nep-fmk New Economics Papers
on Financial Markets
Issue of 2016‒01‒18
six papers chosen by

  1. Pricing of Idiosyncratic Equity and Variance Risks By Elise Gourier
  2. A Semi-Markovian Modeling of Limit Order Markets By Anatoliy Swishchuk; Nelson Vadori
  3. Systemic Risk Management in Financial Networks with Credit Default Swaps By Matt V. Leduc; Sebastian Poledna; Stefan Thurner
  4. Oil price volatility and stock returns in the G7 economies By Elena María Díaz; Juan Carlos Molero; Fernando Pérez de Gracia
  5. Geography and distance effect on financial dynamics in the Chinese stock market By Xing Li; Tian Qiu; Guang Chen; Li-Xin Zhong; Xiong-Fei Jiang
  6. Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector By Jaydip Sen; Tamal Datta Chaudhuri

  1. By: Elise Gourier (Queen Mary University of London)
    Abstract: This paper decomposes the risk premia of individual stocks into contributions from systematic and idiosyncratic risks. I introduce an affine jump-diffusion model, which accounts for both the factor structure of asset returns and that of the variance of idiosyncratic returns. The estimation is performed on a time series of returns and option prices from 2006 to 2012. I find that investors not only require compensation for the systematic movements in returns and variance, but also for non hedgeable idiosyncratic risks. For the stocks of the Dow Jones, these risks account for an average of 50% and 80% of the equity and variance risk premia, respectively. I provide a categorization of sectors based on the risk profile of their Exchange Traded Funds and highlight the high prices of idiosyncratic risks in the Energy, Financial and Consumer Discretionary sectors. Other sectors are found to be appealing alternatives for investors who are not willing to be exposed to non diversifiable risks.
    Keywords: Risk premia, Idiosyncratic risk
    JEL: C38 C51 G12 G13
    Date: 2016–01
  2. By: Anatoliy Swishchuk; Nelson Vadori
    Abstract: R. Cont and A. de Larrard (SIAM J. Finan. Math, 2013) introduced a tractable stochastic model for the dynamics of a limit order book, computing various quantities of interest such as the probability of a price increase or the diffusion limit of the price process. As suggested by empirical observations, we extend their framework to 1) arbitrary distributions for book events inter-arrival times (possibly non-exponential) and 2) both the nature of a new book event and its corresponding inter-arrival time depend on the nature of the previous book event. We do so by resorting to Markov renewal processes to model the dynamics of the bid and ask queues. We keep analytical tractability via explicit expressions for the Laplace transforms of various quantities of interest. We justify and illustrate our approach by calibrating our model to the five stocks Amazon, Apple, Google, Intel and Microsoft on June 21^{st} 2012. As in R. Cont and A. de Larrard, the bid-ask spread remains constant equal to one tick, only the bid and ask queues are modeled (they are independent from each other and get reinitialized after a price change), and all orders have the same size.
    Date: 2016–01
  3. By: Matt V. Leduc; Sebastian Poledna; Stefan Thurner
    Abstract: We study insolvency cascades in an interbank system when banks are allowed to insure their loans with credit default swaps (CDS) sold by other banks. We show that, by properly shifting financial exposures from one institution to another, a CDS market can be designed to rewire the network of interbank exposures in a way that makes it more resilient to insolvency cascades. A regulator can use information about the topology of the interbank network to devise a systemic insurance surcharge that is added to the CDS spread. CDS contracts are thus effectively penalized according to how much they contribute to increasing systemic risk. CDS contracts that decrease systemic risk remain untaxed. We simulate this regulated CDS market using an agent-based model (CRISIS macro-financial model) and we demonstrate that it leads to an interbank system that is more resilient to insolvency cascades.
    Date: 2016–01
  4. By: Elena María Díaz (University of Navarra); Juan Carlos Molero (University of Navarra); Fernando Pérez de Gracia (University of Navarra)
    Abstract: This study examines the relationship between oil price volatility and stock returns in the G7 economies (Canada, France, Germany, Italy, Japan, the UK and the US) using monthly data for the period 1970 to 2014. In order to measure oil volatility we consider alternative specifications for oil prices (world, nominal and real prices). We estimate a vector autoregressive model with the following variables: interest rates, economic activity, stock returns and oil price volatility taking into account the structural break in the year 1986. We find a negative response of G7 stock markets to an increase in oil price volatility. Results also indicate that world oil price volatility is generally more significant for stock markets than the national oil price volatility.
    Keywords: stock returns, oil price volatility, G7 economies, Vector autoregressive (VAR) model
    JEL: C40 G12 Q43
    Date: 2016–01–11
  5. By: Xing Li; Tian Qiu; Guang Chen; Li-Xin Zhong; Xiong-Fei Jiang
    Abstract: Geography effect is investigated for the Chinese stock market, based on the daily data of individual stocks. Companies located around the stock markets are found to greatly contribute to the markets in the geographical sector. A geographical correlation is introduced to quantify the geography effect on the stock correlation, which is observed to approach steady as the company location moves to the northeast China. Stock distance effect is further studied, where companies are found to more likely set their headquarters close to each other. In the normal market environment, the stock correlation decays with the stock distance, but is independent of the stock distance in and after the financial crisis.
    Date: 2016–01
  6. By: Jaydip Sen; Tamal Datta Chaudhuri
    Abstract: With the rapid development and evolution of sophisticated algorithms for statistical analysis of time series data, the research community has started spending considerable effort in technical analysis of such data. Forecasting is also an area which has witnessed a paradigm shift in its approach. In this work, we have used the time series of the index values of the Auto sector in India during January 2010 to December 2015 for a deeper understanding of the behavior of its three constituent components, e.g., the Trend, the Seasonal component, and the Random component. Based on this structural analysis, we have also designed three approaches for forecasting and also computed their accuracy in prediction using suitably chosen training and test data sets. The results clearly demonstrate the accuracy of our decomposition results and efficiency of our forecasting techniques, even in presence of a dominant Random component in the time series.
    Date: 2016–01

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