GARCH With Intervention Analysis to Evaluate Short Selling Restrictions

Generalized autoregressive conditional heteroscedasticity model (henceforth GARCH model) is a successful model for modeling and forecasting the volatility of financial market. But GARCH models use the closing price data of trading days to calculate the return on assets, without considering the asset price changes during the trading day. Especially when asset prices change greatly during the trading day, the volatility estimated by GARCH model is lower than real volatility. The conditional autoregressive range model (henceforth CARR model) use the range data, which is the difference between the highest asset price and lowest asset price during the trading day to study the volatility of asset prices. WCARR model can make full use of asset price change information during the trading day. We use the daily data of China's Shanghai composite index over the period 2002 to 2012 as the sample data and employ CARR model and GARCH model to model the volatility of Shanghai stock market in this paper. The empirical results show that both WCARR model and GARCH model do a great job in fitting the volatility of stock market. We use MZ regression equation, RMSE and MAE to compare the forecasting ability of WCARR model and GARCH-t model. The empirical results show that WCARR model is better than traditional GARCH model both in in-sample forecasting and out-of-sample forecasting.

The paper studies the effects of the stock index futures on the spot market volatility of China, through GARCH model, using HS300 stock index futures and the HS300 index as the research object. The empirical results show that the HS300 stock index futures has weakened the spot market volatility in spite of its very small; In addition, the introduction of HS300 stock index futures accelerate the transmission of the spot market information, showing that stock index future develop the function of price discovery. Introduction The first stock index futures contract officially listed on exchanges in the United States, Kansas, on February 24, 1982, then it has become an important tool to avoid the risks and promote the stability development of the spot market. China financial futures exchange (CFFE) issued the HS300 index futures simulation transaction price index On October 30, 2006, then officially launched the HS300 index futures On April 16, 2010. The introduction of stock index futures not only improves the structure of China's securities market, but also has a profound impact on promoting the healthy development of China's securities market. Since then, the relation between the introduction of stock index futures market and the spot market volatility has become a controversy topic, which is the study in this paper. Literature review A variety of Scholars at home and abroad study the effects different stock index futures on the volatility of different the spot market. Bologna (2002) analyze the volatility of the Indian stock market based on the GARCH model, and the results show that the launch of stock index futures decreases the volatility of stock cash market; Li Zhuo (2007) carried on the empirical analysis in our country Taiwan area about the impact of stock index futures on the spot, which is showing that the introduction of stock index futures reduce the volatility of the spot market in Taiwan; Huang Wei, Liu Zaihua (2007) by studying the stock index futures influenced the volatility of NIFTY in Indian stock market concluded that: the introduction of NIFTY index futures effectively reduced the volatility of the Indian stock market. Model specification and data processing Model specification GARCH (p, q) model is as follows: International Conference on Economic Management and Trade Cooperation (EMTC 2014) © 2014. The authors Published by Atlantis Press 249 σ 2 t=ɑ0+ɑ1μ 2 t-1+......+ɑiμ 2 t-i+......+ɑq μ 2 t-q+β1σ 2 t-1+......+βjσ 2 t-j+......+βpσ 2 t-p. (1) Wherein p, q is a positive integer; ɑ0>0, ɑi≥0,vi=1,2,......,q; βj≥0, j=1,2,......,p; GARCH model uses four parameters to describe its volatility, which ɑ0 represents uncertainty in the original system, ɑi says the impact of new information on market volatility, and βj characterizes the impact of old information on the market volatility. In this model, the greater the value of ɑi, the better the speed and quality of information transmission, and the greater the value of βj, suggesting the influence of volatility interference factor more durable and the poorer the speed and quality of information transmission. In addition, when the articles on total sample modeling, the GARCH model is introduced a virtual variable D, which set D=0 before HS300 index futures issued and set D=1 after HS300 index futures issued. So the conditional variance equation of GARCH model, adding the virtual variables, can be written as: σ 2 t=ɑ0+ɑ1μ 2 t-1+......+ɑiμ 2 t-i+......+ɑq μ 2 t-q+β1σ 2 t-1+......+βjσ 2 t-j+......+βpσ 2 t-p+φD. (2) If φ >0, the introduction of stock index futures increases the volatility of the stock market; If φ < 0, it reduces the volatility of the stock market, and if φ = 0, the stock index futures has no effect on the volatility of the stock market. Data selection and statistical description Data selection The paper selects daily closing price of HS300 index as data, and the reason of choosing it is that it reflects most of the market behavior and more persuasive. All data come from the netease finance. In order to compare the influence on the spot market volatility before and after the stock index futures introduced, data is divided into two subintervals,which are the HS300 index of daily closing price (It)before the HS300 index futures introduced ( April 10, 2007 April 15, 2010) and after the HS300 index futures introduced (April 16, 2010-March 15,2013) . And the total of the data are 1474. In paper, R, R1, R2 are respectively used to present the return of the total samples, before the introduction of HS300 index futures, and after the introduction of HS300 index futures. In addition, the paper uses the log-return first order difference as day returns, so the calculation formula is: Rt = Ln (It) -Ln (It-1) (3) Figure 1 the HS300 index day return line chart

