Financial Quantities Research Articles

Numerical approximation of a hybrid Poisson-jump Ait-Sahalia-type interest rate model with delay

. While the original Ait-Sahalia interest rate model has been found to be of considerable use for describing the time-series evolution of interest rates, it may not possess adequate specifications to explain the responses of interest rates to empirical phenomena such as volatility skew-smile effect, jumps, market regulatory lapses, economic crises, financial clashes, and political instability, among others collectively. In this article, we propose a modified version of this model by incorporating additional features to help collectively describe these empirical phenomena adequately. Since the proposed model does not have a closed-form formula, we construct new techniques of the truncated EM method to study it and justify the method within a Monte Carlo framework to compute some financial quantities.

Green Accounting and Financial Performance of Quoted Manufacturing Companies in Nigeria

The study investigated green accounting activities and its implementations by manufacturing companies in Nigeria in relating to their financial performance. Qualitative research was adopted due to the fact that most of the Nigerian manufacturing companies do not attach financial quantities to their reports on sustainability activities. A sample of fifteen (15) manufacturing companies were investigated- (Dangote Floor mill, Dangote Cement, Cadbury,) The study used data from the central bank statistical bulletin as well as Nigeria Stock Exchange, and data were analyzed in the Statistical Package for Social Sciences (SPSS) Version 22. As well as Microsoft Excel The research hypotheses were tested using Ordinary Lease Square Regression (OLSR) analysis at a significance level of .05. The results of the findings were that there is no positive linear relationship between sustainability accounting of manufacturing companies and their profitability in Rivers state. This study recommends among others that as green accounting and sustainability reporting is still in arithmetic progression in Nigeria, more efforts should be applied in this regard, doing this will see a future positive correlative effect on the profitability of the manufacturing companies in Nigeria

A novel approach to legacy donations with long‐term benefits supported by mathematical analysis

AbstractA novel approach to legacy donations, called the “Master Fund Strategy,” is proposed. Potential long‐term financial benefits for both donor and nonprofit organizations (NPOs) when compared to a “Traditional Fund Strategy” are established through mathematical analysis and computer simulations, providing nonprofit marketing and fundraising professionals an alternative way to lock in bequest funding. In particular, formulas are developed for computing relevant financial quantities associated with the two strategies. Conditions are presented under which the Master Fund Strategy is better than the Traditional Fund Strategy, in the sense that there is a point in time when the net present value of the distributions to the NPO under the Master Fund Strategy exceeds that of a Traditional Fund Strategy and continues to do so beyond that point. These analytical results are obtained under the assumption that the investment rates of return and the fund payouts rates are known constants; however, formulas for relaxing these restrictions are also developed and the consequences are examined with Monte Carlo simulations.

Strong convergence rate of implicit Euler scheme to a CIR model with delay

Cox-Ingersoll-Ross (CIR) process is widely used to model financial quantities. It has been revealed that for some financial problems such as stock prices, the current state depends on the past behavior. In this paper, we study a CIR model with delay in the drift coefficient, and the delay term is super-linear. We give the existence and positiveness to the delayed CIR model, and show that the implicit Euler scheme converges strongly to the exact solution with rate 1/2.

Betting against beta with intraday and overnight signals

The abnormal returns of the Betting Against Beta (BAB) strategy have attracted much interest among researchers and practitioners. Based on a market anomaly related to the Capital Asset Pricing Model, this strategy uses daily beta as a signal for portfolio construction. However, recent literature shows how some financial quantities, including beta, change between trading and non-trading periods. For this reason, we decided to compare the performance of the original BAB strategy with two BAB variants, where the signal for portfolio construction is given by intraday and overnight beta, respectively. Despite all strategies exhibiting positive cumulative returns, using the intraday beta signal leads to significantly higher performances. Further analyses show that the abnormal intraday BAB returns are mainly due to nano and micro-cap stocks which tend to outperform large-cap stocks, as well known from the literature.

