Consistent High-Precision Volatility from High-Frequency Data

Volatility forecasts crucial to many financial applications usually assume implicitly that the frequency of the data should match the forecast horizon; portfolio managers typically rely on risk models estimated using monthly data to produce monthly volatility forecasts, for example. For longer–term forecasts, this practice has two drawbacks: Volatility estimates can be based on stale data; and return events occurring within long sampling intervals are obscured, confounding estimation. Monthly volatility risk measures constructed using higher–frequency data seem to be more robust than those using low–frequency data. Microstructure effects can explain the differences in estimates.

It is often assumed that the price time series have a well-defined continuum limit and that high-frequency data bring us closer to this limit. But because of the discrete trading process, a new incoherent term need to be added to the hypothetical continuum prices. This term limits severely the amount of information that can be extracted from high-frequency time series. This chapter presents various ways to remove the incoherent term in order to obtain estimators based only on the continuous price, consistent with estimators at lower frequencies. This is essential for the volatility, which otherwise is strongly biased upward when using the naive estimators. Various high-frequency volatility estimators are presented, based either on multiple time scales or on lagged covariances. Estimators for the hypothetical underlying prices are also discussed, as well as estimators of the volatility per tick.

Dynamic Copula Models and High Frequency Data

Volatility and Covariation of Financial Assets: A High-Frequency Analysis

Effects of Hormones on Financial Risk Taking: A Research in Istanbul Stock Exchange

BackgroundThis paper examines the pattern of the volatility of the daily return of select commodity futures in India and explores the extent to which the select commodity futures satisfy the Samuelson hypothesis.MethodsOne commodity future from each group of futures is chosen for the analysis. The select commodities are potato, gold, crude oil, and mentha oil. The data are collected from MCX India over the period 2004–2012. This study uses several econometric techniques for the analysis. The GARCH model is introduced for examining the volatility of commodity futures. One of the key contributions of the paper is the use of the β term of the GARCH model to address the Samuelson hypothesis.ResultThe Samuelson hypothesis, when tested by daily returns and using standard deviation as a crude measure of volatility, is supported for gold futures only, as per the value of β (the GARCH effect). The values of the rolling standard deviation, used as a measure of the trend in the volatility of daily returns, exhibits a decreasing volatility trend for potato futures and an increasing volatility trend for gold futures in all contract cycles. The result of the GARCH (1,1) model suggests the presence of persistent volatility and the prevalence of long memory for the select commodity futures, except potato futures.ConclusionsThe study sheds light on significant characteristics of the daily return volatility of the commodity futures under analysis. The results suggest the existence of a developed market for the gold and crude oil futures (with volatility clustering) and show that the maturity effect is only valid for the gold futures.

Measuring and modelling financial volatility is the key to derivative pricing, asset allocation and risk management. The recent availability of high-frequency data allows for refined methods in this field. In particular, more precise measures for the daily or lower frequency volatility can be obtained by summing over squared high-frequency re- turns. In turn, this so called realized volatility can be used for more accurate model evaluation and description of the dynamic and distributional structure of volatility. Moreover, non-parametric measures of systematic risk are attainable, that can straightforwardly be used to model the commonly observed time-variation in the betas. The discussion of these new measures and methods is accompanied by an empirical illustration using high-frequency data of the IBM incorporation and of the DJIA index.

This paper studied the impact of inflation rates and US Dollar exchange rates in Indonesian stock market return volatility in the period of 2002-2012. Daily data of stock market return, inflation rates and US Dollar exchange rates were used to estimate the daily volatility of the stock return. The data of stock market price, inflation rates and US Dollar exchange rates were taken from Yahoo Finance, Indonesia statistics bureau and central bank of Indonesia, respectively. The result showed that both inflation rates and US Dollar exchange rates did not have a significant impact on the Indonesian stock market return volatility. Inflation rates contributed 0.03% change in daily stock market return volatility whereas a 1% appreciation of Rupiah contributed to a 0.0000001% change in daily stock market return volatility. This research finding was different from the result in Nigeria where Yaya’s & Shittu’s (2010) research had the same variables. Inflation rates and US Dollar exchange rates were to examine the impact on Nigeria stock market index return volatility.

