Asset Price Fluctuations Research Articles

Does US Dollar Supply Trigger House Price Fluctuations? An International Comparison

Cyclical fluctuations in asset prices are closely related to the overall stability of the macroeconomy and have thus become a significant topic in academic and professional exploration. Among these fluctuations, changes in the supply of the U.S. dollar, the global reserve, and primary settlement currency have a broad and far-reaching impact on international financial markets. Since COVID-19, asset prices across various countries have shown differing trends due to fluctuations in the U.S. dollar supply. Housing prices, a key focus in asset pricing research, have been affected to varying degrees. This thesis utilizes seven house price indices from representative cities worldwide from 2020 to 2023 to compare the differential impacts across various countries using an empirical approach. The results indicate that the impact of the U.S. dollar money supply (M2) diminishes in a gradient from the U.S. to East Asia, and then to Europe.

Understanding uncertainty shocks and the role of black swans

Uncertainty shocks can trigger business cycles, bank runs and asset price fluctuations. But how do those shocks translate into changes in beliefs and actions? What makes people suddenly become uncertain? This paper provides an explanation for subjective uncertainty shocks, estimated on real-time GDP data, that fits puzzling features of professional forecasts. The key ingredients are that agents do not know the true model governing the distribution of macroeconomic outcomes, but they believe that distribution to be potentially skewed. When agents estimate higher probabilities of extreme tail events, their uncertainty rises. Our estimates reveal that learning about probabilities of tail events – black swans – explains much of the fluctuations in subjective uncertainty.

Household finances, debt overhang and consumption patterns

This paper studies the effect of household debt on consumption throughout the business cycle. We use household-level data for Spain in a period, 2002–2017, characterized by significant fluctuations in leverage, consumption, and asset prices. We find that high levels of debt have a negative effect on consumption during recession periods, while previous debt accumulation does not significantly influence consumption. When considering heterogeneities across types of debt, we observe that households adjust their spending faster in response to debt not associated with real estate assets. By exploiting explicit information on household credit constraints, we find no evidence suggesting that they play a significant role in shaping the debt-consumption nexus. We conclude that in a situation of high leverage and financial stress, debt overhang decreases household consumption. In this context, policies aimed at preventing households from leveraging excessively during expansions, and policies providing debt relief during recessions, can help mitigate consumption shocks.

Consumption Growth Persistence and the Stock–Bond Correlation

Abstract We consider a model in which the correlation between shocks to consumption and to expected future consumption growth is nonzero and varies over time. We validate this assumption empirically using the model’s implication that time variation in consumption growth persistence (CGP) drives the correlation between stock and bond returns. Our model implies that the stock–bond correlation is also related to the predictive relation between bond yields and future stock returns. Finally, we provide suggestive evidence that asset price fluctuations are the primary driver of changes in CGP.

The Inherent Law of the Unpredictability of Financial Asset Price Fluctuations: Multistability and Chaos

The Inherent Law of the Unpredictability of Financial Asset Price Fluctuations: Multistability and Chaos

Measuring economic insecurity by combining income and wealth: an extended well-being approach

AbstractIn this paper, we propose the use of an extended well-being approach to assess economic insecurity. Our main purpose is to study its dimension and identify its main drivers in the United States by overcoming the dichotomy between income and wealth. To this end, we approximate an extended well-being measure that includes monetary resources from income and the potential stream from wealth, which can be understood as an emergency reserve to cope with future economic difficulties but could also be a source of financial distress due to fluctuations in asset holdings and prices. We find that economic insecurity levels are larger when considering our extended well-being variable than income alone. Household income and non-liquid assets appear to be the main drivers of economic insecurity, although part of the US population was able to obtain higher returns on non-liquid assets and maintain their income levels.

Explore jump behaviour in China's crude oil future market during the COVID-19

ABSTRACT We have conducted a thorough analysis of four stochastic volatility models, including double exponential jumps and infinite-activity Lévy jumps. By selecting recent data from Shanghai crude oil futures and Brent crude oil, we explore the impact of the COVID-19 pandemic and characterized the price fluctuations in the Shanghai crude oil market. We estimate the model parameters using the MCMC method and discuss the significance of jumps in crude oil futures returns. Our study provides evidence that the double exponential jump model is more sensitive to asset price fluctuations and shocks and can better capture the price dynamics of the crude oil market.

