The symmetrization of diffusion processes was originally introduced by Imamura, Ishigaki and Okumura, and was applied to pricing of barrier options. The authors of the present paper previously introduced in Ida et al. (Pac J Math Ind 10:1, 2018) a hyperbolic version of the symmetrization of a diffusion by symmetrizing drift coefficient in view of applications under a SABR model which is transformed to a hyperbolic Brownian motion with drift. In the present paper, in order to apply the hyperbolic symmetrization technique to Heston model, we introduce an extension where diffusion coefficient is also symmetrized. Some numerical results are also presented.
The study aims at analysing whether the earnings are managed in the banking industry in India considering the provisioning standards issued by the RBI. The study also examines the presence of capital management and signalling practices by Indian Banks through the usage of Provision for Non-Performing Assets (PNPA). The study comprises of 84 banks in India which includes nationalised banks, private banks and foreign banks focusing on financial data from FY 2005–2016. The study uses panel data regression model for exploring the presence of earnings management, capital management and signalling. The dependent variable considered is PNPA and the independent variables are lag of dependent variable, return on assets, capital adequacy ratio, and change in operating profit. We have also included certain control variables viz. credit deposit ratio, total assets, closing gross NPA, GDP, real interest rates. The results of our study indicates income smoothing practices by Indian Banks. However, the results do not prove the presence of capital management or signalling practices by Indian Banks through the usage of provision for NPA.
This work develops and estimates a three-factor term structure model with explicit sentiment factors in a period including the global financial crisis, where market confidence was said to erode considerably. It utilizes a large text data of real time, relatively high-frequency market news and takes account of the difficulties in incorporating market sentiment into the models. To the best of our knowledge, this is the first attempt to use this category of data in term-structure models. Although market sentiment or market confidence is often regarded as an important driver of asset markets, it is not explicitly incorporated in traditional empirical factor models for daily yield curve data because they are unobservable. To overcome this problem, we use a text mining approach to generate observable variables which are driven by otherwise unobservable sentiment factors. Then, applying the Monte Carlo filter as a filtering method in a state space Bayesian filtering approach, we estimate the dynamic stochastic structure of these latent factors from observable variables driven by these latent variables. As a result, the three-factor model with text mining is able to distinguish (1) a spread-steepening factor which is driven by pessimists’ view and explaining the spreads related to ultra-long term yields from (2) a spread-flattening factor which is driven by optimists’ view and influencing the long and medium term spreads. Also, the three-factor model with text mining has better fitting to the observed yields than the model without text mining. Moreover, we collect market participants’ views about specific spreads in the term structure and find that the movement of the identified sentiment factors are consistent with the market participants’ views, and thus market sentiment.
Empirical test of asset-pricing models are typically performed on portfolios based on firm-characteristics such as size and book-to-market ratios etc. However, because of their strong factor structure, the characteristic sorted portfolios do not provide a sufficient test for asset pricing models. In recent, the appropriateness to use characteristics sorted portfolios has been debated. Literature suggests various alternative test portfolios sorted by other attributes to improve the empirical tests. To address this issue, we construct three sets of test portfolios sorted by firm beta, volatility, and clustering method to test various asset pricing models. We examine whether portfolios sorted by the above methods can improve the explanatory power of various alternative asset pricing models. Our test results suggest that for unconditional models, the statistical significance and estimated risk premiums depend on the choice of tests portfolios. The conditional model has more power to explain the variation of average returns than the unconditional model.
We demonstrate that the use of asymptotic expansion as prior knowledge in the “deep BSDE solver”, which is a deep learning method for high dimensional BSDEs proposed by Weinan et al. (Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations, 2017b. arXiv:1706.04702 ), drastically reduces the loss function and accelerates the speed of convergence. We illustrate the technique and its implications by using Bergman’s model with different lending and borrowing rates as a typical model for FVA as well as a class of solvable BSDEs with quadratic growth drivers. We also present an extension of the deep BSDE solver for reflected BSDEs representing American option prices.
