Herding behaviour and volatility clustering in financial markets

We propose a financial market model in which speculators follow a linear mix of technical and fundamental trading rules to determine their orders. Volatility clustering arises in our model due to speculators’ herding behaviour. In case of heightened uncertainty, speculators observe other speculators’ actions more closely. Since speculators’ trading behaviour then becomes less heterogeneous, the market maker faces a less balanced excess demand and consequently adjusts prices more strongly. Estimating our model using the method of simulated moments reveals that it is able to explain a number of stylized facts of financial markets quite well. Various robustness checks with respect to the model setup reveal that our results are quite stable.     

Public information arrival and volatility persistence in financial markets

This paper explores the relationship between daily market volatility and the arrival of public information in four different financial markets. Public information is measured as the daily number of economic news headlines, divided in six categories of news. Statistical analysis of the news data suggests the presence of particular seasonality effects, as well as a strong degree of autocorrelation. Over the period 1994–1998, significant effects of specific news categories on the volatility of US stocks, treasury bills, bonds and dollar were detected. However, the effects – in size and duration – vary by news category and by financial market. It is demonstrated that most of the volatility persistence, as observed by GARCH models, tends to disappear when news is included in the conditional variance equation.     

Nonparametric realized volatility estimation in the international equity markets

Using high-frequency intraday data, we construct, test and model seven new realized volatility estimators for six international equity indices. We detect jumps in these estimators, construct the jump components of volatility and perform various tests on their properties. Then we use the class of heterogeneous autoregressive (HAR) models for assessing the relevant effects of jumps on volatility. Our results expand and complement the previous literature on the nonparametric realized volatility estimation in terms of volatility jumps being examined and modeled for the international equity market, using such a variety of new realized volatility estimators. The selection of realized volatility estimator greatly affects jump detection, magnitude and modeling. The properties each volatility estimator tries to incorporate affect the detection, magnitude and properties of jumps. These volatility-estimation and jump properties are also evident in jump modeling based on statistical and economic terms.     

A note on institutional hierarchy and volatility in financial markets

From a statistical point of view, the prevalence of non-Gaussian distributions in financial returns and their volatilities shows that the Central Limit Theorem (CLT) often does not apply in financial markets. In this article, we take the position that the independence assumption of the CLT is violated by herding tendencies among market participants, and investigate whether a generic probabilistic herding model can reproduce non-Gaussian statistics in systems with a large number of agents. It is well known that the presence of a herding mechanism in the model is not sufficient for non-Gaussian properties, which crucially depend on the details of the communication network among agents. The main contribution of this article is to show that certain hierarchical networks, which portray the institutional structure of fund investment, warrant non-Gaussian properties for any system size and even lead to an increase in system-wide volatility. Viewed from this perspective, the mere existence of financial institutions with socially interacting managers contributes considerably to financial volatility.     

Estimating daily volatility in financial markets utilizing intraday data

This study proposes a new approach to the estimation of daily realised volatility in financial markets from intraday data. Initially, an examination of intraday returns on S&P 500 Index Futures reveals that returns can be characterised by heteroscedasticity and time-varying autocorrelation. After reviewing a number of daily realised volatility estimators cited in the literature, it is concluded that these estimators are based upon a number of restrictive assumptions in regard to the data generating process for intraday returns. We use a weak set of assumptions about the data generating process for intraday returns, including transaction returns, given in den Haan and Levin [den Haan, W.J., Levin, A., 1996. Inferences from parametric and non-parametric covariance matrix estimation procedures, Working paper, NBER, 195.], which allows for heteroscedasticity and time-varying autocorrelation in intraday returns. These assumptions allow the VARHAC estimator to be employed in the estimation of daily realised volatility. An empirical analysis of the VARHAC daily volatility estimator employing intraday transaction returns concludes that this estimator performs well in comparison to other estimators cited in the literature.     

The degree of financial liberalization and aggregated stock-return volatility in emerging markets

In this study, we address whether the degree of financial liberalization affects the aggregated total volatility of stock returns by considering the time-varying nature of financial liberalization. We also explore channels through which the degree of financial liberalization impacts aggregated total volatility. We document a negative relation to the degree of financial liberalization after controlling for size, liquidity, country, and crisis effects, especially for small and medium-sized markets. Moreover, the degree of financial liberalization transmits its negative impact on aggregated total volatility through aggregated idiosyncratic and local volatilities. Overall, our results provide evidence in favor of the view that the broadening of the investor base due to the increasing degree of financial liberalization causes a reduction in the total volatility of stock returns.     

