Asset Volatility Research Articles

Bonds versus Equities: Information for Investment

ABSTRACTWe provide a simple model of investment by a firm funded with debt and equity and empirical evidence to demonstrate that, once we control for the debt overhang problem with credit spreads, asset volatility is an unambiguously positive signal for investment, while equity volatility sends a mixed signal: Elevated volatility raises the option value of equity and increases investment for financially sound firms, but exacerbates debt overhang and decreases investment for firms close to default. Our study provides a simple unified understanding of the structural and empirical relationships between investment, credit spreads, equity versus asset volatility, leverage, and Tobin's .

Application and Empirical Analysis of Random Volatility Model in Financial Markets

The Stochastic Volatility Model (SVM), as a key innovation in modern financial engineering, has profoundly changed our understanding of the volatility characteristics of financial markets. This model not only considers the volatility of asset prices as a time-varying random variable, but also simulates this uncertainty by introducing stochastic processes such as Brownian motion or more complex stochastic differential equations, thereby achieving a precise characterization of the dynamic characteristics of volatility. Compared to traditional fixed or historical average volatility assumptions, SVM can capture sudden changes in market sentiment, the impact of news events, and the influence of macroeconomic factors on volatility, providing investors with a more realistic perspective on the market. This article not only deeply analyzes the theoretical basis and construction methods of SVM, but also verifies its effectiveness and applicability in different financial market environments through empirical analysis and actual market data, providing valuable references and insights for financial practitioners, scholars, and policy makers.

Comparison of Triple Exponential Smoothing and ARIMA in Predicting Cryptocurrency Prices

Cryptocurrency has emerged as a prominent digital asset over the past decade, but its high price volatility presents significant challenges for investors. This study evaluates and compares the effectiveness of the Triple Exponential Smoothing (TES) and Autoregressive Integrated Moving Average (ARIMA) methods in forecasting the prices of five major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Solana (SOL), and Ripple (XRP). TES models trends and seasonality in time series data, while ARIMA captures autoregressive patterns and moving averages. The dataset is split into 80% for training and 20% for testing, with performance evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). TES outperforms ARIMA in predicting Bitcoin and Binance Coin, achieving MAPE values of 10.38% and 13.81%, and RMSE values of 3,985.55 and 41.28, respectively. However, ARIMA shows better performance for Ethereum, Solana, and Ripple, with MAPE ranging from 8.78% to 32.84% and RMSE between 0.08 and 204.59. Notably, Ethereum has the lowest MAPE at 8.78%, while Ripple exhibits the smallest RMSE at 0.08. These findings suggest that TES is more suitable for cryptocurrencies with relatively stable price patterns, while ARIMA is better adapted to forecasting highly volatile assets. This research underscores the importance of selecting forecasting models based on the specific characteristics of each cryptocurrency

Existence of maximal solutions for the financial stochastic Stefan problem of a volatile asset with spread

Existence of maximal solutions for the financial stochastic Stefan problem of a volatile asset with spread

Deep learning enhanced volatility modeling with covariates

Exogenous information such as policy news and economic indicators can have the potential to trigger significant movements in financial asset volatility. This article presents a model, called the RECH-X model, that allows incorporating exogenous variables into a recurrent neural network for volatility modeling and forecasting. The RECH-X model can allow for abrupt changes in the volatility level and effectively capture the complex serial dependence structure in the volatility dynamics. We demonstrate in a wide range of applications that the RECH-X model consistently outperforms the benchmark models in terms of volatility modeling and forecasting.

Volatility spillover effect between cryptocurrency and stock market using MGARCH Bekk model

This paper explores the volatility spillover effects between the cryptocurrency market and the Pakistan Stock Exchange (PSX). Utilising data from January 1, 2019, to April 5, 2024, sourced from Investing and Yahoo Finance, the study employs the Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) BEKK model to assess the dynamic interactions between these markets. Stationarity tests confirmed the non-stationarity of time series data at their levels, which became stationary after first differencing, ensuring robust econometric analysis. The results indicate significant volatility spillovers from major cryptocurrencies, such as Bitcoin and Ethereum, to the PSX, highlighting a solid interconnectedness between these markets. This suggests that digital asset volatility significantly influences traditional financial systems. The study concludes that integrating cryptocurrencies into global financial markets introduces risks and opportunities for investors and policymakers. The findings underscore the need for market participants to account for these volatility interactions in their risk management strategies. Additionally, policymakers must consider these interlinkages to maintain financial stability. This research contributes to the literature on financial market volatility by emphasising the importance of understanding the impact of emerging digital currencies on traditional stock markets.