Volatility, Uncertainty, and Option Pricing

Amidst global and Chinese uncertainties, this paper delves into stock market volatility and option pricing within the current economic policy context. Focusing on China's stock market, it calculates returns and volatility changes in key indices, analyzing differences among indices and funds, along with temporal variations. Shared data characteristics and reflections on new market trends emerge. Employing methods such as the GARCH model, Black-Scholes formula, and Monte Carlo algorithm, it analyzes volatility and option pricing using extensive annual and monthly data. Visualizations showcase market change patterns. The study defines volatility as a measure of financial asset price fluctuation extent, reflecting asset risk. Higher volatility indicates pronounced price fluctuations and uncertainty, while lower volatility signifies smoother fluctuations and greater certainty. Merging data with volatility's significance, the study probes China's securities market uncertainty, investigating the link between option pricing and volatility. It concludes by identifying the connection between volatility, uncertainty, and option pricing, pointing to future research directions and challenges. Future work will track the latest market trends to enrich understanding.

MULTIPLE-BUBBLE TESTING IN THE CRYPTOCURRENCY MARKET: A CASE STUDY OF BITCOIN

Economic periods and financial crises have highlighted the importance of evaluating financial markets to investors and researchers in recent decades. The asset price fluctuation is an innate quality of the market. However, fluctuations sometimes become abnormal and turn into unbridled spikes and sudden declines, causing irreversible damage to an economy and the trust of investors. In recent years, there have been severe price fluctuations in the cryptocurrency market. Some analysts have defined these fluctuations as price bubbles (i.e., economic bubbles). However, no specific definitions have yet been proposed for these price bubbles. The right-tail augmented Dickey–Fuller (RTADF) test was conducted on the time series data of 13 years (2008–2021) to analyze the presence of bubbles in the Bitcoin market. The RTADF results rejected the null hypothesis and confirmed the alternative hypothesis indicating the presence of bubbles in the Bitcoin market. In other words, there is some evidence of explosive behavior in a bubble. Moreover, the results of GSADF and SADF tests confirmed the presence of multiple bubbles in the Bitcoin market within 2008–2021.

Option Pricing with Fractional Stochastic Volatilities and Jumps

Empirical studies suggest that asset price fluctuations exhibit “long memory”, “volatility smile”, “volatility clustering” and asset prices present “jump”. To fit the above empirical characteristics of the market, this paper proposes a fractional stochastic volatility jump-diffusion model by combining two fractional stochastic volatilities with mixed-exponential jumps. The characteristic function of the log-return is expressed in terms of the solution of two-dimensional fractional Riccati equations of which closed-form solution does not exist. To obtain the explicit characteristic function, we approximate the pricing model by a semimartingale and convert fractional Riccati equations into a classic PDE. By the multi-dimensional Feynman-Kac theorem and the affine structure of the approximate model, we obtain the solution of the PDE with which the explicit characteristic function and its cumulants are derived. Based on the derived characteristic function and Fourier cosine series expansion, we obtain approximate European options prices. By differential evolution algorithm, we calibrate our approximate model and its two nested models to S&P 500 index options and obtain optimal parameter estimates of these models. Numerical results demonstrate the pricing method is fast and accurate. Empirical results demonstrate our approximate model fits the market best among the three models.

Exploring Bubbles in the Digital Economy: The Case of China

This study identifies the cyclical boom-and-bust episodes in digital economy-related assets and determines whether bubbles exist in China's digital economy. The extant literature mainly focuses on tech or cryptocurrency bubbles and has not considered the digital economy. Therefore, we use data from March 22, 2017, to March 3, 2022, and employ the generalized supremum augmented Dickey–Fuller (GSADF) technique to capture the irrational prosperity in China's digital economy. The conclusion suggests the existence of five bubbles in China's digital economy, all accompanied by extreme fluctuations in related asset prices, which the asset-pricing model supports. Under the backdrop of the scientific and technological revolution and industrial transformation, this exploration offers meaningful suggestions for China to facilitate the sound development of the digital economy.