The war on terror and its impact on the long-term volatility of financial markets

In this article, we analyze how the U.S.' declaration of the war on terror and the subsequent invasion of Iraq has impacted long-term volatility of stock markets around the world. In doing so, we utilize two statistical techniques: wavelet-based variance analysis and a semi-parametric fractional autoregressive (SEMIFARIMA) model. Our sample comprises stock and commodity indices worldwide for the sample period January 2000-June 2006. Specifically, we consider four geographic regions: the Americas, Africa/Middle East, Europe, and Asia/Pacific. We conclude that political instability in the Middle East had its greatest impact on the volatility of financial markets around the beginning of the Iraq war, and it mostly hit developed markets (e.g., United States, United Kingdom, and Japan). Thereafter, for most sampled indices, volatility has exhibited a decreasing trend to reach eventually levels even lower than that observed at the beginning of our sample. An exception is Egypt's CMA and the Dow Jones AIG all commodities. We think that the latest political conflicts in the Middle East and their impact on the price of oil may be the most likely driving force of such volatility in those two indices. Specifically, among Egypt's main export products are petroleum and petroleum products.     

Finite sample accuracy and choice of sampling frequency in integrated volatility estimation

We consider the properties of three estimation methods for integrated volatility, i.e. realized volatility, Fourier, and wavelet estimation, when a typical sample of high-frequency data is observed. We employ several different generating mechanisms for the instantaneous volatility process, e.g. Ornstein–Uhlenbeck, long memory, and jump processes. The possibility of market microstructure contamination is also entertained using models with bid-ask bounce and price discreteness, in which case alternative estimators with theoretical justification under market microstructure noise are also examined. The estimation methods are compared in a simulation study which reveals a general robustness towards persistence or jumps in the latent stochastic volatility process. However, bid-ask bounce effects render realized volatility and especially the wavelet estimator less useful in practice, whereas the Fourier method remains useful and is superior to the other two estimators in that case. More strikingly, even compared to bias correction methods for microstructure noise, the Fourier method is superior with respect to RMSE while having only slightly higher bias. A brief empirical illustration with high-frequency GE data is also included.     

Self-oriented monetary policy,global financial markets and excess volatility of international capital flows

This paper explores the nature of macroeconomic spillovers from advanced economies to emerging market economies (EMEs) and the consequences for independent use of monetary policy in EMEs. We first empirically document that a US contractionary monetary policy shock leads a retrenchment in EME capital flows, a fall in EME GDP, and an exchange rate depreciation. We construct a theoretical model that can help to account for these findings. In the model, macroeconomic spillovers may be exacerbated by financial frictions. Absent financial frictions, international spillovers are minor, and an inflation targeting rule represents an effective policy for the EME. With frictions in financial intermediation, however, spillovers are substantially magnified, and an inflation targeting rule has little advantage over an exchange rate peg. However, an optimal monetary policy markedly improves on the performance of naive inflation targeting or an exchange rate peg. Furthermore, optimal policies don't need to be coordinated across countries. A non-cooperative, self-oriented optimal policy gives results very similar to those of a global cooperative optimal policy.     

Economic volatility and financial markets: The case of mortgage‐backed securities

The volatility of aggregate economic activity in the United States decreased markedly in the mid‐eighties. The decrease involved several components of GDP and has been linked to a more stable economic environment, identified by smaller shocks, more effective policy, and a diverse set of innovations in technology as well as financial markets. We study one such financial innovation, and document a negative relation between the rapid growth of mortgage‐backed securities and the volatility of GDP and some of its components from the mid‐1970s to the late 1990s. We also document that this relation changed sign, from negative to positive, in the early 2000s.     

Asymmetric volatility in the foreign exchange markets

We examine the presence or absence of asymmetric volatility in the exchange rates of Australian dollar (AUD), Euro (EUR), British pound (GBP) and Japanese yen (JPY), all against US dollar. Our investigation is based on a variant of the heterogeneous autoregressive realized volatility model, using daily realized variance and return series from 1996 to 2004. We find that a depreciation against USD leads to significantly greater volatility than an appreciation for AUD and GBP, whereas the opposite is true for JPY. Relative to volatility on days following a positive one-standard-deviation return, volatility on days following a negative one-standard-deviation return is higher by 6.6% for AUD, 6.1% for GBP, and 21.2% for JPY. The realized volatility of EUR appears to be symmetric. These results are robust to the removal of jump component from realized volatility and the sub-samplings defined by structural-changes. The asymmetry in AUD, GBP and JPY appears to be embedded in the continuous component of realized volatility rather than the jump component.     

