Asset Price Volatility Research Articles

Does Online Investor Sentiment Affect the Asset Price Movement? Evidence from the Chinese Stock Market

With the quick development of the Internet, online platforms that provide financial news and opinions have attracted more and more attention from investors. The question whether investor sentiment expressed on the Internet platforms has an impact on asset return has not been fully addressed. To this end, this paper uses the Baidu Searching Index as the agent variable to detect the effect of online investor sentiment on the asset price movement in the Chinese stock market. The empirical study shows that although there is a cointegration relationship between online investor sentiment and asset return, the sentiment has a poor ability to predict the price, return, and volatility of asset price. Meanwhile, the structural break points of online investor sentiment do not lead to changes in the asset price movement. Based on the empirical mode decomposition of online investor sentiment, we find that high frequency components of online investor sentiment can be used to predict the asset price movement. Thus, the obtained results could be useful for risk supervision and asset portfolio management.

The impact of regime-switching behaviour of price volatility on efficiency of the US sovereign debt market

This article focuses on the asset price volatility at the stock exchange that result from the regime switching behaviour in the market. This study is devoted to the question about how the asset price volatility affects the US sovereign debt market. The efficient market hypothesis has been a base for the asset pricing. This hypothesis is discussed in this study. The review of the literature reveals nuances of behavioural finance theory, and allows us to better understand the regime switching behaviour in the market. The object of empirical study is the US sovereign debt market. We use the Markov Regime-Switching ARCH (SWARCH) model to analyse data. The results show that there is high volatility regime in both the 2012 and 2017 bonds US market, which significantly affects bond prices.

Capital controls and monetary policy autonomy in a small open economy

Is there a link between capital controls and monetary policy autonomy in a country with a floating currency? Shocks to capital flows into a small open economy lead to volatility in asset prices and credit supply. To lessen the impact of capital flows on financial instability, a central bank finds it optimal to use the domestic interest rate to “manage” the capital account. Capital account restrictions affect the behavior of optimal monetary policy following shocks to the foreign interest rate. Capital controls allow optimal monetary policy to focus less on the foreign interest rate and more on domestic variables.

Financial Intermediation, Leverage, and Macroeconomic Instability

This paper investigates how financial sector leverage affects macroeconomic instability and welfare. In the model, banks borrow (use leverage) to allocate resources to productive projects and pro vide liquidity. When banks do not actively issue new equity, aggregate outcomes depend on the level of equity in the financial sector. Equilibrium is inefficient because agents do not internalize how their decisions affect volatility, aggregate leverage, and the returns on assets. Leverage creates systemic risk, which increases the frequency and duration of crises. Limiting leverage decreases asset price volatility and increases expected returns, which decrease the likelihood that the financial sector is undercapitalized. (JEL E32, E44, G01, G21)

Asset Prices and Optimal Monetary Policy

We construct a New Keynesian DSGE model that features financial frictions, investment frictions, long-run productivity risk and Epstein-Zin preferences. The model successfully reproduces key features of both asset prices and macroeconomic quantities such as consumption, investment, and output. Under this set up, we examine the implications of different monetary policy rules where the central bank responds to inflation, output and asset prices in the presence of productivity shocks, monetary policy shocks and financial shocks. This paper contributes to the current debate on how central bankers ought to respond to asset price volatility, in the context of an overall strategy for monetary policy.

A Truncated Two-Scales Realized Volatility Estimator

This paper introduces a novel estimator of the integrated volatility of asset prices based on high frequency data that is consistent in the presence of price jumps and market micro-structure noise. We begin by introducing a jump signaling indicator based on a local average of intra-daily returns that allows to detect jumps when the price is contaminated by noise. We then combine this technique with the two-scales realized volatility estimator to introduce the so called truncated two-scales realized volatility estimator (TTSRV). We establish consistency of the TTSRV in the presence of finite or infinity activity jumps and noise. In case of finite activity jumps, we also establish the asymptotic distribution of the estimator. A simulation study shows that the TTSRV performs satisfactorily in finite samples and that it out-performs a number of alternative estimators recently proposed in the literature.

