scholarly journals Asymmetric Volatility in the Nepalese Stock Market

2021 ◽  
Vol 22 (1) ◽  
pp. 41-59
Author(s):  
Dinesh Gajurel
Keyword(s):  
Stock Market ◽  
Spillover Effect ◽  
Equity Markets ◽  
Spillover Effects ◽  
Stock Market Index ◽  
International Equity Markets ◽  
Asymmetric Volatility ◽  
Market Index ◽  
International Equity ◽  
Indian Stock Market

This paper investigates the asymmetric volatility behavior of the Nepalese stock market including spillover effects from the US and Indian equity markets. I modeled asymmetric volatility within a generalized autoregressive conditional heteroskdasticy framework using comprehensive data for the Nepal stock market index. The results reveal a very different asymmetry compared to the results in other international equity markets: positive shocks increase volatility by more than negative shocks. The results further suggest that uninformed investors play a significant role in the Nepalese stock market. The spillover effect from the Indian stock market to the Nepalese stock market is negative. Overall, I conclude that a “fear of missing out” (FOMO) of noise traders as well as the deployment of pump and dump schemes are inherent features of the Nepalese stock market. The findings are very useful to policy makers and investors alike.

Linkages between cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles

2016 ◽  
Vol 9 (2) ◽  
pp. 123-146 ◽  
Author(s):  
Kim Hiang Liow
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Business Cycles ◽  
Spillover Effects ◽  
Real Estate Market ◽  
Stock Market Index ◽  
Content Type ◽  
Market Index ◽  
Cross Country ◽  
The Cross

Purpose This research aims to investigate whether and to what extent the co-movements of cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles are linked across G7 from February 1990 to June 2014. Design/methodology/approach The empirical approaches include correlation analysis on Hodrick–Prescott (HP) cycles, HP cycle return spillovers effects using Diebold and Yilmaz’s (2012) spillover index methodology, as well as Croux et al.’s (2001) dynamic correlation and cohesion methodology. Findings There are fairly strong cycle-return spillover effects between the cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles. The interactions among the cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles in G7 are less positively pronounced or exhibit counter-cyclical behavior at the traditional business cycle (medium-term) frequency band when “pure” stock market cycles are considered. Research limitations/implications The research is subject to the usual limitations concerning empirical research. Practical implications This study finds that real estate is an important factor in influencing the degree and behavior of the relationship between cross-country business cycles and cross-country stock market cycles in G7. It provides important empirical insights for portfolio investors to understand and forecast the differential benefits and pitfalls of portfolio diversification in the long-, medium- and short-cycle horizons, as well as for research studying the linkages between the real economy and financial sectors. Originality/value In adding to the existing body of knowledge concerning economic globalization and financial market interdependence, this study evaluates the linkages between business cycles, stock market cycles and public real estate market cycles cross G7 and adds to the academic real estate literature. Because public real estate market is a subset of stock market, our approach is to use an original stock market index, as well as a “pure” stock market index (with the influence of real estate market removed) to offer additional empirical insights from two key complementary perspectives.

scholarly journals A Study on Affiliation between Indian Stock Market and Macro Economic Variables

2018 ◽  
Vol 5 (01) ◽  
Author(s):  
Pooja Chaturvedi Sharma
Keyword(s):  
Stock Market ◽  
Inflation Rate ◽  
Stock Market Volatility ◽  
Stock Market Index ◽  
Complex Interplay ◽  
Monthly Data ◽  
Macroeconomic Variables ◽  
Market Index ◽  
Causality Tests ◽  
Indian Stock Market

Stock market volatility is a result of complex interplay of a host of factors. Hence, it is difficult to make a correct assessment of its movement. Macroeconomic variables have are very much influential in context of the volatility of stock market. This study inspects the association amongst stock market index and selected macroeconomic variables. For the analysis unit root, co-integration, Granger causality tests and Johansen co-integration tests were performed. Outcomes of the study showed that all the variables namely money supply, exchange rate and inflation rate are positively correlated with the stock market index except gold prices. Co-integration existed between the stock market index and macroeconomic variables. The study uses monthly data of past ten years (i.e. from April 2008 to March 2018).

scholarly journals Cross-Country Composite Momentum

2021 ◽  
pp. 11-61
Author(s):  
Birgit Charlotte Müller
Keyword(s):  
Stock Market ◽  
Equity Markets ◽  
Medium Term ◽  
International Equity Markets ◽  
International Equity ◽  
Widespread Phenomenon ◽  
Asset Classes ◽  
Industry Sectors ◽  
The U.S ◽  
Over Time

ZusammenfassungMedium-term price continuation, commonly defined as momentum, is a widespread phenomenon in financial markets. It exists for individual stocks (Jegadeesh and Titman, 1993), for industry sectors (Moskowitz and Grinblatt, 1999), for style portfolios (Lewellen, 2002), in international equity markets (Rouwenhorst, 1998; Chui et al., 2010), and across asset classes (Bhojraj and Swaminathan, 2006; Menkhoff et al., 2012; Asness et al., 2013). Momentum also appears to be persistent over time, at least outside the U.S. stock market (Jegadeesh and Titman, 2001; McLean and Pontif, 2016; Green et al., 2017; Jacobs and Müller, 2020).

