Asset Price Volatility Research Articles

Impact of the Fed's Rate Hike Policy on the Prices of Commodities and Real Estate

The United States has experienced a long period of stubborn inflation since 2022, so the Federal Reserve began to raise interest rates in March 2022, which will cause asset price volatility and valuation changes. The purpose of this study is to explore the changes of some commodity and real estate prices under this background, and to put forward reasonable valuation forecast suggestions. This research mainly analyzes the price changes of crude oil, corn, and real estate under the background of the Fed's interest rate hike and analyzes the changes of asset prices by constructing the structure vector autoregressive (SVAR) model and the grid connectivity model. According to the analysis, the results show that the Fed can influence real asset prices through its monetary policy, and other factors inevitably interfere. The empirical data from March 2022 to March 2024 also show that with the Fed's multiple interest rate hikes, asset prices are affected by different degrees of decline.

Model predictive control for multi-period portfolio optimization: a trading-oriented learning approach

Portfolio optimization aims at finding the optimal allocation of a given wealth among different assets to maximize an investor's utility function. A major critical issue concerns the prediction of future asset returns to forecast market evolution, due to the non-stationarity and volatility of asset prices. In this work, a learning-based Model Predictive Control (MPC) strategy for multi-period portfolio optimization is proposed, where the return prediction model is estimated via a novel trading-oriented learning paradigm. According to such a perspective, the model parameters are not the ones minimizing the prediction error but those that directly maximize the investor's utility. An extensive experimental study carried out on real-market data shows the potential improvements introduced by the proposed methodology compared to benchmark financial strategies.

Does derivative usage boost firm value in an economy with controls? Evidence from India

AbstractThe role of derivatives on firm valuation has recently become a subject of intense debate for emerging markets where there is greater asset price volatility, illiquidity, and the absence of common regulations and accounting standards. Given the lack of data availability, disclosure inconsistency and distinctive regulatory requirements for Indian firms, we assess the impact of corporate hedging via derivative use on the performance of BSE-100 non-financial firms over the period 2010–2017. Our results from keywords-based hand-collected derivative usage data show that all types of derivatives, except rate derivatives, have a significant and positive impact on firm value through channels of reduced earnings volatility and increased leverage with tax benefits. Firms with greater leverage tend to benefit from the use of rate derivatives. Still, among those highly leveraged firms, the ones who experience high earnings volatility are unable to benefit from rate derivatives. Besides, hedge accounting benefits firms in dealing with earnings volatility when they have higher derivatives exposure. As capital controls on international flows can limit the use of derivatives, we show that firms with currency risk exposure in terms of long-term foreign currency debt do benefit from rate derivatives. Our results are also robust with a matched sample for derivative users and non-users. Overall, we conclude that easing regulatory controls on derivatives dealings can be beneficial for firms in growing emerging economies.

Asymmetric volatility in asset prices: An explanation with mental framing

We propose a theoretical framework for the heteroscedasticity, and in particular for the asymmetric volatility of asset returns. Our model is based on the assumption that some investors are subject to mental framing in a dynamic setting. The analysis of individual trading data confirms that, in line with our model, investors tilt their portfolio towards riskier (less risky) assets subsequent to losses (gains). Based on their behavior, we derive a volatility process that accounts for the asymmetry thoroughly investigated in previous empirical studies: the parameter estimation of our volatility model yields the predicted negative relationship between abnormal returns and ensuing volatility.

Construction of a climate risk index under the media attention perspective and its empirical test for financial market volatility

Under the current economic situation, China’s economy is steadily moving from the stage of high-speed growth to that of high-quality development. while climate risks arising from global warming are receiving increasing media attention. Media attention has a profound impact on the perception and investment decisions of financial market participants. This paper constructs a climate risk index based on media attention and empirically investigates its relationship with stock volatility. It is found that the climate risk media index significantly affects stock volatility, which provides a theoretical basis for the regulation of financial asset price volatility. Through in-depth study of its influence mechanism, it can provide accurate decision support and risk management strategies for financial market stabilization.

