Excess volatility pursuit in autoregressive GARCH model based panel data analysis at country level: BRICS context

PurposeThis particular study examined the government bond price variations in order to determine the presence of excess volatility both at country and panel group level of BRICS countries context.Design/methodology/approachThe study applied the autoregressive GARCH panel model approach proposed by Fakhry and Richter (2015) to evaluate the presence of excess volatility and then examined the diversification benefits. Further, the use of discrete wavelet transformation (DWT) has added the advantage to observe volatility across bonds along with potential diversification benefits by retaining information from the time and frequency domain perspective for both the maturities.FindingsThe main finding indicates that the excess volatility is present in BRICS countries at individual level i.e. in the case of Russia, India and China. However, the 10-year bond showing a less volatility compared to 5-year bond with the possibility of reaping out the benefits of diversification with international portfolio of sovereign bonds.Practical implicationsThe main implication of the research is related to the non-perseverance of EMH as far sovereign bonds of BRICS countries are concerned as the results indicate presence of excess volatility in the 5-year and 10-year bond markets. However, the implicit behavior of 5-year bond could benefit the active fund managers and investors by taking an advantage of a reducing systemic risk through short-medium term investments.Originality/valueThis study contributes not only to the existing studies of similar nature by examining the excess volatility in bond markets but also taking account of co-moment of distinct maturities to confirm possible international diversification benefits for BRICS countries context.

On the Intraday Behavior of Bitcoin

We analyze the intraday time series of Bitcoin, comparing its features with those of traditional financial assets such as stocks and exchange rates. The results shed light on similarities as well as significant deviations from the standard patterns. In particular, our most interesting finding is the unusual presence of significant negative first-order autocorrelation of returns calculated on medium-frequency timeframes, such as one, two and four hours, signaling the presence of systematic mean reversion. It is also found that larger price movements lead to stronger reversals, in percentage terms. We finally point out the potential exploitability of the phenomenon by implementing a basic algorithmic trading strategy and retroactively applying it to the data. We explain the findings mainly through (i) investor and trader overreaction, (ii) excess volatility and (iii) cascading liquidations due to excessive use of leverage by market participants.

Idiosyncratic volatility and interruption mechanisms in South Korean stock markets

PurposeThe purpose of this study is to examine how the volatility interruption (VI) mechanisms affect idiosyncratic volatilities in Korean stock markets.Design/methodology/approachCollecting the South Korea Stock Market (KOSPI) data from June 15, 2015 to March 31, 2019, we collect each residual, εi,t, from three different estimated models: capital asset pricing model (CAPM), FF3 and FF5. To estimate the conditional idiosyncratic volatility, the authors employ two conditional time-varying measurements: GARCH and TGARCH.FindingsThe results show that the conditional idiosyncratic volatility increases when stock prices reach the upper and lower static limits, indicating the implementation of adopting static VI mechanism neither stabilize market conditions nor reduce excess volatility along with the existence of price limits.Originality/valueAlthough market regulators and policymakers improve market conditions with the advanced VI mechanism, the empirical results show the adverse effect of the mechanism. Not allowing investors to earn above average returns without accepting above average risks makes Korean stock markets inefficient along with advanced VI mechanisms.

Excess Volatility in Bitcoin: Extreme Value Volatility Estimation

This article investigates the excess volatility in Bitcoin prices using an unbiased extreme value volatility estimator. We capture the time-varying nature of the excess volatility using bootstrap, multi-horizon, sub-sampling and rolling-window approaches. We observe that Bitcoin price changes are almost efficient. Although Bitcoin prices exhibit high volatility and show signs of excess volatility for a few periods, it is decreasing over time. After controlling for the outliers, we also notice that the Bitcoin market shows signs of increasing maturity. Overall, Bitcoin prices show a sign of increasing efficiency with decreasing volatility. Our findings have implications for investors making investment decisions and for regulators making policy choices.

Stock Characteristics and Individual Herding

Individuals who invest stocks in a market with excess volatility generally end up selling or holding the stocks at losses. The purpose of this study was to examine individual herding as it related to three comprehensible stock characteristics, market capitalization, price-to-book ratio, and industry affiliation. The target population was the individual investors who traded in Taiwan Stock Exchange in 2016. Data were collected through subscription. Based on Lakonishok, Shleifer, and Vishny's measure, individual herding was significant. The three stock characteristics were separately and as a whole related to individual herding. The findings confirmed sell-herding higher than buy-herding, more serious herding in high market capitalization stocks, and broad industry herding. The findings also extended knowledge to comparable herding levels with 8 to 10 years ago, more linearity between log market capitalization and log odds of herd occurrence, and less herding in P/B ratio stocks with other independent variables controlled.

ENDOGENOUS PARTICIPATION, RISK, AND LEARNING IN THE STOCK MARKET

A simple asset pricing model with both endogenous stock market participation and subjective risk can explain the negative cross-country correlation between participation rates and the volatility of excess returns, along with the time-varying participation rates in the data. Belief-driven learning dynamics can explain the interplay between participation rates, subjective risk, and stock price volatility. When agents adaptively learn about the risk and return, my model generates 25% of the excess volatility observed in US stock prices, while also matching key moments. With learning about risk, excess volatility of stock prices is driven by fluctuations in the participation rate that arise because agents’ risk estimates vary with prices. I find that learning about risk is quantitatively more important than learning about returns.