Cost-efficient payoffs under model ambiguity

Dybvig (1988a, 1988b) solves in a complete market setting the problem of finding a payoff that is cheapest possible in reaching a given target distribution (“cost-efficient payoff”). In the presence of ambiguity, the distribution of a payoff is, however, no longer known with certainty. We study the problem of finding the cheapest possible payoff whose worst-case distribution stochastically dominates a given target distribution (“robust cost-efficient payoff”) and determine solutions under certain conditions. We study the link between “robust cost-efficiency” and the maxmin expected utility setting of Gilboa and Schmeidler (1989), as well as more generally in a possibly nonexpected robust utility setting. Specifically, we show that solutions to maxmin robust expected utility are necessarily robust cost-efficient. We illustrate our study with examples involving uncertainty both on the drift and on the volatility of the risky asset.

Analytically pricing European options in dynamic markets: Incorporating liquidity variations and economic cycles

This paper discusses the European option pricing problem in the context of asset prices being influenced by liquidity risks and economic cycles. We employ regime switching for asset volatility, and liquidity risks are captured by market-wide liquidity. We obtain an analytical formulation of European option prices, allowing for fast model calibration using real-market data. By estimating model parameters using real option data, we show that pricing errors can be significantly reduced using our model that considers stochastic liquidity, indicating that our model has real-world applications. Our results can help investors and regulators better understand the market and provide a potentially effective risk management tool.

The Impact of Financial Stress and Uncertainty on Green and Conventional Bonds and Stocks: A Nonlinear and Nonparametric Quantile Analysis

This study aims to investigate the impact of financial stress and uncertainty on the returns of green and conventional bonds and stocks in the United States from 2010 to 2022. The research utilizes nonlinear and nonparametric analysis, which includes the quantile-on-quantile and nonparametric causality-in-quantiles approaches to examine the relationship between variables. The data analyzed using R programming language show that financial stress positively impacts the middle quantiles of both conventional and green equity, while financial uncertainty negatively impacts upper quantiles. The study also finds that financial stress has a more significant impact on all types of bonds compared to financial uncertainty, with conventional bonds being more affected. This study proposes a pyramid that classifies financial assets based on their susceptibility to financial stress, which could help investors evaluate risk levels and make better investment decisions. The study recommends that policymakers should encourage green investments by offering incentives, such as tax credits. They should also focus on enhancing the efficiency of volatile assets by implementing new investment rules and regulations.

Volatility spillover and dynamic correlation between Islamic, conventional, cryptocurrency and precious metal markets during the immediate outbreak of COVID-19 pandemic

PurposeThis paper aims to investigate the immediate effect of the outbreak of the COVID-19 pandemic by investigating volatility transmission and dynamic correlation between stock (conventional and Islamic) markets, bitcoin and major commodities such as gold, oil and silver at different investment horizons before and after 161 trading days of the outbreak of the COVID-19 pandemic.Design/methodology/approachThe MGARCH-DCC and maximum overlap discrete wavelet transform -based cross-correlation were used in the estimation of the volatility spillover and continuous wavelet transform in the estimation of the time-varying volatility and correlation between the assets at different investment horizons.FindingsThe authors observed a sudden correlation breakdown following the COVID-19 shock. Oil (Bitcoin) was a major volatility transmitter before (during) COVID-19. Digital gold (Bitcoin), gold and silver became highly correlated during COVID-19. The highest co-movement between the assets was observed at medium and long-term investment horizons.Practical implicationsThe study findings have a financial implication for day traders, investors and policymakers in the understanding of volatility transmission and intercorrelation in a bid to actively manage stylized and well-diversified asset portfolios.Originality/valueThis study is unique for its employment in estimating the time-varying conditional volatility of the investable assets and cross-correlations between them at different investment horizons, particularly before and after COVID-19 outbreak.