A new bivariate approach for modeling the interaction between stock volatility and interest rate: An application to S&P500 returns and options

The GARCH models developed so far do not take into account the interaction between the volatility of asset returns and the dynamics of the interest rate. In this paper, we propose a bivariate GARCH model in which interest rate movements and asset price volatility are fully coupled. This approach yields explicit and simple to implement recursion formulas for the moment generating function, which can be exploited to compute option prices by applying the fast Fourier transform or other convolution techniques. We perform a thorough and comprehensive empirical analysis based on real S&P500 return and option data showing the usefulness and robustness of the suggested methodology. Both in-sample and out-of-sample results reveal the superiority of our approach over the GARCH model with constant interest rates.

Sensitivity Analysis Based on Portfolio Option Models Using Tesla and Nio as Examples

The electric vehicle (EV) industry is undergoing a significant transformation, driven by environmental concerns, advancements in battery technology, and changing consumer preferences. Companies like Tesla and China's NIO are at the forefront of this revolution, showcasing the global potential of EVs. This study undertook a sensitivity analysis of these two leading EV manufacturers, Tesla and NIO, to understand the dynamics of their stock prices and implications for investment strategies. Data was meticulously collected from various financial databases to capture daily stock prices, which were then used to compute monthly returns. The study also delved into options, focusing on the strike prices and volatility of Tesla and NIO options. Using a combination of stock prices, strike prices, and volatility, the intrinsic and time value of these options were analyzed. The research employed a composite option model to describe the price variation of an option, considering factors like asset price volatility, risk-free rate, and dividend yield. The findings revealed that stock prices and option prices exhibited certain sensitivities to market variables. For instance, Tesla's option prices showed an upward trend with rising stock prices, while NIO's option prices displayed varied responses to stock price changes. The study underscores the importance of understanding these sensitivities for informed investment decisions in the evolving EV market.

A study on predictive analysis and rules for international financial risk considering debt crisis

Abstract This paper first establishes a system of international financial risk early warning indicators, constructs a VAR forecasting model for international financial risk, and uses the ADF test to identify the best amount of lags for the model before doing a smoothness test on the variables. The lagged values are used as independent variables to establish equations to describe the dynamic relationship between multiple variables. Then the model is tested for cointegration and Grange causality. The VAR model is then used to estimate the financial risk profile from the standpoint of a global economy and to examine the extent to which the debt crisis has affected international financial risk. The results show that at the 5% significant level, national debt expansion is the cause of asset price volatility, macroeconomic volatility, and debt risk. The forecasted inflation rate for 2023-2025 is 4.18, output gap is 4.94, and the policy interest rate is 4.18 for emerging Asian countries and regions.

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.

Going with the crowd in volatile times: Exposure to environmental variability increases people's preference for popular options

AbstractMore extreme temperature and precipitation events are defining features of climate change, and higher volatility in asset prices is a defining feature of globalization. Four experiments (two preregistered; total N = 2086) found that exposure to a high degree of variability in a given domain shifted people's preferences toward more popular products, that is, products rated by a larger number of consumers. This finding replicated across different experimental manipulations of variability, including graphs depicting either high or low variability in annual rainfall or temperature (Experiments 1 and 2), and in the experienced outcomes of dice rolls, which were manipulated to be perceived as having high or low variability (Experiment 3). The results generalized across different consumer choices, including services (Experiment 1) and products (Experiments 2 and 3). After exposure to higher variability, participants who received a more popular but lower rated option felt less anxious than those who received a less popular but higher rated option, indicating that choosing popular products serves to reduce the anxiety induced by higher variability (Experiment 4). This research highlights both a novel consequence of exposure to greater variability and a novel antecedent of people's preference for popular options.

Uniform Nonparametric Inference for Spatially Dependent Panel Data

This article proposes a uniform functional inference method for nonparametric regressions in a panel-data setting that features general unknown forms of spatio-temporal dependence. The method requires a long time span, but does not impose any restriction on the size of the cross section or the strength of spatial correlation. The uniform inference is justified via a new growing-dimensional Gaussian coupling theory for spatio-temporally dependent panels. We apply the method in two empirical settings. One concerns the nonparametric relationship between asset price volatility and trading volume as depicted by the mixture of distribution hypothesis. The other pertains to testing the rationality of survey-based forecasts, in which we document nonparametric evidence for information rigidity among professional forecasters, offering new support for sticky-information and noisy-information models in macroeconomics.