In March 2010, China's securities market began the trial of margin trading and ended the unilateral trading system since the operation of the stock market in China. It is an important milestone in the development of China's securities market. From 2010 to now, the scale of margin trading continues to expand, the quality of the underlying stocks is getting higher and higher, the regulation of the market is becoming more and more perfect, but how does it affect the volatility of the stock market in China? So far there is no uniform conclusion. This paper deals with the daily trading data of the Shanghai and Shenzhen 300 Index as an indicator of the volatility of the stock market, and uses the total financing balance and margin of the Shanghai and Shenzhen Stock Exchange as the margin trading index. This paper makes an empirical analysis of the impact of margin trading on the volatility of China's stock market. Samples range from March 31, 2010 to March 31, 2016, a total of 1643 trading days.

IN MY judgment," maintained William McChesney Martin, Jr., Chairman of the Board of Governors, Federal Reserve System, "we can never under the present margin regulations have the same result that occurred in terms of a financial crash in 1929 through undermargined accounts, low margins resulting in a financial debacle."' A widely used elementary economics textbook claimed, "Margin requirements have exercised an important restrictive influence on security speculation, as contrasted with the uncontrolled period of the late 1920's... Nearly everyone agrees that Federal Reserve margin requirements exercise a healthy restraint on speculative stock purchases in a boom" (Bach, 1963, p. 116). But what evidence is there that margin requirements have the effects claimed? At the December meetings of the American Economic Association, George J. Stigler, in the Presidential address, called on economists to test their assumptions that state regulation of private activity has indeed been successful (Stigler, 1965). This article brings together some data on the efficacy of margin requirements. While the figures can only be considered preliminary, and more data should be collected to test margin requirements, the data that are available and presented here indicate that not one of the aims of the legislation establishing margin requirements has been accomplished.2

In this article, we analyze the characteristics of the implicit cycle of volatility in Chinese stock market by the theory of frequency spectrum. Through searching literature, we know the fact that the study of volatility in Chinese stock market always concentrate their attention on existence of volatility and there is lack of research on the implicit cycle characteristic in the market volatility. In recent years, some scholars also study the volatility in Chinese stock market and hold that there is implicit cycle of volatility in Chinese stock market, but don't provide the statistical test about peak value. The essence of implicit cycle in volatility is the performance of the low efficient market. Therefore, in this article, we establish the periodgram analysis model, and apply the window spectrum estimate of the power spectrum to analyze the volatility of Shanghai's stock price index and Shenzhen's. We also study the existence on implicit cycle of volatility in Chinese stock market in order to determine the improvement degree about Chinese stock market's efficiency. In this article, we study the implicit cycle of volatility in Chinese stock market by the stock index. The volatility of stock market is referring to the volatility that corresponded to the stock index. The author selects the day closing quotation index of Shanghai stock exchange composite index and of Shenzhen stock exchange component index as data sample and the data sector is from January 4, 1999 to December 13, 2005, amount to 1668 trading day. We each establish the two index's day return rate's percentage sequence. The data sequence doesn't have the tendency and seasonal characteristic. We apply the SPECTRA process of the SAS software (spectral analysis process) to determine the sequence's implicit cycle and provide the statistical test about peak value. So we obtain some researches output. We hold that there does not exist the implicit cycle of volatility in Chinese stock market. From this research we know that the Chinese stock market efficiency obtains the enhancement and the volatility structure have a greater change than several year ago. We also believe that the higher volatility in Chinese stock market is may caused by the centralized and fierce new message and by the worse market absorbency in the shock of message. Both lead to the stock price's volatility

Indian stock market was functioning with Accounting Period settlement cycle till December 31, 2001. But as per the recommendation of G-30 which was a group to determine the best international practices for securities clearing and settlements, Indian stock market has adopted T+2 rolling settlement cycle on April 1, 2003. Difference of settlement and clearing cycles of different stock exchanges might lead to the day of the week anomaly. Day anomaly in return and volatility of stock market was found to be affected by settlement cycle, and it changes over the period of time. Therefore, present study tries to investigate the effect of introduction of T+2 settlement cycle on day of the week anomaly in return and volatility of Indian stock market. Study analyzes daily closing price data of Sensex and Nifty over the period January 2, 1991 to January 31, 2013 covering 5,763 samples. Study computes descriptive statistics of each week days for both subperiods as well as for complete sample period. It also applies GARCH (1, 1) model for investigating the presence of day of the week effect in both return and volatility of Indian stock market. Empirical result reveals that before introduction of T+2 settlement, returns of Monday and Thursday were negative, while after the introduction of T+2 settlement, returns of all days are positive. By applying GARCH model, this study concludes that after the introduction of T+2 settlement, day of the week effect exists only in return of Indian stock market, but it does not exist in volatility of Indian stock market.

