclosure of IPO comment letter responses represents a significant institutional innovation within the registration-based IPO system. This study investigates the impact of IPO comment letter responses on post-IPO stock price volatility. Our findings indicate that both the quantity and quality of information in issuer responses are inversely correlated with stock price volatility. Additionally, this impact is pronounced when issuers invest more in R&D or face higher operational risk. Mechanism tests suggest that response information quantity (quality) is positively associated with changes in the information quantity (quality) of final-version prospectuses, while negatively correlated with informed trading and bid-ask spread. Lastly, we observe a diminishing effect of responses on post-IPO stock price volatility as time passes. These findings have implications for issuers to mitigate stock volatility risk and for regulators to advance reforms in the registration-based IPO system.

Charting the financial odyssey: a literature review on history and evolution of investment strategies in the stock market (1900–2022)

PurposeThe present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.Design/methodology/approachThe studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.FindingsThe study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.Research limitations/implicationsThere were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.Practical implicationsPractitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.Originality/valueThis paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.