Quantifying the short- and long-run impact of inflation-related price volatility on knowledge asset investment

The study of the link between price volatility, measured in the inflation index, and knowledge asset investment is a topic under intense research. Knowledge asset investment is moving with inflation shocks (high synchronicity), particularly at the troughs and peaks of a business cycle. This research investigates the influence of inflation shocks on knowledge asset investment. Previous studies do not provide a piece of direct empirical evidence on the impact of inflation shocks on knowledge asset investment that is available. We employ panel structural vector autoregression to explore the short and long-run effects of inflation shocks on knowledge asset investment in 29 advanced economies from 1995 to 2019. Our empirical results prove a strong and statistically significant connection between knowledge asset investment and price volatility. In addition, we identify a robust empirical link between knowledge asset investment and the inflation index. The findings further indicate that the inflation index determines knowledge asset investment. Our work contributes to technology innovation, asset allocation, and price-level research to inspire novel research on knowledge asset investment.

A time-varying of property residential price in Indonesia: a VAR approach

The crisis of 2008 started with asset price bubbles which spread to other sectors, thus driving a recession. Turmoil in the housing sector can directly harm the domestic economy and financial stability. The research aims to analyze macroeconomic variables that can affect asset prices in Indonesia and how the inflation-targeting framework directly affects asset prices. This study contributes to the current research, such as the early warning system for the asset sector that the crisis of 2008 started with asset price bubbles. The Inflation Targeting Framework (ITF) policy used by the Central Bank has shown its effectiveness in the property sector. It can be seen that a negative response is shown from property prices when there are inflationary shocks. The response of interest rates to fluctuations in housing prices is stronger than the response of housing prices to fluctuations in interest rates. It indicates that the interest rate stimulus is more reactive to changes in housing prices as an accommodation of housing price volatility. GDP and money supply will respond negatively to property price fluctuations, which can lead to a crisis because GDP responds negatively. The strengthening of fiscal and monetary policy can soften the volatility of asset prices.

The impact of simultaneous shocks to financial markets and mortality on pension buy-out prices

AbstractIn this paper, we determine the fair value of a pension buyout contract under the assumption that changes in mortality can have an impact on financial markets. Our proposed model allows for shocks to occur simultaneously in mortality rates and financial markets, so that strong changes in mortality rates can affect interest rates and asset prices. This approach challenges the common but very strong assumption that mortality and market risk drivers are independent. A simulation-based pricing framework is applied to determine the buyout premium for a hypothetical fully funded pension scheme. The results of an extensive sensitivity analysis show how buyout prices are affected by changes in mortality and financial markets. Surprisingly, we find that the impact of shocks is similar whether or not these shocks occur simultaneously or not, although there are some differences in annuity prices and buyout premiums. We clearly see that the intensity and severity of shocks, and asset price volatility play a dominant role for buyout prices.

Dynamic Asset-Backed Security Design

Abstract Borrowers obtain liquidity by issuing securities backed by the current period payoff and resale price of a long-lived collateral asset, and they are privately informed about the payoff distribution. Asset price can be self-fulfilling: a higher asset price lowers adverse selection and allows borrowers to raise greater funding, which makes the asset more valuable, leading to multiple equilibria. Optimal security design eliminates multiple equilibria, improves welfare, and can be implemented as a repo contract. Persistent adverse selection lowers debt funding, generates volatility in asset prices, and exacerbates credit crunches. The theory demonstrates the role of asset-backed securities on stability of market-based financial systems.

Comparison of the Performances for the Mean-Reversion Strategy for Exchange Rate

Mean-reversion theory predicts that historical returns and asset price volatility will eventually return to the long-run mean or average level of the entire dataset. The mean-reversion strategy applies the fundamental idea of selling high and attracting low to stock market investment. Researchers studying the international exchange market have shown that foreign exchange rates display momentum and mean reversion behavior. This study aims to assess the effectiveness of the mean-reversion approach on exchange rates and improve it. Using historical data, the author assessed the mean-reversion method's impact on the exchange rate. According to the study, the method offers a risk-to-reward ratio that is conservative. The clear disadvantage is that for most of the exchange rate, mean-reversion methods yield lesser profits. Furthermore, the return is significantly influenced by the mean-reversion lookback period. Quantitatively, exchange rates with significant volatility and variation often outperform when using the mean-reversion approach. Overall, these results shed light on guiding further exploration of mean-revision strategy.

