Intraday Volatility Research Articles

Macroeconomic news and intraday seasonal volatility in the cryptocurrency markets

ABSTRACT We examine the effects of US, German, and Japanese macroeconomic news surprises and monetary policy decisions on the intraday cyclical volatility of Bitcoin and Ethereum markets. We first document intraday seasonality specific to each day of the week and show that these patterns exhibit a slightly different volatility compared to all-day seasonality. Second, the US monetary policy news and macroeconomic surprises generate the largest effect on the seasonal volatility. Third, Ethereum seasonality is more sensitive to macroeconomic fundamentals compared to Bitcoin. These results suggest that to improve cryptocurrency pricing, portfolio management, and risk management practices associated with cryptocurrency transactions, investors should consider the interactions between day of the week effects, intraday seasonality patterns and the macroeconomics news releases.

COVID-induced sentiment and the intraday volatility spillovers between energy and other ETFs

Did Covid19 induce market turmoil impact the intraday volatility spillovers between energy and other ETFs?. To examine this, we first estimate the realized volatility of ETFs using the 5-min high-frequency data. Next, we employ time-varying parameter vector autoregressions (TVP-VAR). Finally, we utilize the wavelet coherence measure to test the time-frequency impact of COVID-induced sentiment on the spillovers by employing investors' psychological and behavioural factors. We find that oil and stock markets are net transmitters while currency, bonds, and silver markets are net receivers. The wavelet analysis embarked significant impact of media coverage and fake news index towards shaping investors' pessimism for their investments. We proposed useful implications for policymakers, governments, investors, and portfolio managers.

Trading around the clock: Revisit volatility spillover between crude oil and equity markets in different trading sessions

AbstractWe investigate the volatility spillover between the crude oil (West Texas Intermediate) and G7 countries' equity markets with high‐frequency data. Considering the trading period difference among different countries, the volatility spillover of oil market intraday and overnight sessions is studied with G7 countries, respectively. The empirical findings suggest that the US stock market dominates intraday volatility spillover to the oil market, while the European and Asian stock markets dominate overnight spillovers. The asymmetric spillover result shows that stock markets mainly spillover bad volatility to the oil market in the intraday trading session, and spillover good volatility to the oil market in the overnight trading session. Our results provide empirical evidence that crude oil market overnight trading information is also important for understanding information diffusion and volatility forecasting.

Europe's gone “right” – A comparative study of stock market reactions to populist success in Sweden and Italy

We analyze the reactions of national equity markets to the election of far-right populist governments in Italy and Sweden in September 2022. We apply event study methodology to samples of 285 Swedish and 144 Italian stocks. Share prices of Italian stocks largely aligned in the week before the vote. Conversely, the Swedish electoral outcome hit markets by surprise. Share prices adjusted in the days following the vote. We use firm-level increases in intraday volatility before the vote to estimate sensitivities to electoral outcomes. Dollar-neutral long-minus-short strategies based on these sensitivities prove explanatory power of historical intraday volatility in Sweden, where post-event reactions and uncertainty were evident. The model identified those stocks that are most sensitive to the election outcome.

Volatility Forecasting with Machine Learning and Intraday Commonality

Abstract We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting 1-day-ahead RVs using past intraday RVs as predictors, and highlight interesting time-of-day effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs.

Intraday Seasonality and Volatility Pattern: An Explanation with Recurrence Quantification Analysis

The Recurrence Quantification Analysis (RQA), a pattern recognition-based time series analysis method, can be successfully utilized for short, nonstationary, nonlinear, and chaotic time series. These RQA measures quantify several properties of time series, including predictability, regularity, stability, randomness, and complexity. In this regard, first, we analyzed the intraday seasonality with RQA and demonstrated how RQA measures change among the intraday periods by using 160 million row matched orders of 100 shares from Borsa Istanbul Equity Market between 2019M10 and 2020M02. We selected 50 stocks from the BIST50 Index group and 50 stocks from outside of the BIST100 Index group. Since these two share groups exhibit similar intraday RQA seasonality, our results are robust. Second, we explained intraday volatility with RQA measures and found a relationship between RQA measures and intraday volatility using a regression model.