Volatility Spillover and Contagion Effects on Stock Markets: Global and Local Leaders Determination (Part 2)

2014 ◽  
Vol 2014 (6) ◽  
pp. 3-34
Author(s):  
Konstantin Asaturov ◽  
Tamara Teplova
Keyword(s):  
Financial Crisis ◽  
Stock Markets ◽  
Stock Market Index ◽  
International Equity Markets ◽  
Market Index ◽  
Contagion Effects ◽  
The Us ◽  
International Equity ◽  
Russian Stock Market ◽  
Global And Local

The paper presents an ARMA-DCC-GARCH model used for a quantitative analysis of dynamic linkages between 26 stock markets in three regions (Americas, Europe and Asia) over the period from 1995 to 2012. Dynamic conditional correlations between the international equity markets were tested to reveal contagion effects and its origins. It was found that the US market spreads shocks to the majority of international stock markets during the Dotcom crisis of 2000-2002 and the Financial Crisis of 2007-2009. The German, French and British markets are also proved to be contagion originators during the Financial Crisis of 2007-2009 and Eurozone Crisis of 2010-2012. The authors provide evidence that, the German and French markets transmitted a positive effect from euro currency adoption in 2002 through Europe. Concerning the linkages in Eastern and Northern European region Russia is found to be a source of contagion for the neighboring countries in time of Russian stock market index (RTSI) fall in 2008 and Banking Crisis in 2004, whereas Poland with the second biggest equity market capitalization affected much fewer countries in the area during the Financial Crisis of 2007-2009. Poland’s entry into European Union in May 2004 had no impact on interrelationships between Polish and the other stock markets.

NONLINEARITIES AND GARCH INADEQUACY FOR MODELING STOCK MARKET RETURNS: EMPIRICAL EVIDENCE FROM LATIN AMERICA

2010 ◽  
Vol 15 (5) ◽  
pp. 713-724 ◽  
Author(s):  
Claudio A. Bonilla ◽  
Rafael Romero-Meza ◽  
Carlos Maquieira
Keyword(s):  
Stock Market ◽  
Latin American ◽  
Spillover Effects ◽  
Stock Index ◽  
Output Volatility ◽  
Stock Market Index ◽  
Market Returns ◽  
Stock Market Returns ◽  
Market Index ◽  
Data Generating Process

In this paper, we analyze the adequacy of using GARCH as the data-generating process to model conditional volatility of stock market index rates-of-return series. Using the Hinich portmanteau bicorrelation test, we find that a GARCH formulation or any of its variants fail to provide an adequate characterization for the underlying process of the main Latin American stock market indices. Policymakers need to be careful when using autoregressive models for policy analysis and forecast because the inadequacy of GARCH models has strong implications for the pricing of stock index options, portfolio selection, and risk management. In particular, measures of spillover effects and output volatility may not be correct when GARCH-type models are used to evaluate economic policy.

scholarly journals Effect of Macroeconomic Determinants on Indian Stock Market

2019 ◽  
Vol 8 (2) ◽  
pp. 22-27
Author(s):  
Krishna Gadasandula
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Causality Analysis ◽  
Stock Market Index ◽  
Causality Test ◽  
Macroeconomic Variables ◽  
Bank Rate ◽  
Market Index ◽  
Long Run ◽  
Indian Stock Market

Stock market is one of the important forms of investment. The prices of stock markets are affected by much macro-economic factors. The study investigates the relationships between the Indian stock market index (NSE Nifty) and four macroeconomic variables, namely, GDP, Inflation, Exchange Rate and Bank Rate. The data is collected on a quarterly basis for the time period March 2000 to December 2017. The study employs the Johansen’s co-integration approach to the long-run equilibrium relationship between stock market index and macroeconomic variables. For causality analysis, the study carried out Granger and Geweke causality tests. From this paper it is observed that the Granger causality test results do not demonstrate the presence of any bidirectional causality. The results show the unidirectional causal associations running from GDP to Inflation, Bank Rate to GDP, Exchange Rate to GDP, NIFTY Index to GDP, Exchange Rate to Inflation, NIFTY Index to Inflation, and Bank Rate to NIFTY Index. Apart from that, the results also show no causal association between Inflation and Bank Rate, Bank Rate and Exchange Rate, and Exchange Rate and NIFTY Index. However, the bidirectional causal associations appear. When we look into the results of Geweke causality analysis shows that bidirectional causal associations exist between Inflation and Bank Rate, and Exchange Rate and Nifty Index.

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