Time and frequency uncertainty spillover among macro uncertainty, financial stress and asset markets

Purpose This study aims to examine the uncertainty spillover among eight important asset classes (cryptocurrencies, US stocks, US bonds, US dollar, agriculture, metal, oil and gold) using weekly data from 2014 to 2020. This study also examines the US macro uncertainty and US financial stress spillover on these assets. Design/methodology/approach The authors use time–frequency connectedness method to study the uncertainty spillover among the asset classes. Findings This study’s findings revealed that the uncertainty spillover is time-varying and peaked during the 2016 oil supply glut and COVID-19 pandemic. US stocks are the highest transmitter of uncertainty to all other assets, followed by the US dollar and oil. US stocks (US dollar and oil) transmit uncertainty in long (short) term. Furthermore, US macro uncertainty is the net transmitter of uncertainty to the US stocks, industrial metals and oil markets. In contrast, US financial stress is the net transmitter of uncertainty to the US bonds, cryptocurrencies, the US dollar and gold markets. US financial stress (US macro uncertainty) has long (short)-term effects on asset price volatility. Originality/value This study complements the studies on volatility spillover among the important asset classes. This study also includes recently financialized asset classes such as cryptocurrencies, agricultural and industrial commodities. This study examines the macro uncertainty and financial stress spillover on these assets.

Sino-US Economic Policy Uncertainty and Spillover Effect of Asset Price Volatility in China

By constructing a TVP-VAR-DY model, this paper investigates the dynamic spillover effect of Sino-US economic policy uncertainty on China's asset price volatility. The study finds that Sino-US EPU is the net exporter of China's asset price volatility spillover effect, and the net spillover effect of Sino-US EPU on China's asset prices volatility deviates from the EPU index trends of the respective countries. EPUs in China and the United States are observed in a state of mutual spillover. The network structure of the net spillover effect of asset price volatility between China and the United States shows a dynamic evolutional trend, and the spillover effect of EPU index on asset price volatility in China is heterogeneous in different periods and different levels.

A classical model of speculative asset price dynamics

In retrospect, the experimental findings on competitive market behavior called for a revival of the old, classical, view of competition as a collective higgling and bargaining process (as opposed to price-taking behaviors) founded on reservation prices (in place of the utility function). In this paper, we specialize the classical methodology to deal with speculation, an important impediment to price stability. The model involves typical features of a field or lab asset market setup and lends itself to an experimental test of its specific predictions; here we use the model to explain three general stylized facts, well established both empirically and experimentally: the excess, fat-tailed, and clustered volatility of speculative asset prices. The fat tails emerge in the model from the amplifying nature of speculation, leading to a random-coefficient autoregressive return process (and power-law tails); the volatility clustering is due to the traders’ long memory of news; bubbles are a persistent phenomenon in the model, and, assuming the standard lab present value pattern, the bubble size increases with the proportion of speculators and decreases with the trading horizon.

Stock markets and economic uncertainty: Roles of legislative sessions and coalition strength

Legislative sessions constitute a critical component of the Westminster Parliamentary system, in a country like India. They play a crucial role in important policy decisions that have ramifications for the corporate sector's cash flows, leading to changes in stock price and volatility. Using data from India, we find that legislative session meetings influence stock price volatility, and the effect is inversely proportional to the strength of the ruling coalition government. These findings advance the theories of intraparty portfolio allocation and partisan trust discrimination in representative democracies based on intraparty and interparty competition in a ruling alliance. Our findings suggest that political competition reflected through government strength in legislative session meetings shapes the mechanism of economic uncertainty, i.e., the foundation of asset price volatility. We draw implications for the conduct of parliamentary business as well as stock investment strategy.