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A widely accepted belief indicates that terror activities have negative impact on stock markets. Contrary to numerous empirical studies, the purpose of this paper is to consider this issue from another point of view in the sense that markets can become desensitized to terror.

Here, instead of directly analyzing the existing data, the stochastic nature of the events is taken into consideration.

The author compares three countries and found out that the correlation between terror and stock markets is almost nil when terror events become a commonplace.

This paper applies mean reverting stochastic processes to terror incidents and brings out interesting results.

The purpose of this paper is to develop a theoretical model of a jump diffusion-mean reversion constant proportion portfolio insurance strategy under the presence of transaction cost and stochastic floor as opposed to the deterministic floor used in the previous literatures.

The paper adopts Merton’s jump diffusion (JD) model to simulate the price path followed by risky assets and the CIR mean reversion model to simulate the path followed by the short-term interest rate. The floor of the CPPI strategy is linked to the stochastic process driving the value of a fixed income instrument whose yield follows the CIR mean reversion model. The developed model is benchmarked against CNX-NIFTY 50 and is back tested during the extreme regimes in the Indian market using the scenario-based Monte Carlo simulation technique.

Back testing the algorithm using Monte Carlo simulation across the crisis and recovery phases of the 2008 recession regime revealed that the portfolio performs better than the risky markets during the crisis by hedging the downside risk effectively and performs better than the fixed income instruments during the growth phase by leveraging on the upside potential. This makes it a value-enhancing proposition for the risk-averse investors.

The study modifies the CPPI algorithm by re-defining the floor of the algorithm to be a stochastic mean reverting process which is guided by the movement of the short-term interest rate in the economy. This development is more relevant for two reasons: first, the short-term interest rate changes with time, and hence the constant yield during each rebalancing steps is not practically feasible; second, the historical literatures have revealed that the short-term interest rate tends to move opposite to that of the equity market. Thereby, during the bear run the floor will increase at a higher rate, whereas the growth of the floor will stagnate during the bull phase which aids the model to capitalize on the upward potential during the growth phase and to cut down on the exposure during the crisis phase.

The purpose of this paper is to investigate how to determine optimal investing stopping time in a stochastic environment, such as with stochastic returns, stochastic interest rate and stochastic expected growth rate.

Transformation method was used for solving optimal stopping problem by providing a way to transform path‐dependent problem into a path‐independent one. Based on option pricing theory, optimal investing stopping time was thought of as an optimal executed timing problem of American‐style option.

First, the authors transform a path‐dependent stop timing problem to a path‐independent one with transformation under very general conditions, to directly use the existing conclusion of optimal stopping time literature. Second, when dynamics of capital growth is homogeneous, the authors changed the two dimensional optimal stop timing problem into a single dimension problem based on the assumption of zero exercise costs. Third, the authors investigated the comparative dynamics about asset selling boundary on asset value, state variable and return predictability. With constant discount rate and growth rate, the optimal selling timing depends on the simple comparison between capital cost and growth rate.

The paper's contributions to analysis method may be as follows. The authors demonstrate how to transform a path‐dependent stopping problem into a path‐independent one under general conditions. The transform method in this article can be applied to other path‐dependent optimal stopping problems. In particular, a Riccati ordinary differential equation for the transformation is set up. In most examples commonly met in finance, the equation can be solved explicitly.

The authors formulate India’s energy targets in light of pushing for renewable energy sources and reducing the dependence on imported coal. Share of imported coal in electricity generation has been approximately 10 per cent in recent years. While investments in renewables have grown in recent years as seen in installed capacities, coal-fired electricity generation has grown because of rising demand for electricity. The purpose of this study is to find a planner solution when high global coal prices force greater investments in renewable energies.

The authors use real options approach where global coal prices are the stochastic variable. They present an optimal stopping problem and solving the problem backward, the revenues from continuing with the current energy generation mix and those from replacing imported coal with wind and solar is compared for each period.

The “trigger price” for global coal prices when it is optimal for the social planner to invest in additional wind and solar capacities is found. Trigger prices is the threshold when investment must be undertaken whatever be the future evolution of coal prices; this gives the problem a value of waiting. India cannot afford to wait to invest if faced with strict short-term goals.

The work evaluates India’s domestic targets and its Paris Agreement goals in light of using more of wind and sun and replacing imported coal. Various data sources (government reports, research articles) are consulted to predict shares of electricity from various sources in future and the authors find the operating costs and the investment costs associated with switching to renewables.

More than 80 percent of S&P 500 firms that issue ESOs use the Black-Scholes-Merton (BSM) model and substitute the estimated average term for the contractual expiration to calculate ESO expense. This simplification systematically overprices ESOs, which worsens as the stock's volatility increases. The purpose of this paper is to present a modification of the BSM model to explicitly incorporate the rates of forfeiture pre- and post-vesting and the rate of early exercise.

The paper demonstrates the model's usefulness by employing historical exercise and forfeiture data for 127 separate ESO grants and 1.31 billion ESOs to calculate the exercise and forfeiture parameters and value ESOs for nine firms.