Introduction of Financial Settlement Among LFC Areas

Abstract Settlement of unintended deviation between scheduled and realized energy exchange among LFC (Load Frequency Control) areas used to be conducted through the energy compensation programs, where energy deviation from period of registration is compensated at a later stage period of compensation of unintended deviation. With the adoption of the Synchronous Area Framework Agreement (SAFA), the Regional Group for Continental Europe the methodology of accounting and financial settlement of unintended deviations FSKAR (Financial settlement kΔf, ACE and ramping period) is defined. The FSKAR methodology defines necessary data for the calculation and determination of unintended deviation. The methodology defines the method of determining settlement prices. The paper describes the methodology of determining settlement prices, the method of data collection and validation, the method of calculation of deviations and other energy and financial quantities, and data exchange between entities.

PHYSICAL AND FINANCIAL PERFORMANCE OF PUBLIC, PRIVATE AND FOREIGN BANKS IN INDIA

Banks are the fundamental components of the financial system and are also active players in the financial markets. Therefore an efficient banking system is needed in mobilizing the savings and channeling them to productive purposes for the development of any economy. This paper attempts to analyze and compare the physical and financial performance of three banking sectors: Public, Private and Foreign banks using physical and financial quantities of inputs and comparing the revenue maximization of banks during 2009-10 to 2018-19.

Robust estimation of time-dependent precision matrix with application to the cryptocurrency market

Most financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, when addressing asset pricing, portfolio selection, and investment strategies, accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context. In this regard, fundamental tools that increasingly attract research interests are precision matrix and graphical models, which are able to obtain insights into the joint evolution of financial quantities. In this paper, we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series. Furthermore, we provide an algorithm to handle parameter estimations that uses the “maximization–minimization” approach. We apply the methodology to synthetic data to test its performances. Then, we consider the cryptocurrency market as a real data application, given its remarkable suitability for the proposed method because of its volatile and unregulated nature.

Financial Stability Control for Business Sustainability: A Case Study from Food Production

Conventional financial management methods, based on extrapolation approaches to financial analysis, often reach their limits due to violations of stationary controlled financial variables, for example, interventions in the economy and social life necessary to manage the COVID-19 pandemic. Therefore, we have created a procedure for controlling financial quantities, which respects the non-stationarity of the controlled quantity using the maximum control deviation covering the confidence interval of a random variable or random vector. For this interval, we then determined the algebraic criteria of the transfer functions using the Laplace transform. For the Laplace transform, we determined the theorem on the values of the stable roots of the characteristic equation, including the deductive proof. This theorem is directly usable for determining the stability of the management for selected financial variables. For the practical application, we used the consistency of the stable roots of the characteristic equation with the Stodola and Hurwitz stability conditions. We demonstrated the procedure for selected quantities of financial management in food production. In conclusion, we proposed a control mechanism for the convergence of regulatory deviation using a combination of proportional and integration schemes. We also determined the diversification of action interventions (into development, production, and marketing) using a factorial design.

Improved Dividend Estimation from Intraday Quotes.

Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may reduce the quality of the estimates. In this paper, we study the impacts on estimation quality when using intraday data to estimate dividends. The methodology is based on earlier linear regression (ordinary least squares) estimates, which have been adapted to intraday data. Further, the method is also generalized in two aspects. First, the dividends are expressed as present values of future dividends rather than dividend yields. Second, to account for heteroscedasticity, the estimation methodology was formulated as a weighted least squares, where the weights are determined from the market data. This method is compared with a traditional method on out-of-sample S&P 500 European options market data. The results show that estimations based on intraday data have, with statistical significance, a higher quality than the corresponding single-times estimates. Additionally, the two generalizations of the methodology are shown to improve the estimation quality further.

