Volatility Factors Research Articles

The Impact of Discretionary Measurement Criteria on Investors’ Judgement and Decisions

This study investigates the effect on nonprofessional investors’ judgements and decisions of discretionary measurement choices. Using a paper-and-pencil experience, we collect and analyze information regarding investment amounts as well as past and future financial performance judgements of firms’ earnings by manipulating fair value (mark-to-market and mark-to-model) criteria and benchmarking it with historical cost-based financial statements. We proxy nonprofessional investors with graduate students from a business school. Our results show evidence that nonprofessional investors view fair value changes as permanent. We argue for a cashflow volatility factor. Contrary to previous research, we do not find evidence of any effect on investors’ willingness to invest (average budget amounts invested) or performance judgments (past and future). We corroborate previous evidence that investors rank measurement concepts’ relevance differently for different classes, although, on average, mark-to-market fair values and historical cost are rated more relevant and reliable than mark-to-model fair values.

Pricing of the geometric Asian options under a multifactor stochastic volatility model

This paper focuses on the pricing of continuous geometric Asian options (GAOs) under a new multifactor stochastic volatility model. The model considers fast and slow mean reverting factors of volatility, where slow volatility factor is approximated by a quadratic arc. The asymptotic expansion of the price function is assumed, and the first order price approximation is derived using the perturbation techniques for both floating and fixed strike GAOs. Much simplified pricing formulae for the GAOs are obtained in this multifactor stochastic volatility framework. The zeroth order term in the price approximation is the modified Black–Scholes price for the GAOs. This modified price is expressed in terms of the Black–Scholes price for the GAOs. The accuracy of the approximate option pricing formulae is established, and also verified numerically by comparing the model prices with the Monte Carlo simulation prices and the Black–Scholes prices for the GAOs. The model parameter is estimated by capturing the volatility smiles. The sensitivity analysis is also performed to investigate the effect of underlying parameters on the approximated prices.

The pricing of volatility risk in the US equity market

We analyze whether the pricing of volatility risk depends on the asset pricing framework applied in the tests, the specified volatility proxies, and the portfolio sorts used for spanning the asset universe. For this purpose, we compare the results using a macroeconomic and fundamental based asset pricing model using three proxies of volatility and uncertainty, using size/value sorted and industry sector portfolios. Our results reveal that the marginal pricing effect of the VIX volatility factor is strong and statistically significant throughout the models and specifications, while the effect of an EGARCH-based volatility factor is mixed, mostly smaller but with the correct sign. In most cases, the EGARCH factor does not impair the pricing effect of the VIX. The portfolio sorts have a substantial impact on the volatility premiums in both model frameworks. The size of the volatility risk premium is more uniform across the models if the industry sector portfolio sort is used. Finally, the size/value portfolio sort generates larger volatility risk premiums for both models.

Analysis of Influencing Factors of Financial Market Volatility Based on Cluster Analysis

The financial market has certain volatility in its stability due to the influence of many factors. In the analysis of the financial market, it is necessary to combine scientific and effective intelligent algorithms. Based on the cluster analysis algorithm, this paper analyzes the factors affecting financial market volatility. Moreover, this paper conducts a more in-depth study and summary of the fuzzy C-means clustering algorithm, derives the analysis and solution process of the improved algorithm in detail, and gives the steps to improve the clustering algorithm. In addition, this paper constructs an analysis model of financial market volatility influencing factors based on cluster analysis and establishes a supply chain financial credit risk evaluation index system that includes the quality of corporate managers, corporate indicators, and asset structure. Finally, this paper verifies the performance of the algorithm model of this paper through experimental research. It can be seen from the experimental research results that the methods and models proposed in this paper have certain effects.

Volatility Factors of Returns and Risk Analysis Using Quantile Risk Measures in the Gold and Silver Market

The aim of the paper is to identify unobservable factors that may significantly determine the level of gold and silver returns and to assess the risk of investment in these metals. To measure risk, the value at risk and other, less popular measures are used: the ES, MS, Rachev ratio and GlueVaR risk measure. Normal and Student’s t‑distributions are used as theoretical distributions. The results of the study show that we can identify latent factors based on observable variables that have a significant impact on the level of gold and silver returns. In addition, it was observed that the risk measures would vary depending on the period of research. It was shown that the estimates of the risk measures using Student’s t‑distribution have a lower estimation error than those based on the normal distribution.