In this paper we consider a discrete-time formulation of dynamic transaction cost problems. We examine applicability of numerical discrete probability approximation as an alternative simplistic approach to solve dynamic transaction cost problems. We provide a computational study of a lattice-based heuristic method on simple transaction cost models and highlight its many advantages. The solution of these problems provides a dynamic investor with important insights as to how the portfolio should be re-balanced when faced with transaction costs.
This paper studies the predictive ability of corridor implied volatility (CIV) measure. It is motivated by the fact that CIV is measured with better precision and reliability than the model-free implied volatility due to the lack of liquid options in the tails of the risk-neutral distribution. By adding CIV measures to the modified GARCH specifications, the out-of-sample predictive ability of CIV is measured by the forecast accuracy of conditional volatility. It finds that the narrowest CIV measure, covering about 10% of the RND, dominate the 1-day ahead conditional volatility forecasts regardless of the choice of GARCH models in high volatile period as market moves to non volatile periods, the optimal width broadens. For multi-day ahead forecasts narrow and mid-range CIV measures are favoured in the full sample and high volatile period for all forecast horizons, depending on which loss functions are used whereas in non turbulent markets, certain mid-range CIV measures are favoured, for rare instances, wide CIV measures dominate the performance. Regarding the comparisons between best performed CIV measures and two benchmark measures (market volatility index and at-the-money BlackScholes implied volatility), it shows that under the EGARCH framework, none of the benchmark measures are found to outperform best performed CIV measures, whereas under the GARCH and NAGARCH models, best performed CIV measures are outperformed by benchmark measures for certain instances.
This paper aims to study the dynamics of corporate bond yield spread in India, and attempted to identify the possible determinants: bonds' liquidity, credit quality and therefore their yield spreads. A large sample of daily corporate bond trade data over a period of 6 years (20112016), classified into Issuers Segment-wise and Rating-wise, are analyzed within a basic statistical framework and using panel regression model. Default risk, as captured by the credit rating, is found to significantly affect the yield spread, for all types of securities. Even if the summary statistics and panel regression results broadly support the relationship between bond liquidity, captured through various bond characteristics and trade statistics, and yield spread, use of better liquidity proxy measure may improve the said relationship. Movements in equity market also affect corporate bond yield spread in India.
This paper investigates the impact of non-managerial and managerial blockholders on the value of the firms listed in the Alternative Investment Market (AIM). This study mainly investigates whether the effect of blockholders on firm value is due to the AIM high ownership concentration and low investor protection. The primary empirical finding, using GMM, justifies that non-managerial and managerial blockholders in the AIM affect the firm value in different ways. Non-managerial blockholders in the AIM improve the firm value by monitoring managers when their block sizes are up to 32%. However, when their block sizes exceed 32%, the blockholders expropriate other shareholders.
This study finds a necessary and sufficient condition for mutual fund separation, in which investors have the same portfolio of risky assets regardless of their utility functions. Unlike previous studies, the market condition is obtained in analytic form, using the ClarkOcone formula of Ocone and Karatzas (Stoch Stoch Rep 34(34):187220, 1991). We also find that the condition for separation among arbitrary utility functions is equivalent to the condition for separation among utility functions with constant relative risk aversion (CRRA utility functions). The condition is that a conditional expectation of an infinitesimal change in the uncertainty of an instantaneous Sharpe ratio maximizing portfolio can be hedged by the Sharpe ratio maximizer itself. In a Markovian market, such an infinitesimal change is characterized as an infinitesimal change in state variables. A closer look at the ClarkOcone formula offers an intuition of the condition: an investor invests in (1) the Sharpe ratio maximizer and (2) another portfolio in such a way as to reduce the uncertainty produced by an infinitesimal change in the Sharpe ratio maximizer, depending on four components: the investor's wealth level, marginal utility and risk tolerance, at the time of consumption, and the shadow price. This decomposition is also valid in non-Markovian markets.