Illiquidity and volatility spillover effects in equity markets during and after the global financial crisis: An MEM approach

Even though the volatility spillover effects in global equity markets have been documented extensively, the transmission of illiquidity across national borders has not. In this paper, we propose a multiplicative error model (MEM) for the dynamics of illiquidity. We empirically study the illiquidity and volatility spillover effects in eight developed equity markets during and after the recent financial crisis. We find that equity markets are interdependent, both in terms of volatility and illiquidity. Most markets show an increase in volatility and illiquidity spillover effects during the crisis. Furthermore, we find volatility and illiquidity transmission are highly relevant. Illiquidity is a more important channel than volatility in propagating the shocks in equity markets. Our results show an overall crucial role for illiquidity in US markets in influencing other equity markets' illiquidity and volatility. These findings are of importance for policy makers as well as institutional and private investors.     

Asymmetric volatility and risk in equity markets

It appears that volatility in equity markets is asymmetric:returns and conditional volatility are negatively correlated.We provide a unified framework to simultaneously investigateasymmetric volatility at the firm and the market level and toexamine two potential explanations of the asymmetry: leverageeffects and volatility feedback. Our empirical application usesthe market portfolio and portfolios with different leverageconstructed from Nikkei 225 stocks. We reject the pure leveragemodel of Christie (1982) and find support for a volatility feedbackstory. Volatility feedback at the firm level is enhanced bystrong asymmetries in conditional covariances. Conditional betasdo not show significant asymmetries. We document the risk premiumimplications of these findings.     

Universal features of price formation in financial markets: perspectives from deep learning

Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary relation between order flow history and the direction of price moves. The universal price formation model exhibits a remarkably stable out-of-sample accuracy across a wide range of stocks and time periods. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific.

The universal model—trained on data from all stocks—outperforms asset-specific models trained on time series of any given stock. This weighs in favor of pooling together financial data from various stocks, rather than designing asset- or sector-specific models, as is currently commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations improves forecast accuracy, indicating that there is path-dependence in price dynamics.     

Procyclical volatility in Chinese stock markets

Review of Quantitative Finance and Accounting - We investigate the macroeconomic determinants of stock market volatility in China using the two-component GARCH-MIDAS model of Engle et al....     

Stock volatility predictability in bull and bear markets

The recent literature on stock return predictability suggests that it varies substantially across economic states, being strongest during bad economic times. In line with this evidence, we document that stock volatility predictability is also state dependent. In particular, in this paper, we use a large data set of high-frequency data on individual stocks and a few popular time-series volatility models to comprehensively examine how volatility forecastability varies across bull and bear states of the stock market. We find that the volatility forecast horizon is substantially longer when the market is in a bear state than when it is in a bull state. In addition, over all but the shortest horizons, the volatility forecast accuracy is higher when the market is in a bear state. This difference increases as the forecast horizon lengthens. Our study concludes that stock volatility predictability is strongest during bad economic times, proxied by bear market states.     

Diversification in financial and crypto markets

This article investigates the conditional value at risk (CVaR) of two portfolio optimiza- tion approaches containing assets from the financial and crypto markets. We first catch the conditional interdependence structure among each variable through the vine-copula-GARCH model before merging it with the Mean-CVaR model. We then optimize each portfolio and find out the optimal allocation while evaluating the precise risk. The results indicate that the D-Vine copula is more suitable for both portfolios and that, when different conditional stock indices information are being taken into consideration, the crypto-market components can act as a weak hedge/safe haven against financial market indices. Furthermore, as CVaR is found to outperform the mean-variance of Markowitz in both portfolios, both risk measures similarly show that when including cryptocurrencies in a portfolio, the S&P 500 shall not be included. Additionally, the inclusion of Ethereum in a portfolio already containing Bitcoin does not boost the return.     

Transmission of volatility between stock markets

This article investigates why, in October 1987, almost all stockmarkets fell together despite widely differing economic circumstances.We construct a model in which 'contagion' between markets occursas a result of attempts by rational agents to infer informationfrom price changes in other markets. This provides a channelthrough which a 'mistake' in one market can be transmitted toother markets. We offer supporting evidence for contagion effectsusing two different sources of data.     

Money and liquidity in financial markets

We argue that there is a connection between the interbank market for liquidity and the broader financial markets, which has its basis in demand for liquidity by banks. Tightness in the market for liquidity leads banks to engage in what we term “liquidity pull-back,” which involves selling financial assets either by banks directly or by levered investors. Empirical tests on the stock market are supportive. Tighter interbank markets are associated with relatively more volume in more liquid stocks; selling pressure, especially in more liquid stocks; and transitory negative returns. We control for market-wide uncertainty and in the process also contribute to the literature on portfolio rebalancing. Our general point is that money matters in financial markets.