Forecasting Stochastic Volatility Characteristics for the Financial Fossil Oil Market Densities

This paper builds and implements multifactor stochastic volatility models for the international oil/energy markets (Brent oil and WTI oil) for the period 2011–2021. The main objective is to make step ahead volatility predictions for the front month contracts followed by an implication discussion for the market (differences) and observed data dependence important for market participants, implying predictability. The paper estimates multifactor stochastic volatility models for both contracts giving access to a long-simulated realization of the state vector with associated contract movements. The realization establishes a functional form of the conditional distributions, which are evaluated on observed data giving the conditional mean function for the volatility factors at the data points (nonlinear Kalman filter). For both Brent and WTI oil contracts, the first factor is a slow-moving persistent factor while the second factor is a fast-moving immediate mean reverting factor. The negative correlation between the mean and volatility suggests higher volatilities from negative price movements. The results indicate that holding volatility as an asset of its own is insurance against market crashes as well as being an excellent diversification instrument. Furthermore, the volatility data dependence is strong, indicating predictability. Hence, using the Kalman filter from a realization of an optimal multifactor SV model visualizes the latent step ahead volatility paths, and the data dependence gives access to accurate static forecasts. The results extend market transparency and make it easier to implement risk management including derivative trading (including swaps).

Fluctuations in global output volatility

In this paper, we dissect the time-varying output volatility of the main world economies to study its dynamics, spillovers, and determinants, from a global perspective. Our analysis relies on a hierarchical volatility factor model and Bayesian model averaging. We show that the increasing comovement observed in international macroeconomic volatility is substantially larger in developing than in developed countries. Instead, developed countries have exhibited more asymmetric volatility shocks than developing countries in recent times. We also show that, although the downward trend in global volatility is related with increasing trade, idiosyncratic changes in volatility are highly influenced by domestic monetary policies. However, due to the declining role played by these idiosyncratic components over time, policymakers currently face greater constraints when it comes to stabilizing output fluctuations.

A discrete-time hedging framework with multiple factors and fat tails: On what matters

This article presents a quadratic hedging framework for a general class of discrete-time affine multi-factor models and investigates the extent to which multi-component volatility factors, fat tails, and a non-monotonic pricing kernel can improve the hedging performance. A semi-explicit hedging formula is derived for our general framework which applies to a myriad of the option pricing models proposed in the discrete-time literature. We conduct an extensive empirical study of the impact of modelling features on the hedging effectiveness of S&P 500 options. Overall, we find that fat tails can be credited for half of the hedging improvement observed, while a second volatility factor and a non-monotonic pricing kernel each contribute to a quarter of this improvement. Moreover, our study indicates that the added value of these features for hedging is different than for pricing. A robustness analysis shows that a similar conclusion can be reached when considering the Dow Jones Industrial Average. Finally, the use of a hedging-based loss function in the estimation process is investigated in an additional robustness test, and this choice has a rather marginal impact on hedging performance.

Prediction of Stock Prices Using Statistical and Machine Learning Models: A Comparative Analysis

Abstract With the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms namely Kalman filters, XGBoost and ARIMA. Kalman filters are recursive and use a feedback mechanism to perform error correction. This correction makes them best suited for making accurate predictions as they can factor in the market volatility, whereas XGBoost is a promising technique for datasets that are nonlinear and can gather knowledge by detecting patterns and relationships in the data. XGBoost is also capable of capturing the time dependency of features efficiently. ARIMA refers to an Auto Regressive Integrated Moving Average model that has become very popular in recent times. It is mostly used on time series data and works by eliminating its stationarity. Finally, a hybrid model combining Kalman filters and XGBoostis discussed and a comparison of the results of each of the four models, are made to provide a better clarity for making investments by forecasting the price of a stock.

Volatility morphology of asset value and credit spread puzzle

Merton model has provided a classic theoretical framework for explaining credit spreads. This paper extends Merton model by introducing morphology factor of asset value volatility in the model, and conducts empirical studies on the effect of asset volatility morphology on credit spreads in China’s bond market. The results show that asset volatility morphology is economically important and can explain credit spreads well. Furthermore, this paper analyzes the asymmetric influences of monetary policy on credit spreads and asset volatility morphology. This paper points out that the responses of credit spreads and asset volatility morphology to monetary policy are consistent in the tight liquidity environments. To this end, monetary policy and liquidity, which are two factors that have been ignored by classic Merton model but proved to have significant influences on credit spreads, play roles in influencing credit spreads by changing volatility morphology of asset value. Since asset volatility morphology can reflect the change of investors’ expectation on the default probability of asset, the argument mentioned in the credit spread puzzle that the fundamentals related to bond default probability cannot explain credit spreads needs to be reexamined.