Intraday Periodic Volatility Curves

The volatility of financial asset returns displays pronounced variation over the trading day. Our goal is nonparametric inference for the average intraday volatility pattern, viewed as a function of time-of-day. The functional inference is based on a long span of high-frequency return data. Our setup allows for general forms of volatility dynamics, including time-variation in the intraday pattern. The estimation is based on forming local volatility estimates from the high-frequency returns over overlapping blocks of asymptotically shrinking size, and then averaging these estimates across days in the sample. The block-based estimation of volatility renders the error in the estimation due to the martingale return innovation asymptotically negligible. As a result, the centered and scaled calendar volatility effect estimator converges to a Gaussian process determined by the empirical process error associated with estimating average volatility across the trading day. Feasible inference is obtained by consistently estimating the limiting covariance operator. Simulation results corroborate our theoretical findings. In an application to S&P 500 futures data, we find evidence for a shift in the intraday volatility pattern over time, including a more pronounced role for volatility outside U.S. trading hours in the latter part of the sample. Supplementary materials for this article are available online.

A Study of Bitcoin-Based Intraday Volatility Forecasting for Cross-Market Spreads

ABSTRACT This study provides a volatility estimation based on cross-market spreads by analyzing the behavior of Bitcoin cross-market arbitrageurs. This study crawls real-time price data from different exchanges for empirical analysis and verifies the accuracy and validity of the method employed by comparing it with the existing mainstream methods. The following conclusions are drawn: 1) The more exchanges that can be utilized, the smaller the Bitcoin price volatility, and the larger the cross-market spread, the better the estimation effect of the proposed method; and 2) Volume had no significant effect on the estimation using our method.

Adverse selection in cryptocurrency markets

AbstractIn this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverse‐selection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverse‐selection costs for major cryptocurrencies. Also, our results suggest that adverse‐selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse‐selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse‐selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.

Threats to central bank independence and exchange rate volatility: High-frequency identification with Trump’s Fed tweets

This study presents market-based evidence that threats to central bank independence increase exchange rate volatility. We use Trump’s tweets that criticize the Fed’s monetary policies and advocate lower interest rates as a proxy for threats to central bank independence. We find that intraday exchange rate volatility rises following Trump’s Fed tweets. Moreover, this relation is more pronounced for the tweets with greater market attention and more negative sentiment. Our findings suggest that market participants do not perceive the Federal Reserve as independent from political pressure.

Cryptocurrency Volatility Forecasting Using Commonality in Intraday Volatility

Cryptocurrency Volatility Forecasting Using Commonality in Intraday Volatility

The relationship between crude oil futures and exchange rates in the context of the Covid-19 shock: a tale of two markets

We examine the high-frequency intraday return and volatility transmission between crude oil futures prices and exchange rates during the 2020 Covid-19 pandemic in the context of two markets: the newly established renminbi-denominated Shanghai International Energy Exchange in China and the US-dollar-denominated Brent market in the United Kingdom. By controlling for the influence of the stock markets, our findings reveal significant disparities in return linkages, yet fairly comparable volatility transmission patterns. The International Energy Exchange shows no return linkages with exchange rates except before the shock, while Brent consistently shows return spillovers from crude oil futures prices to exchange rates. In both markets, the volatility spillovers from exchange rates to crude oil futures prices are unidirectional prior to the shock but become bidirectional as a result of the shock. Nevertheless, both the return and volatility spillover patterns in China resemble those in the United Kingdom when utilizing offshore instead of onshore exchange rates. Such similarities in return and volatility spillovers can also be observed during the 2022 Covid-19 shock that emerged in Shanghai. These findings have significant practical implications.

Modelling and Forecasting Intraday Volatility with Range Based GARCH Models for Indian Stock Market

Modelling and Forecasting Intraday Volatility with Range Based GARCH Models for Indian Stock Market

Intra-Day Volatility Mechanisms as a Consequence of the 2021-2023 Energy Crisis

Intra-Day Volatility Mechanisms as a Consequence of the 2021-2023 Energy Crisis

Change point detection for the intraday volatility using functional ARCH and conditional Copula

In this research, we are concerned with intraday volatilities computed by functional ARCH(1) (fARCH(1), for short) model for high-frequency financial time series. A conditional-Copula multiple change point detection (CPD) for intraday volatilities is proposed using fARCH(1), bivariate Gaussian Copula and t-Copula conditional distributions. We employ current available multivariate CPD models which include energy test based control chart (ETCC) and nonparametric multivariate change point model (NPMVCP) to implement the proposed CPD method for the intraday volatilities. A simulation study is conducted to demonstrate that the functional ARCH based conditional-Copula CPD for the intraday volatilities can be a useful econometrics method to detect abnormal intraday volatilities in the financial market. We analyze intraday volatilities of the Korea composite stock price index (KOSPI) and the Hyundai-Motor (HDM) company stock data with one minute high-frequency to illustrate our proposed CPD method.