Transmission mechanism of monetary policy in India - An Expost Study

The transmission mechanism of monetary policy is correlated to lending which expands aggregate demand in the economy. The banking system form an important place in the monetary policy transmission. The channels of monetary policy transmission are: Interest Rate Channel, Credit or Loan Supply Channel, Exchange Rate Channel, and Asset Price Channel. The financial prices include interest rates, exchange rates, yields, asset prices, and equity prices, and the financial quantities consists of money supply, credit aggregates, supply of government bonds and foreign denominated assets. RBI uses various monetary policy frameworks over the years which is classified as pre-monetary targeting (1947 to 1984-85), monetary targeting (1984-85 to 1997- 98), Multiple Indicators approach (1998-99 to 2014), Inflation Targeting (from 2013 onwards), and Flexible Inflation Targeting (FIT). MCLR also drastically decreased for all these banks from 2019 to 2021. The external benchmark has increased for the three types of banks from 2019 to 2021. The repo rate gives a contrast data between pre and post FIT periods. The financial system is not fully developed which is a hindrance to the transmission mechanism. The cost of lending, issues in bond market, and pandemic situation related problems requires an appropriate transmission mechanism in the monetary policy of our country.

On the effectiveness of the European Central Bank’s conventional and unconventional policies under uncertainty

In this paper, we investigate the effectiveness of conventional and unconventional monetary policy measures by the European Central Bank (ECB) conditional on the prevailing level of uncertainty. To obtain exogenous variation in central bank policy, we rely on high-frequency surprises in financial market data for the euro area (EA) around policy announcement dates. We trace the dynamic effects of shocks to the short-term policy rate, forward guidance and quantitative easing on several key macroeconomic and financial quantities alongside survey-based measures of expectations. For this purpose, we propose a Bayesian smooth-transition vector autoregression (ST-VAR), using a measure of economic policy uncertainty as signal variable. Our results suggest that transmission channels are impaired when uncertainty is elevated. While conventional monetary policy and forward guidance can be less effective during such periods, quantitative easing measures seem to work comparatively well in uncertain times.

Testing a parameter restriction on the boundary for the g-and-h distribution: a simulated approach

We develop a likelihood-ratio test for discriminating between the g-and-h and the g distribution, which is a special case of the former obtained when the parameter h is equal to zero. The g distribution is a shifted lognormal, and is therefore suitable for modeling economic and financial quantities. The g-and-h is a more flexible distribution, capable of fitting highly skewed and/or leptokurtic data, but is computationally much more demanding. Accordingly, in practical applications the test is a valuable tool for resolving the tractability-flexibility trade-off between the two distributions. Since the classical result for the asymptotic distribution of the test is not valid in this setup, we derive the null distribution via simulation. Further Monte Carlo experiments allow us to estimate the power function and to perform a comparison with a similar test proposed by Xu and Genton (Comput Stat Data Anal 91:78–91, 2015). Finally, the practical relevance of the test is illustrated by two risk management applications dealing with operational and actuarial losses.

Learning Bermudans

American and Bermudan-type financial instruments are often priced with specific Monte Carlo techniques whose efficiency critically depends on the effective dimensionality of the problem and the available computational power. In our work we focus on Bermudan Swaptions, well-known interest rate derivatives embedded in callable debt instruments or traded in the OTC market for hedging or speculation purposes, and we adopt an original pricing approach based on Supervised Learning (SL) algorithms. In particular, we link the price of a Bermudan Swaption to its natural hedges, i.e. the underlying European Swaptions, and other sound financial quantities through SL non-parametric regressions. We test different algorithms, from linear models to decision tree-based models and Artificial Neural Networks (ANN), analyzing their predictive performances. All the SL algorithms result to be reliable and fast, allowing to overcome the computational bottleneck of standard Monte Carlo simulations; the best performing algorithms for our problem result to be Ridge, ANN and Gradient Boosted Regression Tree. Moreover, using feature importance techniques, we are able to rank the most important driving factors of a Bermudan Swaption price, confirming that the value of the maximum underlying European Swaption is the prevailing feature.

Variance Swaps with Deterministic and Stochastic Correlations

As market observations say that many financial quantities are correlated in a time dependent, nonlinear or unpredictable way, in this study, we present an approach to price discretely sampled variance swaps based on the Heston model extended by incorporating deterministic or stochastic correlation between an underlying asset and its variance. We obtain a closed form exact formula for the fair delivery prices under the deterministic correlation model and an affine approximation formula under the stochastic correlation model. A comparison with Monte–Carlo simulations supports the validity of the pricing formulas. Based on the analytic results, we find that the fair delivery price increases as time to maturity or leverage effect increases or sampling frequency decreases. On the other hand, the impact scale of the correlation volatility is so imperceptible that the time dependent deterministic correlation model can still be a good proxy of the stochastic correlation environment in the case of variance swap pricing, while the approximation formula with the stochastic correlation is better than the exact formula with the deterministic correlation in computing sense.