Does behavioral-motivated volatility effect explain the beta anomaly? Evidence from China

We find that the beta anomaly in the Chinese stock market is mainly driven by behavioral effects measured by lottery demand or idiosyncratic risk. The betting against volatility factor that is closely related to behavioral effects produces significant positive alpha, while the alpha of the betting against correlation factor related to leverage constraints is insignificant. When a lottery demand or an idiosyncratic risk factor is added to the well-established factor models, the beta anomaly disappears.

Low volatility and ESG investing combined: Invesco’s holistic approach

The low volatility factor in conjunction with the style factors Quality, Value and Momentum, has empirically proven to be able to moderate market risks and improve a portfolio’s overall risk-return profile. By integrating ESG into such a factor portfolio, future risks may be mitigated. We present a proprietary approach to managing ESG risks that can maximize sensitivities to the desired multi-factor characteristics, and we calculate Climate VaR under different global warming scenarios.

Common and Idiosyncratic Conditional Volatility Factors: Theory and Empirical Evidence

We propose a multiplicative dynamic factor structure for the conditional modelling of the variances of an N-dimensional vector of financial returns. We identify common and idiosyncratic conditional volatility factors. The econometric framework is based on an observation-driven time series model that is simple and parsimonious. The common factor is modeled by a normal density and is robust to fat-tailed returns as it averages information over the cross-section of the observed N-dimensional vector of returns. The idiosyncratic factors are designed to capture the erratic shocks in returns and therefore rely on fat-tailed densities. Our model is potentially of a high-dimension, is parsimonious and it does not necessarily suffer from the curse of dimensionality. The relatively simple structure of the model leads to simple computations for the estimation of parameters and signal extraction of factors. We derive the stochastic properties of our proposed dynamic factor model, including bounded moments, stationarity, ergodicity, and filter invertibility. We further establish consistency and asymptotic normality of the maximum likelihood estimator. The finite sample properties of the estimator and the reliability of our method to track the common conditional volatility factor are investigated by means of a Monte Carlo study. Finally, we illustrate our approach with two empirical studies. The first study is for a panel of financial returns from ten stocks of the S&P100. The second study is for the panel of returns from all S&P100 stocks.

What drives volatility of the U.S. oil and gas firms?

We study how the day-ahead stock price volatility of 15 firms that are S&P 500 constituents in the Oil & Gas Exploration & Production sub-industry is driven through six volatility factors represented by realized volatilities, namely, (i) firms’ own volatility, (ii) industry market volatility, (iii) local (U.S.) market volatility, (iv) world equity market volatility, (v) oil price volatility, and (vi) natural gas price volatility. Existing studies have reported results based on analysis of one or few volatility components, but given the high dependence among volatility factors, this might bias (overestimate) the true importance of each of the volatility factors on the price fluctuation of stocks in the Oil & Gas Exploration & Production sub-industry. To take into account this inter-relatedness of volatility factors, we study all volatility factors together. Using augmented heterogeneous autoregressive (HAR) models and dynamic model averaging, our analysis shows that market volatility is most influential, followed by a stock’s own volatility and industry level volatility. The role of the volatility of the oil market is of lesser importance, while the volatility of the world equity market does not appear to contain incremental information useful for predicting the volatility of firms in the Oil & Gas Exploration & Production sub-industry. The role of the natural gas market is specific. An in-sample analysis suggests a negative relationship between firm-level volatility and volatility on the natural gas market. However, in an out-of-sample framework, the volatility of the natural gas market appears to be unrelated to firm-level volatility. Dynamic model averaging further suggests that the market and industry factors are time-varying. These findings have implications for financial risk management, as we show that in an out-of-sample framework, HAR models augmented with volatility factors outperform the plain HAR model by up to a 3.88% increase in volatility forecast accuracy.

Trust‐aware secure routing protocol for wireless sensor networks

A trust-aware secure routing protocol (TSRP) for wireless sensor networks is proposed in this paper to defend against varieties of attacks. First, each node calculates the comprehensive trust values of its neighbors based on direct trust value, indirect trust value, volatilization factor, and residual energy to defend against black hole, selective forwarding, wormhole, hello flood, and sinkhole attacks. Second, any source node that needs to send data forwards a routing request packet to its neighbors in multi-path mode, and this continues until the sink at the end is reached. Finally, the sink finds the optimal path based on the path's comprehensive trust values, transmission distance, and hop count by analyzing the received packets. Simulation results show that TSRP has lower network latency, smaller packet loss rate, and lower average network energy consumption than ad hoc on-demand distance vector routing and trust based secure routing protocol.