Financial prices are often discretized—with smallest tick size of one cent, for example. Thus prices involve rounding errors. Rounding errors affect the estimation of volatility, and understanding them is critical, particularly when using high frequency data. We study the asymptotic behavior of realized volatility (RV), which is commonly used as an estimator of integrated volatility. We prove the convergence of the RV and scaled RV under varous conditions on the rounding level and the number of observations. A bias-corrected volatility estimator is proposed and an associated central limit theorem is shown. The simulation and empirical results demonstrate that the proposed method can yield substantial statistical improvement.

Properties of range-based volatility estimators

This study employs the daily data of the Stock Exchange of Thailand to test for the leverage and volatility feedback effects. The period of investigation is during January 4, 2005 to December 27, 2013, which includes the Subprime crisis period in the US that might affect the volatility of stock market return in emerging stock markets. The results from this study show that the US subprime crisis imposes a minimal positive impact on volatility. In addition, the estimations of the three parametric asymmetric volatility models give the results showing some evidence of the volatility feedback and leverage effects. The findings give implications for portfolio diversification and risk management.

Dielectric and mechanical properties of hypersonic radome materials and metamaterial design: A review

Market microstructure theory highlights two important empirical predictions about how financial asset prices evolve over time, which are: (1) trades convey information that contributes to security price movements; and (2) intensified trading activity increases price volatility. This thesis provides three essays that develop new empirical models for high frequency returns, volatility, trading volumes and trade durations in order to test these two empirical predictions. Our newly proposed models show strong support for these theoretically implied predictions and provide additional insights into the interdependence amongst returns, volatility, volumes, and trade durations.

This study provides a rigorous examination of historical stock price volatility. We measure changes in the variability of equity returns on a daily and monthly basis and test whether volatility has changed over the period 1926 through 2014. Our results are of importance to practitioners, individual investors, policy makers, and the press and address the widely held perception among market participants and the general public that stock market volatility has increased over time. Our results suggest this belief is accurate, but only when volatility is measured on a daily basis. When measured using monthly increments, there has been no discernable change in return volatility. This is likely good news for both traders and investors. Traders are active on a daily basis and can profit from larger movements in the market, thus the slight increase in daily volatility is likely both important to them and viewed positively. Long-term investors can take comfort that many of the large daily price declines are at least partially offset by similar price increases and that, when measured over longer periods, volatility has not increased.

The paper presents theoretical and methodological approaches to forecasting pricing in the real estate market based on the study of stochastic models of time series, which are statistical samples of prices of real estate in certain segments of this market generated consistently over time. The main stages of the research of time series in the real estate market are given. Two main components of time series of real estate prices are distinguished - fundamental (regular) and random (irregular). The fundamental (regular) component of the time series contains trend, cyclical and seasonal trends. The random component is noise, which slightly deviates the value of the time series from the trend. The work analyzed the fundamental price-forming factors in the real estate market, which have the greatest influence on the formation of the real estate price and determine its trend. The results of the analysis of time series of prices on the secondary real estate market in the traditionally most active business cities of Ukraine (Kyiv, Kharkiv, Dnipro, Odesa, Lviv, Ivano-Frankivsk) in the period 2016-2023 are presented. It is shown that prices on regional real estate markets are formed under the influence of both general trends throughout the country and random crisis phenomena inherent in this region. In the regional section of the analyzed real estate markets, on average, a growing trend is observed, which is stimulated by the increasing solvent demand for residential square meters. At the same time, it is of particular interest to study the behavior of real estate market prices during periods of crises caused by extraordinary events and conditions (pandemic and war) observed in 2020, 2022 and in the current year 2023. If the coronavirus, as a crisis phenomenon, affected the dynamics of regional real estate markets more or less equally, then the impact of a full-scale war manifested itself differently in different regions of our country. In front-line cities that suffer quite a lot from shelling (for example, Kharkiv), the real estate market reacted by lowering prices and demand. At the same time, in the western regions (for example, Lviv, Ivano-Frankivsk), the cost of square meters has increased significantly, which is due to a sharp increase in demand due to temporarily displaced and evacuated persons. Further directions of time series research for forecasting pricing in the real estate market have been determined.