Feedback Trading, Investor Sentiment and the Volatility Puzzle: An Infinite Theoretical Framework

This article constructs a behavioral financial model that provides feedback on both historical prices and company fundamentals without considering asset liquidation to discuss the long-term impact of investor sentiment and feedback trading on asset price fluctuations. The research conclusion shows that the abnormal volatility of asset prices is captured by the value effect, the cognitive bias effect, the sentiment shock effect, and the trading inductive effect. The value effect is the volatility of asset prices that is completely determined by fundamental factors; the higher the degree of cyclical fluctuations in fundamental factors, the higher the volatility of prices. The bias effect refers to investors’ misreading of basic information and trends in asset prices; the greater the instability of emotional shocks, the greater the abnormal volatility of asset prices. The trading inductive effect is also called the Keynes effect, which reflects the role played by rational traders.

Pricing Variance Swaps under MRG Model with Regime-Switching: Discrete Observations Case

In this paper, we creatively price the discretely sampled variance swaps under the mean-reverting Gaussian model (MRG model in short) with regime-switching asymmetric double exponential jump diffusion. We extend the traditional MRG model by further considering the trend of the financial market as well as a sudden and unexpected event of the market. This new model is meaningful because it uses observable Markov chains that represent market states to adjust its parameters, which helps capture the movement of the market and fluctuations in asset prices. By utilizing the characteristic function and the conditional transition characteristic function, we obtain analytical solutions for pricing formulae. Note that this is our first effort to provide the analytical solution for the ordinary differential equations satisfied by the Feynman–Kac theorem. To achieve this, we have developed a new methodology in Proposition 2 that involves dividing the sampling interval into more detailed switching and non-switching intervals. One significant advantage of our closed-form solution is its high computational accuracy and efficiency. Subsequent semi-Monte Carlo simulations will provide specific validation results.

Digital financial asset price fluctuation forecasting in digital economy era using blockchain information: A reconstructed dynamic-bound Levenberg–Marquardt neural-network approach

Digital financial assets such as cryptocurrency are playing an increasingly crucial role in the digital economy era. Cryptocurrency is characterized by significant volatility and asset price fluctuations in the short term. Therefore, the development of an accurate and technologically reliable forecasting approach is important. For accurately predicting the closing price of the cryptocurrency, as a representative digital financial asset, we developed a reconstructed dynamic-bound Levenberg–Marquardt neural network (R-DB-LM-NN) architecture and a corresponding neural-network training algorithm with a moving-boundary mechanism for evaluating the correctness of each descent direction. We used a high-frequency blockchain information dataset for training and prediction. The dynamic bound was introduced to increase the step size so that the neural network could effectively cross the local minimum and to avoid interrupting the neural-network iteration process. Then, we built a high-frequency encrypted digital currency blockchain information dataset. Experiments confirmed that the proposed architecture and algorithm are superior to traditional neural-network machine-learning methods, such as artificial neural networks, and deep-learning methods, such as long short-term memory and convolutional neural networks, with regard to prediction performance. Finally, the implications of the study and limitations of the proposed approach are discussed, along with the extension of the approach to other time series research domains, for researchers and practitioners.

Climate risk and financial systems: A nonlinear network connectedness analysis

To deeply analyze and understand the macro-financial impact of climate change, this paper investigates the effect of climate risk on systemic financial risks by employing a network approach. The results demonstrate that climate risk not only affects a single financial market but also induces risk co-movement, which aggravates potential systemic financial risks. Specifically, the system-wide connectedness across the financial system respectively increased by 2.52% and 1.76% after the withdrawal of the US from the Kyoto Protocol and the Copenhagen UN Climate Change Conference. The bond and stock markets are the primary transmitters of climate shocks, while the forex and commodity markets appear to be more sensitive to climate-related information. In addition, the vulnerability of financial asset price fluctuations to climate risk changes substantially over time. Quantile regressions reveal the positive impact of climate risk on total connectedness across the financial system. This study provides novel insight into how the financial system responds to climate-related information and how systemic risk dynamics materialize.