Asset Price Risk, Banks and Markets

This paper examines the operation of Diamond-Dybvig banks when depositors have access to the asset market. Previous studies have shown that banks are redundant in this environment since it is impossible to prevent the strategic withdrawals. This paper shows that the strategic withdrawals can be prevented if the market risk, due to asset price volatility, is considered. Banks provide deterministic returns to the depositors since the aggregate withdrawals are predictable, and therefore, banks can match the assets’ maturities and the time pattern of the aggregate withdrawals such that no asset liquidation is involved. However, an individual consumer with stochastic liquidity need is vulnerable to the price volatility if he holds the asset directly. Therefore, banks improve the consumers’ welfare by providing the insurance against not only the liquidity shock but also the market risk. Banks are not redundant.

The Boats That Did Not Sail: Asset Price Volatility in a Natural Experiment

ABSTRACTWhat explains short‐term fluctuations of stock prices? This paper exploits a natural experiment from the 18 century in which information flows were regularly interrupted for exogenous reasons. English shares were traded on the Amsterdam exchange and news came in on sailboats that were often delayed because of adverse weather conditions. The paper documents that prices responded strongly to boat arrivals, but there was considerable volatility in the absence of news. The evidence suggests that this was largely the result of the revelation of (long‐lived) private information and the (transitory) impact of uninformed liquidity trades on intermediaries' risk premia.

Intraday industry-specific spillover effect in European equity markets

This paper investigates the existence of financial contagion between the US and 10 European stock markets. Using intraday minute-per-minute data of a large set of 374 equities from three different industries, over the period from January to June 2011, we investigate the impact of increased volatility in the US on the inter-country industry-level spillover effect. Self-built industry indices are used, which allows the implementation of the same index methodology across different markets. We first show that the spillover of asset price volatility from the US to European markets does exist; the greatest spike in the volatility in the target markets is observed in the first minute, and is absorbed in the first 5 min after the volatility increase. Second, we can state that euro-denominated markets amplify the spillover effect of volatility from the US market. Third, we provide evidence of the industry heterogeneity of the spillover effects, and claim that an analysis of financial contagion across different industries is desirable, using industry indices instead of global market indices.

A Financial Market Model Incorporating Herd Behaviour.

Herd behaviour in financial markets is a recurring phenomenon that exacerbates asset price volatility, and is considered a possible contributor to market fragility. While numerous studies investigate herd behaviour in financial markets, it is often considered without reference to the pricing of financial instruments or other market dynamics. Here, a trader interaction model based upon informational cascades in the presence of information thresholds is used to construct a new model of asset price returns that allows for both quiescent and herd-like regimes. Agent interaction is modelled using a stochastic pulse-coupled network, parametrised by information thresholds and a network coupling probability. Agents may possess either one or two information thresholds that, in each case, determine the number of distinct states an agent may occupy before trading takes place. In the case where agents possess two thresholds (labelled as the finite state-space model, corresponding to agents’ accumulating information over a bounded state-space), and where coupling strength is maximal, an asymptotic expression for the cascade-size probability is derived and shown to follow a power law when a critical value of network coupling probability is attained. For a range of model parameters, a mixture of negative binomial distributions is used to approximate the cascade-size distribution. This approximation is subsequently used to express the volatility of model price returns in terms of the model parameter which controls the network coupling probability. In the case where agents possess a single pulse-coupling threshold (labelled as the semi-infinite state-space model corresponding to agents’ accumulating information over an unbounded state-space), numerical evidence is presented that demonstrates volatility clustering and long-memory patterns in the volatility of asset returns. Finally, output from the model is compared to both the distribution of historical stock returns and the market price of an equity index option.