The Fourier–Malliavin Volatility (FMVol) MATLAB® library

This paper presents the Fourier–Malliavin Volatility (FMVol) estimation library for MATLAB®. This library includes functions that implement Fourier–Malliavin estimators (see Malliavin and Mancino (2002, 2009)) of the volatility and co-volatility of continuous stochastic volatility processes and second-order quantities, like the quarticity (the squared volatility), the volatility of volatility and the leverage (the covariance between changes in the process and changes in its volatility). The Fourier–Malliavin method is fully non-parametric, does not require equally-spaced observations and is robust to measurement errors, or noise, without any preliminary bias correction or pre-treatment of the observations. Furthermore, in its multivariate version, it is intrinsically robust to irregular and asynchronous sampling. Although originally introduced for a specific application in financial econometrics, namely the estimation of asset volatilities, the Fourier–Malliavin method is a general method that can be applied whenever one is interested in reconstructing the latent volatility and second-order quantities of a continuous stochastic volatility process from discrete observations.

Modelling the Volatility of NFTs and Traditional Financial Assets using MGARCH Family Models

Modelling the Volatility of NFTs and Traditional Financial Assets using MGARCH Family Models

The impact of cryptocurrency-related cyberattacks on return, volatility, and trading volume of cryptocurrencies and traditional financial assets

We investigate the impact of cryptocurrency-related cyberattacks on the cryptocurrency market and traditional financial markets. The dataset consists of historical cyberattack data and trading data for twenty cryptocurrencies, three cryptocurrency uncertainty indices, five payment companies, four stock indices, a commodity index, and gold. We find that cyberattacks are associated with negative returns, increased volatility, and increased trading volume not only for the cryptocurrencies but also for the payment companies, the financial and technology sectors, and the general stock market. However, the impact of cyberattacks on cryptocurrencies has been decreasing over time, while the impact on payment companies and the financial sector has been increasing. Moreover, gold prices have shown a positive response to these cyberattacks. These results underscore the need for enhanced cybersecurity measures in the fintech sector and may inform both policymakers and market participants.

Assessing network risk with FRM: links with pricing kernel volatility and application to cryptocurrencies

The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. FRM @ Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market.

Hedging the climate change risks of China's brown assets: Green assets or precious metals?

A large discrepancy exists between the dire impact of climate change projected by most natural scientists and the modest estimates of damages calculated by mainstream economists. In this article, we first measure the climate risk, including the transition and physical risks, in China using textual analysis. Then, considering the heterogeneity of climate change's effect, we investigate the impact of climate risks on the brown and green assets of China (including price, volatility, and correlation) for the period from February 2012 to April 2022. In addition, the performance of China's green assets and international precious metal assets is examined in hedging the climate risk exposure of China's brown assets. The results reveal that transition and physical risks lead to contrary changes in the prices of brown and hedging assets. Transition risk causes the volatility of brown and green assets to change in the opposite direction, while physical risk causes the volatility of the two types of assets to change in the same direction. Under extreme climate risk conditions, the correlation between brown and hedging assets decreases. Finally, the findings show that the performance of international precious metals in hedging climate risk exposure is better than that of China's green equity assets.

Working capital balances and financial policy

A firm's working-capital account balances demonstrate substantial connections with its financial policy. Receivables and inventories are associated with lower asset volatility and higher future cash flow, and correlate with increased leverage. Conversely, payables show a positive association with shareholder return volatility and a negative correlation with future cash flow and are linked to decreased debt levels. In dynamic panel regressions estimated with system Generalized Method of Moments (GMM), a one standard-deviation increase in payables (receivables) is associated with a 0.42 (0.66) standard-deviation decrease (increase) in leverage. Furthermore, higher levels of inventories and receivables are linked with higher firm credit ratings, along with a preference for debt financing over equity issuance, while increased payables are linked with lower credit ratings and a tendency to favor equity financing over debt financing. The QuickPay regulatory shock, expediting payments and reducing receivables' volume and quality, diminished their collateral role and lowered debt sensitivity to receivables.

