nep-fmk New Economics Papers
on Financial Markets
Issue of 2013‒10‒02
four papers chosen by
Kwang Soo Cheong
Johns Hopkins University

  1. Measuring return and volatility spillovers in euro area financial markets By Dimitrios P. Louzis
  2. The Return-Volatility Relation in Commodity Futures Markets By Carl Chiarella; Boda Kang; Christina Sklibosios Nikitopoulos; Thuy-Duong To
  3. Market Index and Stock Price Direction Prediction using Machine Learning Techniques: An empirical study on the KOSPI and HSI By Yanshan Wang; In-Chan Choi
  4. Four Factor Model in Indian Equities Market By Agarwalla, Sobhesh Kumar; Jacob, Joshy; Varma, Jayanth R.

  1. By: Dimitrios P. Louzis (Bank of Greece)
    Abstract: This study examines the return (price) and volatility spillovers among the money, stock, foreign exchange and bond markets of the euro area, utilizing the forecast-error variance decomposition framework of a generalized VAR model proposed by Diebold and Yilmaz (2012) [Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 23, 57-66]. Our empirical results, based on a data set covering a twelve-year period (2000-2012), suggest a high level of total return and volatility spillover effects throughout the sample, indicating that, on average, more than the 50% of the forecast-error variance of the respective VAR model is explained by spillover effects. Moreover, the stock market is identified as the main transmitter of both return and volatility spillovers even during the current sovereign debt crisis. With the exception of the period 2011-2012, bonds of the periphery countries under financial support mechanisms are receivers of return spillovers, whereas, they transmit volatility spillovers to other markets diachronically. Finally, we identify the key role of money market in volatility transmission in the euro area during the outbreak of the global financial crisis.
    Keywords: Asset markets; Spillovers; Vector Autoregressive; Euro area; Financial Crisis.
    JEL: G01 G10 G20 C53
    Date: 2013–03
    URL: http://d.repec.org/n?u=RePEc:bog:wpaper:154&r=fmk
  2. By: Carl Chiarella (Finance Discipline Group, UTS Business School, University of Technology, Sydney); Boda Kang; Christina Sklibosios Nikitopoulos (Finance Discipline Group, UTS Business School, University of Technology, Sydney); Thuy-Duong To
    Abstract: By employing a continuous time stochastic volatility model, we analyse the dynamic relation between price returns and volatility changes in the commodity futures markets. We use an extensive daily database of gold and crude oil futures and futures options to estimate the model that is well suited to assess the return?volatility relation for the entire term structure of futures prices. Our empirical results indicate a positive relation in the gold futures market and a negative relation in the crude oil futures market, especially over periods of high volatility principally driven by market-wide shocks. However, the opposite reaction occurs over quiet volatility periods when typically commodity-specific effects dominate. As leverage effect and volatility feedback effect do not adequately explain this reaction especially for the crude oil futures, we propose the convenience yield effect. We demonstrate that commodity futures markets in normal backwardation entail a positive relation, while futures markets in contango entail a negative relation.
    Keywords: Return-volatility relation; Commodity futures returns; Gold futures volatility; Crude oil futures volatility; Contango; Backwardation
    JEL: G13 E32 Q40
    Date: 2013–08–01
    URL: http://d.repec.org/n?u=RePEc:uts:rpaper:336&r=fmk
  3. By: Yanshan Wang; In-Chan Choi
    Abstract: The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. In this paper, we propose an empirical study on the Korean and Hong Kong stock market with an integrated machine learning framework that employs Principal Component Analysis (PCA) and Support Vector Machine (SVM). We try to predict the upward or downward direction of stock market index and stock price. In the proposed framework, PCA, as a feature selection method, identifies principal components in the stock market movement and SVM, as a classifier for future stock market movement, processes them along with other economic factors in training and forecasting. We present the results of an extensive empirical study of the proposed method on the Korean composite stock price index (KOSPI) and Hangseng index (HSI), as well as the individual constituents included in the indices. In our experiment, ten years data (from January 1st, 2002 to January 1st, 2012) are collected and schemed by rolling windows to predict one-day-ahead directions. The experimental results show notably high hit ratios in predicting the movements of the individual constituents in the KOSPI and HSI. The results also varify the \textit{co-movement} effect between the Korean (Hong Kong) stock market and the American stock market.
    Date: 2013–09
    URL: http://d.repec.org/n?u=RePEc:arx:papers:1309.7119&r=fmk
  4. By: Agarwalla, Sobhesh Kumar; Jacob, Joshy; Varma, Jayanth R.
    Abstract: We compute the Fama-French and momentum factor returns for the Indian equity market for the 1993-2012 period using data from CMIE Prowess. We differ from the previous studies in several significant ways. First, we cover a greater number of firms relative to the existing studies. Second, we exclude illiquid firms to ensure that the portfolios are investible. Third, we have classified firms into small and big using more appropriate cut-off considering the distribution of firm size. Fourth, as there are several instances of vanishing of public companies in India, we have computed the returns with a correction for survival bias. During the period, the average annual return of the momentum factor was 21.2%; the average annual return on the value portfolio (HML or VMG) was 6%; that of the size factor (SMB) was -0.8%; and the average annual excess return on the market factor (Rm-Rf) was 3.5%. The daily and monthly time series of the four factor returns and the returns of the underlying portfolios are available at http://www.iimahd.ernet.in/~jrvarma/Indian-Fama-French-Momentum/ .
    URL: http://d.repec.org/n?u=RePEc:iim:iimawp:12130&r=fmk

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