Evaluation and designing reverse logistics for risk-neutral and risk-seeking decision makers

Designing appropriate supply chain would provide numerous valuable feedbacks for the whole chain, since using returned products instead of reproducing them, is a more appropriate response to the environmental concerns on the one hand which provides benefit and financial savings for the chains on the other hand. Therefore, this paper presents a three-objective function mathematical model to maximise financial savings and quantities of returned products to the chain and minimise total costs in terms of uncertainty and risk that derives from reverse logistics nature. Finally, the developed model was solved by Monte Carlo simulation and genetic algorithm along with proper risk measures for risk-neutral and risk-seeking decision makers. The results indicated financial savings are one of the best objective functions in order to show superiority of reverse logistics network. As another result, it was pointed out that profitability of the chain increases because of delivering return products before their scrap-life.

Generalized Ait-Sahalia-type interest rate model with Poisson jumps and convergence of the numerical approximation

In this paper, we consider a generalized Ait-Sahalia interest rate model with Poisson jumps in finance. The analytical properties including positivity, boundedness and pathwise asymptotic estimations of the solution to this model are investigated. Moreover, we prove that the Euler–Maruyama (EM) numerical solution converges to the true solution of the model in probability. Finally, we apply the EM solution to compute some financial quantities. A numerical example is provided to demonstrate the effectiveness of our results.

Option Pricing With Regime Switching Correlation: A Numerical PDE Approach

Modelling correlation between financial quantities is important in the accurate pricing of financial derivatives. In this paper, we introduce some stochasticity in correlation, by considering a regime-switching correlation model, in which the transition rates between regimes are given. We present a derivation of the associated Partial Differential Equation (PDE) problem. The problem involves a system of l PDEs, where l is the number of regimes. We formulate a finite difference method for the solution of the PDE system, and numerically demonstrate that it converges with second order. We study the effect of certain model parameters on the computed prices. We compare prices from this model, associated PDE and method with those from a stochastic correlation model, associated PDE and method in van Emmerich, 2006, Leung, 2017, Leung et al., 2016 and discuss advantages and disadvantages.

QUANTO PRICING IN STOCHASTIC CORRELATION MODELS

Correlation plays an important role in pricing multi-asset options. In this work we incorporate stochastic correlation into pricing quanto options which is one special and important type of multi-asset option. Motivated by the market observations that the correlations between financial quantities behave like a stochastic process, instead of using a constant correlation, we allow the asset price process and the exchange rate process to be stochastically correlated with a parameter which is driven either by an Ornstein–Uhlenbeck process or a bounded Jacobi process. We derive an exact quanto option pricing formula in the stochastic correlation model of the Ornstein–Uhlenbeck process and a highly accurate approximated pricing formula in the stochastic correlation model of the bounded Jacobi process, where correlation risk has been hedged. The comparison between prices using our pricing formula and the Monte-Carlo method are provided.

Pre-averaging estimate of high dimensional integrated covariance matrix with noisy and asynchronous high-frequency data

With rapid development of the global market, the number of financial securities has significantly grown, which greatly challenges the measuring of financial quantities. Among others, the estimation of covariance matrix which plays an important role in risk management becomes no longer accurate. In this paper, we consider the estimation of integrated covariance matrix of semi-martingales under framework of high dimension by using high frequency data. We assume that the multivariate asset prices are observed asynchronously and all the observed prices are contaminated by microstructure noise. We employ the pre-averaging method to remove the microstructure noise and the generalized synchronization method to deal with the non-synchronicity. Moreover, to avoid the inconsistency in the high-dimensional covariance matrix estimation, we propose a regularized estimate. The consistency under matrix [Formula: see text]-norm is established. Compared to existing results, our estimator improves the accuracy of the estimation. Finally, we assess the theoretical results via some simulation studies.