The modified BSM model is just as accurate but easier to use than the more computationally intensive utility maximization and trinomial lattice models, and it avoids the ASC 718 BSM model's overpricing bias.

If firms prefer the BSM model over more mathematically elegant alternatives, they should at least use a BSM model that is free of overpricing bias.

This paper, based on case-studies with five universal banks from Europe and North America, aims to investigate which types of comprehensive risk measure (CRM) models are being used in the industry, the challenges being faced in implementation and how they are being currently rectified. Undoubtedly, CRM remains the most challenging and ambiguous measure applied to the correlation trading book. The turmoil surrounding the new regulatory framework boils down to the Basel Committee implementing a range of capital charges for market risk to promote “safer” banking in times of financial crisis. This report discusses current issues faced by global banks when complying with the complex set of financial rules imposed by Basel 2.5.

The current research project is based on in-depth, semi-structured interviews with five universal banks to explore the strides major banks are taking to introduce CRM modelling while complying with the new regulatory requirements.

There are three measures introduced by the Basel Committee to serve as capital charges for market risk: incremental risk charge; stressed value at risk and CRM. All of these regulatory-driven measures have met with strong criticism for their cumbersome nature and extremely high capital charges. Furthermore, with banks facing imminent implementation deadlines, all challenges surrounding CRM must be rectified. This paper provides some practical insights into how banks are finalising the new methodologies to comply with Basel 2.5.

The introduction of CRM and regulatory approval of new internal market risk models under Basel 2.5 has exerted strong pressure on global banks. The issues and computational challenges surrounding the implementation of CRM methodologies are currently fiercely debated among the affected banks. With little guidance from regulators, it remains very unclear how to implement, calculate and validate CRM in practice. To this end, a need for a study that sheds some light on practices with developing and computing CRM emerged. On submitting this paper to the journal, we have received news that JP Morgan is to pay four regulators $920 million as a result of a CRM-related scandal.

Group incentive schemes have been shown to be positively associated with firm performance but it remains an open question whether this association can be explained by the motivating characteristics of the group-incentive scheme itself, or if this is due to factors that tend to accompany group-incentive schemes. We use a controlled experiment to directly test if group-incentive schemes can motivate sustained individual effort in the absence of rules, norms, and institutions that are known to mitigate free-riding behavior.

We use a controlled lab experiment that randomly assigns subjects to one of three compensation contracts used to incentivize an onerous effort task. Two of the compensation contracts are group-incentive schemes where subjects have an incentive to free-ride on the efforts of their coworkers, and the third (control) is a flat-wage contract.

We find that both group-incentive schemes resulted in sustained, higher performance relative to the flat-wage compensation contract. Further, we do not find evidence of free-riding behavior under the two group-incentive schemes.

Although we do find sustained cooperation/performance over the three work periods of our experiment under the group-incentive schemes, further testing would be required to evaluate whether group-incentive schemes can sustain cooperation over a longer time horizon without complementary norms, policies, or institutions that mitigate free-riding.

By unambiguously showing that group-incentive schemes can, by themselves, motivate workers to provide sustained levels of effort, this suggests that the “1/n problem” may be, in part, an artifact of the rational-actor modeling conventions.

This paper reviews the literature which models lease covenants using option‐pricing techniques, probabilistic measures of risk and the contractual misalignment of incentives. These quantitative models, in conjunction with conventional discounting mathematics, offer ways to gauge the effects on rent of changes to many lease clauses. With the exception of discounted cash flow analysis to adjust rents for leasing incentives, none appears to be used in practice yet. The program has been designed to bridge the gap between academic developments in this field and current practices in rental valuation. The program works from rental values set on benchmark or standard lease conditions in that market and adjusts for different clauses. The program displays all the stages in calculating the effects of each changed clause and operates entirely from parameters set by the user. Trials of this program are described.

Liquidity is an important aspect of market efficiency. The purpose of this paper is threefold: first, this paper aims to discuss indicators that provide information about liquidity in agricultural land markets. Second, this paper aims to reflect on determinants of market liquidity and analyze the relationship with land prices. Third, this paper aims to conduct an empirical analysis for Germany that illustrates these concepts and allows hypothesis testing.

This study reviews liquidity dimensions and measurement in financial markets and derives indicators applicable to farmland markets. In an empirical analysis, this study exhibits the spatial and temporal variability of land market liquidity in Lower Saxony, a German federal state with the highest agricultural production value. This study uses a rich dataset that includes 72,547 sale transactions of arable land between 1990 and 2018. The research focuses on volume-based (number of transactions, volume and turnover) and time-based (trading frequency and durations) measures. A panel vector autoregression and Granger causality tests are applied to investigate the relation between land turnover and land prices.

The paper confirms the thinness of farmland markets but also reveals regional and temporal heterogeneity of land market liquidity. This study finds that the relation between market liquidity and prices is ambiguous. This study concludes that a high demand from expanding farms absorbs supply shocks regardless of the current price level in agricultural land markets.

Even though the relevance of agricultural land markets’ thinness is widely acknowledged in the literature, this paper is one of the first attempts to measure liquidity in agricultural land markets and to explain its relationship with land prices.