Quantitative easing in the US and financial cycles in emerging markets

Large international capital movements tend to be associated with strong fluctuations in asset prices and credit, contributing to domestic financial cycles and posing challenges for stabilization policies, especially in emerging market economies. In this paper we argue that these challenges are particularly severe if the global financial cycle is driven by quantitative easing (QE) in the US, and when the local banking sector has large holdings of government bonds, like in many Latin American (LA) countries. We first investigate empirically the impact of a typical round of QE by the US Fed on LA economies, finding a persistent expansion in credit to households and house prices, as well as a significant loss of price competitiveness. We next develop a quantitative macroeconomic model of a small open economy with segmented asset markets and banks, which accounts for these observations. In this framework, foreign QE creates tensions between macroeconomic and financial stability as a contractionary impact of exchange rate appreciation is accompanied by booming credit and house prices. As a consequence, conventional monetary policy accommodation aimed at stabilizing output and inflation would further exacerbate domestic financial cycle. We show that an effective way of resolving this trade-off is to impose measures that directly reduce the inflow of capital. Combining foreign exchange interventions with tightening of local credit policies can also restore macroeconomic and financial stability, but at the expense of a large redistribution of wealth between borrowers and savers.

Retracted: The Influence and Prediction of Industry Asset Price Fluctuation Based on the LSTM Model and Investor Sentiment

Retracted: The Influence and Prediction of Industry Asset Price Fluctuation Based on the LSTM Model and Investor Sentiment

Can cryptocurrencies provide a viable hedging mechanism for benchmark index investors?

This paper investigates the non-linear causal nexus at lower, medium, and upper quantiles that cryptocurrencies can generate as a hedging mechanism for international benchmark indices, as represented by the MSCI World index and its sectoral sub-indices. Significant causality-in-mean between each of the assets examined is detected at lower quantiles only in a small number of sectors, whereas all analysed assets are found to trigger important non-linear causal impacts on the volatility of the selected benchmark indices, particularly during periods denoted to possess significant asset price fluctuations. From a financial perspective, this result suggests that cryptocurrencies are developing to possess a very similar ability to that of long-standing traditional financial assets, serving as a hedging mechanism against extreme fluctuations within investor portfolios that include MSCI constituents during a variety of very different market conditions.

Real estate assets, heterogeneous firms, and debt stability

By constructing a “financial accelerator” model that combines real estate assets and heterogenous enterprises, we use China’s housing price data to explore the effect of real estate assets on corporate debt and macroeconomic stability in the context of the Chinese economy. Based on the dynamic economic characteristics of the model, we find that the financial accelerator mechanism of housing prices further amplifies the various effects of housing price fluctuations when transmitted to the macroeconomic system, resulting in the size of corporate debt closely related to the economic boom–bust cycles. However, owing to the institutional factors, heterogenous firms have some differences in debt stability under the influence of asset price fluctuations. There is an obvious crowding-out effect of state-owned enterprise (SOE) credit on private-owned enterprise (POE) credit, and the existence of the implicit guarantee ratio of SOE credit will weaken the external financing constraints on the SOE and promote the expansion of SOE credit. Finally, the effect of fiscal and monetary policies on corporate debt stability is somewhat divergent, institutional factors can similarly weaken policy effects.

Asset Prices and Unemployment Fluctuations: A Resolution of the Unemployment Volatility Puzzle

Abstract Recent work has demonstrated that existing solutions of the unemployment volatility puzzle are at odds with the procylicality of the opportunity cost of employment, the cyclicality of wages, and the volatility of risk-free rates. We propose a model of business cycles that is immune to these critiques by incorporating two key features. First, we allow for preferences that generate time-varying risk over the business cycle to account for observed fluctuations in asset prices. Second, we introduce human capital acquisition, as is consistent with the evidence on how wages grow with experience in the labor market. Our model reproduces the observed fluctuations in unemployment because hiring a worker is a risky investment with long-duration returns. As in the data, the price of risk in our model sharply increases in recessions. The benefit from hiring new workers therefore greatly declines, leading to a large decrease in job vacancies and an increase in unemployment of the same magnitude as in the data. We show that our results extend to versions of the model that include physical capital, a life cycle for workers, and alternative preference structures common in the asset-pricing literature.