nep-fmk New Economics Papers
on Financial Markets
Issue of 2016‒05‒08
six papers chosen by

  1. Financial Markets in the Face of the Apocalypse By Jedrzej Bialkowski; Ehud I. Ronn
  2. Can We Invest Based on Equity Risk Premia and Risk Factors from Multi-Factor Models? By Paweł Sakowski; Robert Ślepaczuk; Mateusz Wywiał
  3. Capital Requirements, Risk Shifting and the Mortgage Market By Uluc, Arzu; Wieladek, Tomasz
  4. Capital Pricing in Margin Periods of Risk and Repo KVA By Wujiang Lou
  5. Microstructure Invariance in U.S. Stock Market Trades By Kyle, Albert S.; Obizhaeva, Anna A.; Tuzun, Tugkan
  6. A Framework for Predictive Analysis of Stock Market Indices : A Study of the Indian Auto Sector By Jaydip Sen; Tamal Datta Chaudhuri

  1. By: Jedrzej Bialkowski (University of Canterbury); Ehud I. Ronn
    Abstract: This paper brings together two strands of the literature: Quantifying the impact of apocalyptic risk on capital markets, and the correct computation of the equity risk premium. For the former, we use events in four countries during the Second World War to discern markets' incorporation of information regarding the probability of an Armageddon for each country. We argue that past computations of the equity risk premium did not properly account for the financial implications of political collapse on property/civil/human rights. Accordingly, we show that past calculations overstated the equity risk premium. We provide an estimate of the equity risk premium that is corrected for lack of basic rights, demonstrating the important changes in this estimate over time.
    Keywords: Rare (“black swan") events; Equity premium; International political crises; property, civil, and human rights; World War II
    JEL: G12 G15
    Date: 2016–04–25
  2. By: Paweł Sakowski (Faculty of Economic Sciences, University of Warsaw); Robert Ślepaczuk (Faculty of Economic Sciences, University of Warsaw; Union Investment TFI S.A.); Mateusz Wywiał (Faculty of Economic Sciences, University of Warsaw; Quedex Derivatives Exchange)
    Abstract: We find that detailed analysis of multi-factor models makes it possible to propose investment strategies based on equity risk premium disequlibrium. We examine two investment algorithms built on weekly data of world equity indices for emerging and developed countries in the period of 2000-2015. We create seven risk factors using additional data about market capitalisation, book value, country GDP and betas of equity indices. The first strategy utilises theoretical value of equity risk premium from seven-factor Markov-switching model with variables common for all countries and variables specific to developed/emerging countries. We compare theoretical with realised equity risk premium for a given index to undertake the buy/sell decisions. The second algorithm works only on eight risk factors and applies them as input variables to Markowitz models with alternative optimisation criteria (target risk, target return, maximum Sharpe ratio, minimum variance and equally weighted assets). Finally, we notice that the impact of risk factors on final results of investment strategy is much more important than the selection of a particular econometric model in order to correctly evaluate equity risk premium.
    Keywords: investment algorithms, multi-factor models, Markov switching model, asset pricing models, equity risk premia, risk factors, Markowitz model
    JEL: C15 G11 F30 G12 G13 G14 G15
    Date: 2016
  3. By: Uluc, Arzu; Wieladek, Tomasz
    Abstract: We study the effect of changes to bank-specific capital requirements on mortgage loan supply with a new loan-level dataset containing all mortgages issued in the UK between 2005Q2 and 2007Q2. We find that a rise of a 100 basis points in capital requirements leads to a 5.4% decline in individual loan size by bank. Loans issued by competing banks rise by roughly the same amount, which is indicative of credit substitution. Borrowers with an impaired credit history (verified income) are not (most) affected. This is consistent with origination of riskier loans to grow capital by raising retained earnings. No evidence for credit substitution of non-bank finance companies is found.
    Keywords: Capital requirements; credit substitution.; loan-level data; mortgage market
    JEL: G21 G28
    Date: 2016–04
  4. By: Wujiang Lou
    Abstract: The presence of hedging errors is practically a norm of derivatives businesses. Using the unhedgeable gap risk during a margin period of risk as a starting point, this article introduces a reserve capital approach to the hedging error and its inclusion in derivatives pricing and valuation. Specifically, we define economic capital associated with the gap risk hedging error and build the cost of capital into the Black-Scholes-Merton option pricing framework. An extended partial differential equation is derived, showing terms for expected gap loss and economic capital charge, corresponding to capital valuation adjustment--KVA. For a repurchase agreement, economic capital is computed under a double-exponential jump-diffusion model, either estimated from historical data or calibrated to options smile. We find that the expected loss of a repo is very small and that cost of economic capital is the dominant component of the repo pricing spread. A repo therefore constitutes an ideal case to study economic capital and its valuation impact. The approach taken can be extended into margined OTC derivatives and more generally derivatives in incomplete markets.
    Date: 2016–04
  5. By: Kyle, Albert S.; Obizhaeva, Anna A.; Tuzun, Tugkan
    Abstract: This paper studies invariance relationships in tick-by-tick transaction data in the U.S. stock market. Over the 1993–2001 period, the estimated monthly regression coefficients of the log of trade arrival rate on the log of trading activity have an almost constant value of 0.666, strikingly close to the value of 2/3 predicted by the invariance hypothesis. Over the 2001–14 period, the estimated coefficients rise, and their average value is equal to 0.79, suggesting that the reduction in tick size in 2001 and the subsequent increase in algorithmic trading resulted in a more intense order shredding in more liquid stocks. The distributions of trade sizes, adjusted for differences in trading activity, resemble a log-normal before 2001; there is clearly visible truncation at the round-lot boundary and clustering of trades at even levels. These distributions change dramatically over the 2001–14 period with their means shifting downward. The invariance hypothesis explains about 88 percent of the cross-sectional variation in trade arrival rates and average trade sizes; additional explanatory variables include the invariance-implied measure of effective price volatility.
    Keywords: market microstructure ; transactions data ; market frictions ; trade size ; tick size ; order shredding ; clustering ; TAQ data
    JEL: G10 G23
    Date: 2016–04–19
  6. By: Jaydip Sen; Tamal Datta Chaudhuri
    Abstract: Analysis and prediction of stock market time series data has attracted considerable interest from the research community over the last decade. Rapid development and evolution of sophisticated algorithms for statistical analysis of time series data, and availability of high-performance hardware has made it possible to process and analyze high volume stock market time series data effectively, in real-time. Among many other important characteristics and behavior of such data, forecasting is an area which has witnessed considerable focus. In this work, we have used 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 five approaches for forecasting and also computed their accuracy in prediction using suitably chosen training and test data sets. Extensive results are presented to demonstrate the effectiveness of our proposed decomposition approaches of time series and the efficiency of our forecasting techniques, even in presence of a random component and a sharply changing trend component in the time-series.
    Date: 2016–04

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