Evaluasi Model Jaringan Saraf Tiruan Berbasis LSTM dalam Memprediksi Fluktuasi Harga Bitcoin

Amid the highly volatile fluctuations in the cryptocurrency market, the ability to accurately predict Bitcoin prices becomes crucial for investors and financial analysts. This study aims to develop a predictive model using Long Short-Term Memory (LSTM) Neural Networks, a specific form of recurrent neural network, to predict Bitcoin prices. Historical data on daily closing prices of Bitcoin from 2015 to 2023 was used to train and test the model. Following data preprocessing, which included normalization and the creation of a time series dataset, the LSTM model was constructed with two LSTM layers and two dense layers to enhance the predictive analysis. The model was trained with the data split into 80% for training and 20% for testing. Results show that the LSTM model was able to produce fairly accurate predictions with a low loss value on the test data. Further evaluation through comparison with baseline models showed significant improvements in predictive accuracy. This research demonstrates the potential application of advanced machine learning techniques in financial analysis, particularly in predicting the prices of highly volatile assets like Bitcoin. With continuous improvements to the model architecture and parameter optimization, Bitcoin price predictions could become more reliable, helping stakeholders make more informed investment decisions.

PROSPECTS FOR REGULATION OF ACCOUNTING AND TAXATION OF VIRTUAL ASSETS IN UKRAINE

Introduction. The advent of virtual assets has brought both challenges and opportunities, reshaping global financial systems and necessitating updated regulatory, accounting, and taxation frameworks. Ukraine stands at a critical juncture, integrating these digital innovations into its legal and financial infrastructure while aligning with global standards. Purpose. This article aims to provide a thorough examination of Ukraine’s developing frameworks for the regulation, accounting, and taxation of virtual assets. It assesses the effectiveness of current strategies and identifies areas for enhancement to ensure a robust integration of virtual assets into Ukraine's financial system. Methods. The research employs a comparative analysis method, juxtaposing Ukraine's frameworks with global standards. This approach is supported by a review of recent academic literature and regulatory reports, offering a comprehensive perspective on the adaptation processes and methodologies applied in the integration of virtual assets. Results. The findings reveal that while Ukraine has made significant progress, particularly with the adoption of the "Law on Virtual Assets," gaps remain in fully securing a stable and transparent virtual asset market. The study highlights the need for continual regulatory updates and the adoption of international accounting standards to manage the complexity and volatility of virtual assets. Conclusion. Ukraine’s proactive regulatory adaptations position it to potentially lead in the integration of virtual assets globally. Continued alignment with international standards and responsive updates to regulatory frameworks are essential to harness the economic benefits and mitigate associated risks. This ongoing evolution will support Ukraine's strategic positioning in the global digital economy and foster a stable environment for innovation and investment in virtual assets.

Stability of the Epstein–Zin problem

AbstractWe investigate the stability of the Epstein–Zin problem with respect to small distortions in the dynamics of the traded securities. We work in incomplete market model settings, where our parametrization of perturbations allows for joint distortions in returns and volatility of the risky assets and the interest rate. Considering empirically the most relevant specifications of risk aversion and elasticity of intertemporal substitution, we provide a condition that guarantees the convexity of the domain of the underlying problem and results in the existence and uniqueness of a solution to it. Then, we prove the convergence of the optimal consumption streams, the associated wealth processes, the indirect utility processes, and the value functions in the limit when the model perturbations vanish.

The Early Exercise Risk Premium

We study the asset pricing implications of being able to optimally early exercise plain vanilla puts, contrasting expected raw and delta-hedged returns across equivalent American and European puts. Our theory suggests that American puts yield less negative raw but more negative delta-hedged expected returns than equivalent European puts. The raw (delta-hedged) spread widens with a higher early exercise probability as induced through, for example, moneyness, time to maturity, and underlying asset volatility (variance and jump risk premiums). An empirical comparison of single-stock American puts with equivalent synthetic European puts formed from put–call parity supports our theory if and only if we allow for optimal early exercises in our return calculations. More strikingly, allowing for optimal early exercises significantly alters the profitability of 14 out of 15 well-known option anomalies with the average absolute change equal to 33% and five anomalies becoming insignificant. This paper was accepted by Lukas Schmid, finance. Supplemental Material: The internet appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00440 .