Macro-Prudential Response to Increased Global Market Volatility

Volatilities of price indicators have remained extremely stabilized during the period of low interest rates since the Global Financial Crisis (GFC) of September 2009. Low volatility pushes down risk premium. That could lead global investors’ risk appetite to increase. There has been a big change in global liquidity flows since 2009. Emerging market economies (EMEs), with relatively high credit risk, received a huge capital inflows backed up by the increased risk appetite of global investors.US Federal Reserve is now trying to normalize its monetary policy by increasing the policy rate tied at zero low bound for about seven years. This will bring asset price volatility and risk premium to normalize too. We remain concerned about the downside risk to the capital outflows from EMEs, including Korea. And it may well potentially cause a decrease in asset price and a growth contraction in EMEs. Accordingly, we overview volatility of financial market and new trends in capital flows, and identify the determinants of capital flows to/from EMEs. We also review the use of capital flow management policies in EMEs including Korea, and examine the effectiveness of Asset? Based Reserve Requirements (ABRR) as an alternative macro-prudential policy measure to manage capital flows.

Semi-Parametric Restrictions on Production-Based Asset Pricing Models

Matching asset price volatility in production economies is difficult. This paper shows that this difficulty can be summarized by three nested restrictions. First, matching asset price volatility requires volatile investment returns. Second, volatile investment returns require either large capital adjustment costs or volatile investment specific technology shocks. Third, large costs or volatile shocks require a low elasticity of intertemporal substitution. I quantify these restrictions and show that they apply to a broad class of models which spans many assumptions about preferences, beliefs, market completeness or the stochastic structure of shocks.

Bringing an elementary agent-based model to the data: Estimation via GMM and an application to forecasting of asset price volatility

We explore the issue of estimating a simple agent-based model of price formation in an asset market using the approach of Alfarano et al. (2008) as an example. Since we are able to derive various moment conditions for this model, we can apply generalized method of moments (GMM) estimation. We find that we can get relatively accurate parameter estimates with an appropriate design of the GMM estimator that reduces the biases arising from strong correlations of the estimates of certain parameters. We apply our estimator to a sample of long records of returns of various stock and foreign exchange markets as well as the price of gold. Using the estimated parameters to form the best linear forecasts for future volatility we find that the behavioral model generates sensible forecasts that get close to those of a standard GARCH(1,1) model in their overall performance, and often provide useful information on top of the information incorporated in the GARCH forecasts.

Stability of utility maximization in nonequivalent markets

Stability of the utility maximization problem with random endowment and indifference prices is studied for a sequence of financial markets in an incomplete Brownian setting. Our novelty lies in the nonequivalence of markets, in which the volatility of asset prices (as well as the drift) varies. Degeneracies arise from the presence of nonequivalence. In the positive real line utility framework, a counterexample is presented showing that the expected utility maximization problem can be unstable. A positive stability result is proved for utility functions on the entire real line.

Optimal investment and consumption under partial information

We present a unified approach for partial information optimal investment and consumption problems in a non-Markovian Ito process market. The stochastic local mean rate of return and the Wiener process cannot be observed by the agent, whereas the path-dependent volatility, the path-dependent interest rate and the asset prices can be observed. The main assumption is that the asset price volatility is a nonanticipative functional of the asset price trajectory. The utility functions are general and satisfy standard conditions. First, we show that the corresponding full information market is complete and in this setting we solve the problem using standard methods. Second, we transform the original partial information problem into a corresponding full information problem using filtering theory, and show that it follows that the market is observationally complete in the sense that any contingent claim adapted to the observable filtration is replicable. Using the solutions of the full information problem we then easily derive solutions to the original partial information problem.

Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High-Frequency Trading

We investigate the effects of different regulatory policies directed towards high-frequency trading (HFT) through an agent-based model of a limit order book able to generate flash crashes as the result of the interactions between low- and high-frequency (HF) traders. We analyze the impact of the imposition of minimum resting times, of circuit breakers (both ex-post and ex-ante types), of cancellation fees and of transaction taxes on asset price volatility and on the occurrence and duration of flash crashes. In the model, low- frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. In contrast, high-frequency traders activation is event-driven and depends on price fluctuations. In addition, high-frequency traders employ low-latency directional strategies that exploit market information and they can cancel their orders depending on expected profits. Monte-Carlo simulations reveal that reducing HF order cancellation, via minimum resting times or cancellation fees, or discouraging HFT via financial transaction taxes, reduces market volatility and the frequency of flash crashes. However, these policies also imply a longer duration of flash crashes. Furthermore, the introduction of an ex-ante circuit breaker markedly reduces price volatility and removes flash crashes. In contrast, ex-post circuit breakers do not affect market volatility and they increase the duration of flash crashes. Our results show that HFT-targeted policies face a trade-off between market stability and resilience. Policies that reduce volatility and the incidence of flash crashes also imply a reduced ability of the market to quickly recover from a crash. The dual role of HFT, as both a cause of the flash crash and a fundamental actor in the post-crash recovery underlies the above trade-off.

A Supply-Lock Competitive Market for Investable Products

We propose a new market structure, a supply-lock competition (SLC), for analyzing transactions of investable goods. This study explains and analyzes how the SLC is applicable for transactions of stock, art, real estate, currency, and scarce goods. We suggest that the formation and fluctuation of the market price for these investable assets are not only related to their intrinsic values or investors? behavior, but are also derived from supply restriction. A mechanism of the SLC market structure benefits market efficiency but also continues volatility of asset prices in secondary markets as well. We demonstrate that a demand orientation dominates the valuation of investable assets, resulting in a divergence of market price and intrinsic value.

Indeterminacy in stochastic overlapping generations models: real effects in the long run

Indeterminate equilibria are known to exist for overlapping generations models, though recent research has been limited to deterministic settings in which all equilibria converge to a steady state in the long run. This paper analyzes stochastic overlapping generations models with three-period-lived representative consumers and adopts a novel computational algorithm to numerically approximate the entire set of competitive equilibria. In a stochastic setting with incomplete markets, indeterminacy has real effects in the long run. Our numerical simulations reveal that indeterminacy is an order of magnitude more important than endowment shocks in explaining long-run consumption and asset price volatility.

Endogenous Uncertainty and Credit Crunches

We develop a theory of endogenous uncertainty where the ability of investors to learn about firm-level fundamentals declines during financial crises. At the same time, higher uncertainty reinforces financial distress of firms, giving rise to “belief traps” — a persistent cycle of uncertainty, pessimistic expectations, and financial constraints, through which a temporary shortage of funds can develop into a long-lasting funding problem for firms. At the macro-level, belief traps provide a rationale for the long-lasting recessions that typically entail financial crises. In our model, financial crises are characterized by high levels of credit misallocation, an increased cross-sectional dispersion of growth rates, endogenously increased pessimism, uncertainty and disagreement among investors, highly volatile asset prices, and high risk premia. A calibration of our model to U.S. micro data on investor beliefs matches the slow recovery after the 08/09 crisis remarkably well.

A nonparametric kernel regression approach for pricing options on stock market index

ABSTRACTPrevious options studies typically assume that the dynamics of the underlying asset price follow a geometric Brownian motion (GBM) when pricing options on stocks, stock indices, currencies or futures. However, there is mounting empirical evidence that the volatility of asset price or return is far from constant. This article, in contrast to studies that use parametric approach for option pricing, employs nonparametric kernel regression to deal with changing volatility and, accordingly, prices options on stock index. Specifically, we first estimate nonparametrically the volatility of asset return in the GBM based on the Nadaraya–Watson (N–W) kernel estimator. Then, based on the N–W estimates for the volatility, we use Monte Carlo simulation to compute option prices under different settings. Finally, we compare the index option prices under our nonparametric model with those under the Black–Scholes model and the Stein–Stein model.