(ProQuest: ... denotes formulae omitted.)I. IntroductionDo margin requirements play an important role in reducing stock market volatility? Many government regulators of the stock markets thought that volatility in the stock market might be controlled by some restrictions about buying on margin and short-selling. In other words, margin and short-selling can be easily used to stimulate the stock prices when the stock market is in recession, while these policies can be adopted to reduce the bubble of the stock prices when it is in boom.Initial margin requirements were firstly imposed by the US Congress with the Securities and Exchange Act of 1934 to reduce the credit-financed speculation in the stock market, which may lead to excessive price volatility through a process1 (Garbade, 1982). Therefore, initial margin requirements are designed and adopted to prevent excess volatility in the stock markets. However, the previous literatures show mixed results. For example, some studies (Kupiec, 1989; Hardouvelis, 1990, Hardouvelis and Theodossiu, 2002 among others) find the negative relationship between margin requirements and stock volatility, while the other studies (Schwert, 1989; Hsieh and Miller, 1990; Kim and Oppenheimer, 2002 among others) find no reliable evidence.Basically, the pyramiding-depyramiding process takes for granted the presence of both rational and irrational investors (speculators) and expects the negative relationship between margin requirement and stock volatility. DeLong, Shleifer, Summers and Waldman (1990) theoretically show that the amount of nonfundamental volatility in the stock market increases, when noise (destabilizing) traders lever their positions. Therefore, higher margin requirements primarily restrict the participation of irrational investors in the stock market and settle excess volatilities and mispricing. Kumar et al. (1991) refer to this as the speculative effect.Along with the speculative effect, Kumar et al. (1991) also mentioned the liquidity effect. If speculation is inherently stabilizing, then higher margin requirements restrict the activities of rational investors. That is, higher margins could potentially generate the lack of liquidity in the stock market. This lack of liquidity would cause higher volatility. This implies that when the liquidity effect prevails, there could be a positive relationship between margin requirement and stock volatility.Depending on which effect between the speculative effect and the liquidity effect is dominant, the positive or negative relationship can be observed. In other words, if irrational (rational) investors play a role in the stock market, we can observe the negative (positive) relationship. It is natural that irrational investors play an important role in the short-run than in the long-run because sufficient time is given to collect information over the long-run. Therefore, we expect that dividing volatility into long-run and short-run components gives more insights for the effect of initial margin requirements.The purpose of this paper is to examine the effect of initial margin requirements on long-run and short-run volatilities in the Japanese stock market. To consider the effect of initial margin requirement on long-run volatilities, the previous literatures (e.g., Hardouvelis, 1990; Hsieh and Miller, 1990; Hardouvelis and Theodossiu, 2002) construct longer horizon volatilities such as monthly and annual volatilities to examine the relationship between margin requirements and stock volatility. When using longer horizon volatilities, the relationship may be unreliable due to a handful of independent observations from generating long horizon volatilities. In the study of Hardouvelis (1990), he constructs the rolling 12-month estimator of volatility by implicitly assuming that volatility is nonstationary.2 Considering this problem, we adopt the component GARCH (CGARCH) model,3 proposed by Engle and Lee (1999). …

