AN INTRODUCTION TO STOCK MARKET VOLATILITY

An Introduction of Stock market Volatility — Dr.Debesh Bhowmik

Volatility is basically a function of uncertainty.-say’s John Bollinger. Volatility can either be measured by using the standard deviation or variance between returns from that same security or market index. Commonly, the higher the volatility, the riskier the security. In other words, volatility refers to the amount of uncertainty or risk about the size of changes in a securitys value. A higher volatility means that a securitys value can potentially be spread out over a larger range of values. One measure of the relative volatility of a particular stock to the market is its beta. A beta approximates the overall volatility of a securitys returns against the returns of a relevant benchmark (usually the S&P 500 is used). For example, a stock with a beta value of 1.1 has historically moved 110% for every 100% move in the benchmark, based on price level. Conversely, a stock with a beta of .9 has historically moved 90% for every 100% move in the underlying index. Volatility is measured by the Chicago Board of Options Exchange (CBOE), primarily through the CBOE Volatility Index (VIX) and, to a lesser extent, the CBOE Nasdaq Volatility Index (VXN) for technology stocks. Seasoned traders who monitor the markets closely usually buy stocks and index options when the VIX is high. When the VIX is low, it usually indicates that investors believe the market will head higher. For starters, remember that success in the market does not depend on predicting the future—predictions only measure the short term. Volatility is more dependent on mass hysteria—fear and greed—than on underlying economic or financial events.

Volatility is a measure of dispersion around the mean or average return of a security. One way to measure volatility is by using the standard deviation. which tells you how tightly the price of a stock is grouped around the mean or moving average (MA). When the prices are tightly bunched together, the standard deviation is small. When the price is spread apart, you have a relatively large standard deviation. For securities. the higher the standard deviation, the greater the dispersion of returns and the higher the risk associated with the investment. As described by modern portfolio theory (MPT), volatility creates risk that is associated with the degree of dispersion of returns around the average. In other words, the greater the chance of a lower-than-expected return, the riskier the investment. There is a strong relationship between volatility and market performance. Volatility tends to decline as the stock market rises and increase as the stock market falls. When volatility increases, risk increases and returns decrease. Risk is represented by the dispersion of returns around the mean. The greater the dispersion of returns around the mean, the larger the drop in the compound return .

Whats causing the second-to-second, minute-to-minute, hour-to-hour, day-to-day mood swings? A confluence of fears and events, it turns out. Here are seven reasons for the wild ride:[1] Fear factor,[2] Double-dip worries,[3] Europe uncertainty,[4] Lack of political leadership,[5] Policy makers out of bullets,[6] Computer trades are destabilizing,[7] Forced selling.

In a 2011 report, Crestmont Research examined the historical relationship between stock market performance and the volatility of the market. For this analysis, Crestmont used the average range for each day to measure the volatility of the Standard & Poors 500 Index (S&P 500) index. Their research tells us that higher volatility corresponds to a higher probability of a declining market. Lower volatility corresponds to a higher probability of a rising market. Region and country economic factors, such as tax and interest rate policy, contribute to the directional change of the market and thus volatility. For example, in many countries, the central bank sets the short-term interest rates for overnight borrowing by banks. When they change the overnight rate. it can cause stock markets to react, sometimes violently.

Changes in inflation trends influence the long-term stock market trends and volatility. Expanding price-earning ratios (P/E ratio) tend to correspond to economic periods when inflation is either falling or is low and stable. This is when markets experience low volatility as they trend higher. On the other hand, periods of falling P/E ratios tend to relate to rising or higher inflation periods when prices are more unstable. This tends to cause the stock markets to decline and experience higher volatility. Industry and sector factors can also cause increased stock market volatility. One way is to use the CBOE Volatility Index (VIX). The VIX measures the implied volatility (IV) in the prices of a basket of put and call options on the S&P 500 Index. The VIX is used as a tool to measure investor risk. A high reading on the VIX marks periods of higher stock market volatility. This high volatility also aligns with stock market bottoms. Low readings on the VIX mark periods of lower volatility. As a general trend, when the VIX rises the S&P 500 drops. When the VIX is at a high, the S&P 500 is at a low, which may be a good time to buy. The higher level of volatility that comes with bear markets has a direct impact on portfolios. It also adds to the level of concern and worry on the part of investors as they watch the value of their portfolios move more violently and decrease in value. This causes irrational responses which can increase investors losses. Historically, the volatility of the stock market is roughly 20% a year and 5.8% a month, but volatility keeps on changing, so we go through periods of high volatility and low volatility.

In 1029 and 1930 ,it caused the volatility to run really high, and it continued high, even into World War II until it was clear we were going to win that war.After World War II, the volatility dramatically declined and stayed long-term stable for decades. We got pickups in volatility after market drops, like in 1973–1974. Then at the turn of the century we had the tech bubble collapse and 9/11, which led to a burst of volatility, but it dampened back down until there was a big increase in volatility with the financial crisis. The VIX Index is the most common measure of market volatility. It uses the price of options to estimate the implied volatility.

Weve gone through some periods where that VIX Index got to almost record levels, especially after the financial crisis. But it mean-reverts. We didnt have a VIX Index in the 1920s and 1930s and early 1940s, but the volatility in that period was more extreme, sustained, and longer-lived than we get nowadays. The volatility of stocks has generally gone down over time. In the current situation, its been particularly frustrating for politicians and those who run economies to see that the stock markets did recover but the labor markets, with a much stickier structure, have not. As investors get interested in a stock, trading volume, volatility, and prices rise, but stocks that are already volatile and very liquid actually have the worst returns.

Using trading data from 1990 to 2011,the visuals are designed from S&P 500 index option data replicating the implied volatility wave (or variance swap curve) extending to an expiration of one year. The front of the volatility wave contains the same data used to calculate the CBOE VIX index. The movement of this wave demonstrates changing trader expectations of the future stock market volatility. As the wave moves through time the expected (or implied) volatility surface transforms into a realized volatility surface derived from historical S&P 500 index movement.

The worry is that if interest rates now increase too much, this circle will become a vicious onehigher interest rates will lead to money flowing back to the US from emerging markets, consumption in the US will decline, world growth will slow, and stock markets across the world will decline, with emerging markets being particularly hard hit.

Using the daily returns of the indices of US (S&P 500) and the Indian stock markets (CNX S&P Nifty), C.A. Yoonus, Research Scholar, Institute for Financial Management and Research (IFMR), Chennai,examines the impact of the global financial crisis on the level of financial integration between the US and Indian stock markets from March 2005 to November 2010. The article also analyses the existence of cointegration and dynamic relationship between the two indices during the pre-crisis, crisis and post-crisis periods, and in the last five years, using the Johansen Cointegration analysis and the Vector Auto Regression (VAR) Model.

The study of Prashant Joshi, I-Shou University, Taiwan examines the return and volatility spillover among Asian stock markets in India, Hong Kong, Japan, China, Jakarta, and Korea using a six-variable asymmetric generalized autoregressive conditional heteroscedasticity. The magnitude of volatility linkages is low indicating weak integration of Asian stock markets. The study finds that own volatility spillover is higher than cross-market spillover. The overall persistence of stock market volatility is highest for Japan (0.931) and lowest for China (0.824).Using high-frequency intraday data, Jon Wongswan finds a large and significant association between developed-economy macroeconomic announcements and emerging-economy equity volatility and trading volume at short time horizons.