Time-varying Uncertainty of the Federal Reserve's Output Gap Estimate

<ns3:p>A factor stochastic volatility model estimates the common component to estimates of the output gap produced by the staff of the Federal Reserve, its time-varying volatility, and time-varying, horizon-specific forecast uncertainty. Output gap estimates are very uncertain, even well after the fact, especially at business cycle turning points. However, the common component of the output gap estimates is clearly procyclical, and innovations to the common factor produce persistent positive effects on economic activity. Output gaps estimated by the Congressional Budget Office have very similar properties. Increased macroeconomic uncertainty, as measured by the common factor's volatility, leads to persistent negative responses in economic variables.</ns3:p>

Idiosyncratic Volatility, Conditional Liquidity, and Cross-section of Stock Returns in Korea

Purpose - This study examines whether flight-to-liquidity (FTL) explains the dynamic liquidity risk on stock returns, and whether it has a significant influence on determinants the cross-section of stock returns. Design/methodology/approach - This study suggests a new risk factor, dynamic liquidity hedge portfolio (DLP), to reflect the dynamic impact of liquidity risk on stock returns and the Fama-MacBeth 2 stage regression analysis is employed in order to analyze the data. Findings - First, the DLP factor shows more positive and significant beta for the small or illiquidity stocks. Second, the DLP shows a different influence than SMB (size risk factor), HML (value risk factor), NMP (liquidity risk factor), FTVOL (total volatility factor) in determining the cross-section of stock returns. In addition, the DLP has a statistically significant risk premium of around 5%, which is relatively larger than other risk factors. Research implications or Originality - This study has academic value in terms of newly confirming that the DLP factor has a more significant impact on cross-sectional determination of stock returns than other risk factors by proposing a conditional liquidity factor that can explain the FTL phenomenon.

Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm

In view of the shortcomings of traditional ant colony algorithm (ACO) in path planning of indoor mobile robot, such as a long time path planning, non-optimal path for the slow convergence speed, and local optimal solution characteristic of ACO, an improvement adaptive ant colony algorithm (IAACO) is proposed in this paper. In IAACO, firstly, in order to accelerate the real-time and safety of robot path planning, angle guidance factor and obstacle exclusion factor are introduced into the transfer probability of ACO; secondly, heuristic information adaptive adjustment factor and adaptive pheromone volatilization factor are introduced into the pheromone update rule of ACO, to balance the convergence and global search ability of ACO; Finally, the multi-objective performance indexes are introduced to transform the path planning problem into a multi-objective optimization problem, so as to realize the comprehensive global optimization of robot path planning. The experimental results of main parameters selection, path planning performance in different environments, diversity of the optimal solution show that IAACO can make the robot attain global optimization path, and high real-time and stability performances of path planning.

Bond Return Predictability and its Economic Value: The Case of China

This paper investigates bond return predictability and its economic value. Using regression models, we first examine both the statistical and economic significance of bond return predictability in the Chinese market, and analyze the non-Markov and stochastic volatility properties of bond yields. On the basis of the above analysis, we propose a systematic method for constructing non-Markov dynamic term structure models (DTSMs) under a generalized Heath-Jarrow-Morton (HJM) framework with stochastic volatility. Then, we investigate the roles of the non-Markov property and stochastic volatility in bond return predictability and its economic gains realizing. Finally, we analyze the economic drivers of bond return predictability. Empirical results show that bond return predictability in the Chinese market is statistically significant, which also can be converted into significant economic gains. The non-Markov property and stochastic volatility are of critical importance for this conversion process. Moreover, time-varying risk premia driven by the economic environment are the main source of the bond return predictability in the Chinese market, while unspanned stochastic volatility factors also contain much information for future bond returns.

Toward pricing financial derivatives with an IBM quantum computer

Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce the time-evolution of interest rates. Several stochastic dynamics have been proposed in the literature to model either the instantaneous interest rate or the instantaneous forward rate. A successful approach to model the latter is the celebrated Heath-Jarrow-Morton framework, in which its dynamics is entirely specified by volatility factors. On its multifactor version, this model considers several noisy components to capture at best the dynamics of several time-maturing forward rates. However, as no general analytical solution is available, there is a trade-off between the number of noisy factors considered and the computational time to perform a numerical simulation. Here, we employ the quantum principal component analysis to reduce the number of noisy factors required to accurately simulate the time evolution of several time-maturing forward rates. The principal components are experimentally estimated with the $5$-qubit IBMQX2 quantum computer for $2\times 2$ and $3\times 3$ cross-correlation matrices, which are based on historical data for two and three time-maturing forward rates. This manuscript is a first step towards the design of a general quantum algorithm to fully simulate on quantum computers the Heath-Jarrow-Morton model for pricing interest-rate financial derivatives. It shows indeed that practical applications of quantum computers in finance will be achievable in the near future.