There has been much discussion among the regulators, investors and academics in policy circles to control the increasing volatility by using the price limits on financial markets. In spite of the strong existence of price limits worldwide, there is no much information regarding the effects of price limits on volatility and price discovery. Most of the previous studies find no evidence for the price limits that reduce the volatility. This study examines the effects of price limits on volatility in stock returns through testing the overreaction and information hypotheses by using the same methodology in Phylaktis et al. (1999) for Athens Stock Exchange in one of the leading emerging markets - Istanbul Stock Exchange (ISE) in the period between years 1990 and 2001 for a larger sample. More specifically, we investigate the effects of increase in price limits on volatility in ISE in the period following the structural change in July 14, 1994 since daily cumulative price limit is doubled as a result of transition from one to two sessions in a trading day by using the econometric techniques such as serial correlation and GARCH models. Our results do not support the information hypothesis in contrast to findings of Phylaktis et al. (1999). Serial correlation analysis gives us no strong evidence to reject or confirm the information hypothesis and inconclusive. Therefore, this inconclusive result motivates us to further analysis in future. GARCH estimation on daily and monthly stock returns controlling for structural breaks, financial and economic crises, trading activity and macroeconomic factors point out in a direction that volatility on stock returns has reduced despite the increase in daily price limits in the period following the structural change in ISE on 14 July, 1994. The majority of stock exhibit a negative and significant sign for the coefficient on the dummy variable. The results are robust to data frequency, leverage effect, financial crises and macroeconomic indicators. In other words, with double sessions and despite broader implicit daily price limit ranges, volatility seems to decline. Findings imply that transition from one to two sessions in a trading day with lunch-break makes positive impact in reducing the volatility in a environment where the price limit is almost doubled. It seems that the two hour lunch-break between the daily sessions has the effect of a circuit breaker, thus facilitating the dissemination of information and preventing severe overreaction to news events which is consistent to overreaction hypothesis. Finally, we find that volatility has decreased after the increase in price limits both for cross-section of stocks and overall index as well in ISE and thus price limits have no impact on volatility in stock market by the positive contribution of trading halt in the middle of the trading day.

Do climate change and geopolitical risk influence volatility? Empirical evidence from leading economies.

Chapter 5 - Evolution of Short Selling Regulations and Trading Practices

Critical study of Indian stock market relationship with domestic (DIIs) and foreign institutional investors (FIIs)

This study empirically analyzes the influence of the trade disputes between China and the United States on the stock markets of the host countries. From 2000 to 2019, using 20 years of daily return series of S&P 500 and SSE composite stock price index data, after separating in the pre- and the post-trade dispute, we dynamically analyze the volatility clustering, asymmetric volatility, etc. by using time-variable volatility models such as EGARCH and TGARCH models. The main results are as follows. Firstly, in each of the U.S. and Chinese stock markets, volatility clustering was observed during the pre and the post-trade dispute, but its intensity increased in the U.S. market and conversely decreased in the Chinese market in the post-trade dispute, suggesting that the impact of the U.S. market to Chinese market is more sensitive. Secondly, after the trade dispute, the variability and persistence of the U.S market has decreased, while it has increased in the Chinese market. Thirdly, in the post-trade dispute, asymmetric volatility in the Chinese stock market is not noted, and the degree of asymmetric volatility that existed in the pre-trade dispute is weaker than the U.S. market. It confirms that in the Chinese market, investors are not sensitive to the increase in debt ratios and the increase in risk premium caused by the fall in stock prices during the crisis. Fourthly, during the trade dispute period, the possibility of adverse asymmetric volatility (ADV) in the Chinese stock market is still present, suggesting that Chinese investors respond more sensitively to good news than bad news. Thus, the speculative tendency, which is more sensitive to positive factors than negative ones, is found to likely increase in the Chinese market in the trade dispute period. To summarize the above results, in the case of market turbulence such as trade disputes, active intervention from government is able to effectively stabilize the market. Unlike developed stock markets, Chinese investors find the possibility that the contrarian strategies are more effective than “momentum” strategies.

This study investigates the impact of the COVID-19 pandemic on the stock market in Nepal. While prior research has shown the significant impact of catastrophic events on stock market volatility, there is a lack of research specifically examining the Nepalese context. The study aims to explore the effects of the pandemic on stock market performance and volatility, with a focus on the government's response to the situation. Descriptive statistics, correlation analysis, and hypothesis testing are used to analyze the data and assess stock market volatility, return, and overall performance. The findings suggest that the short-term impact of the COVID-19 outbreak and government policy measures had a significant and adverse effect on stock market volatility, return, and overall performance. This study contributes to the understanding of the Nepalese stock market dynamics during the COVID-19 pandemic and provides insights for investors and policymakers.

This paper examines the dynamic and switching effects of volatility spillovers arising from US stock market returns and GDP growth on those of Australia, Canada and the UK. For this purpose, we use quarterly data (1961q1–2013q1) and a constant probability Markov regime switching model. We found that the US stock market volatility significantly affects the stock market volatility of all three countries at least in one of the two specified regimes over time. However, the stock market volatilities in none of the three countries are contemporaneously influenced by the US output volatility even after allowing for two distinct regimes. On the other hand, the US stock market volatility exerts significant influences on the output volatilities of both Australia and the UK. Compared with Australia and the UK, Canada and the US show substantial output volatility co-movements, thereby confirming the close association between the two neighbouring economies through the NAFTA (North American Free Trade Agreement). We conclude that shocks emanating from the US stock market have unequivocal flow-on effects on the output and return volatilities of the other economies.

Modeling dependence structure between stock market volatility and sukuk yields: A nonlinear study in the case of Saudi Arabia