Visceral emotions and Bitcoin trading

Using a new emotion-mining technique, we quantify visceral emotions among Bitcoin investors and investigate whether an empirical connection exists between micro-level visceral emotions and macro-level price dynamics in the cryptocurrency market. We find that cryptocurrency market investors carry visceral emotions. Bitcoin's intraday volatility and trading volume are associated with visceral emotions. This association is more salient for negative emotions. This form of emotional trading can cause a more behavioral group of investors to perform poorly in the cryptocurrency market.

Nonlinear autocorrelation function of functional time series

In functional time series analysis, the functional autocorrelation function (fACF) plays an important role in revealing the temporal dependence structures underlying the dynamics and identifying the lags at which substantial correlation exists. However, akin to its counterpart in the univariate case, the fACF is restricted by linear structure and can be misleading in reflecting nonlinear temporal dependence. This paper proposes a nonlinear alternative to the fACF for analyzing the temporal dependence in functional time series. We consider linear and nonlinear data generating processes: a functional autoregressive process and a functional generalized autoregressive conditional heteroskedasticity process. We demonstrate that when the process exhibits linear temporal structures, the inference obtained from our proposed nonlinear fACF is consistent with that from the fACF. When the underlying process exhibits nonlinear temporal dependence, our nonlinear fACF has a superior capability in uncovering the nonlinear structure that the fACF misleads. An empirical data analysis highlights its applications in unveiling nonlinear temporal structures in the daily curves of the intraday volatility dynamics of the foreign exchange rate.

Cryptocurrencies Intraday High-Frequency Volatility Spillover Effects Using Univariate and Multivariate GARCH Models

Over the past years, cryptocurrencies have drawn substantial attention from the media while attracting many investors. Since then, cryptocurrency prices have experienced high fluctuations. In this paper, we forecast the high-frequency 1 min volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, and Ripple, by modeling volatility to select the best model. We propose various generalized autoregressive conditional heteroscedasticity (GARCH) family models, including an sGARCH(1,1), GJR-GARCH(1,1), TGARCH(1,1), EGARCH(1,1), which we compare to a multivariate DCC-GARCH(1,1) model to forecast the intraday price volatility. We evaluate the results under the MSE and MAE loss functions. Statistical analyses demonstrate that the univariate GJR-GARCH model (1,1) shows a superior predictive accuracy at all horizons, followed closely by the TGARCH(1,1), which are the best models for modeling the volatility process on out-of-sample data and have more accurately indicated the asymmetric incidence of shocks in the cryptocurrency market. The study determines evidence of bidirectional shock transmission effects between the cryptocurrency pairs. Hence, the multivariate DCC-GARCH model can identify the cryptocurrency market’s cross-market volatility shocks and volatility transmissions. In addition, we introduce a comparison of the models using the improvement rate (IR) metric for comparing models. As a result, we compare the different forecasting models to the chosen benchmarking model to confirm the improvement trends for the model’s predictions.

The Return of the Call Auction

A call auction is a form of trading that died out in the precomputer age but has made its reentrance in electronic markets and is currently a significant component of major stock markets globally. In contrast with a continuous market in which a transaction is made any time a buy and a sell order meet or cross in price, call auctions batch orders together for execution in multilateral trades at specific points in time. The electronic call, an important innovation in market structure, has unique benefits concerning liquidity provision, intraday volatility control, and price discovery accuracy. NASDAQ introduced electronic calls to open and close its markets in 2004, and the NYSE did the same in 2007. Using data between 2010 and 2021 for these two markets, the authors assess how the opening and closing call auction volumes compare to the daily continuous trading volume and investigate the factors that impact the use of call auctions.

Intraday volatility analysis of CSI 300 index futures: a dependent functional data method

This study introduces a new volatility model based on dependent functional data to investigate the intraday volatility characteristics of CSI 300 in the context of high-frequency data. The volatility curve is fitted and reconstructed using three methods: functional principal component analysis, Newey-West kernel, and truncation-free Bartlett kernel. We adopt a functional time series approach for short-term dynamic forecasting. The empirical results show that the proposed dependent functional volatility estimation model based on the long-term covariance of the truncated Bartlett kernel can accurately capture the intraday volatility trajectory and outperforms other models in terms of forecast accuracy and profitability. This study improves the volatility-related research methodology, which is conducive to discovering the price formation mechanism of the stock index futures market and improving risk management capabilities.