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Risk Management in Shipping Modeling, Measuring,

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Title: Risk Management in Shipping Modeling, Measuring,


1
Risk Management in Shipping Modeling,
Measuring, Managing Freight Market Uncertainty
Presented at National Technical University of
Athens School of Naval Architecture Marine
Engineering 29 May 2003
2
Risk Management in Shipping Presentation
Outline
  • Introduction
  • About this presentation
  • About FreightMetrics
  • About Risk Management
  • Defining risk
  • The risk management process
  • Scope of risk management
  • Modern applications of risk management
  • Measuring Market Risk
  • The traditional approach to market risk
    measurement
  • The Value-at-Risk (VaR) approach

3
Risk Management in Shipping Presentation
Outline
  • Measuring Market Risk in Shipping
  • Justification for risk management in shipping
  • Market risk measurement vs. market forecasting
  • Identifying the impact of freight market risk on
    fleet cash flow
  • Developing a framework for measuring freight
    market risk
  • Measuring Market Risk in Shipping Using the
    Fr8MetricsTM Methodology
  • Main methodological features
  • How does Fr8MetricsTM work?
  • Benefits of the Fr8MetricsTM methodology
  • Potential users and managerial applications
  • Software implementation
  • Managing Freight Market Risk
  • Altering the risk profile using managerial
    decisions
  • Altering the risk profile using freight
    derivatives

4
Risk Management in Shipping Introduction
About This Presentation
  • Our objective
  • Shipping is a business activity exposed to a wide
    variety of risks.
  • In this presentation we are concerned with the
    measurement of one particular form of risk
    namely freight market risk, or the risk of loss
    arising from unexpected changes in freight rates.
  • Our motivation
  • Risk management is a notion that exists in
    financial markets for decades, having experienced
    significant technological and modeling advances
    over the years.
  • Shipping has proved rather slow in adopting
    modern risk management techniques and best
    practices from other industries.
  • Our motivation is to present a modern framework
    for measuring freight market risk, using the
    paradigm of other market-sensitive industries.

5
Risk Management in Shipping Introduction
About FreightMetrics
  • What FreightMetrics is…
  • A provider of consulting services and software
    solutions for measuring and managing freight
    market risk.
  • Working closely with Shipping Banks, Shipowners,
    and Freight Traders, in order to quantify their
    exposure to freight market risk in terms of
    cash-flow sensitivity.
  • Our approach lies in transferring best practices
    and modern methodologies from the area of
    financial risk management to shipping.
  • What FreightMetrics is NOT…
  • Shipbroker.
  • Forecasting agency.
  • Market news vendor.
  • Financial intermediary.
  • For more information about FreightMetrics, visit
    our website at www.freightmetrics.com

6
Risk Management in Shipping About Risk
Management Defining Risk
  • Definition of Risk
  • We define (financial) risk as the prospect of
    financial loss due to unforeseen changes in
    underlying risk factors. These risk factors are
    the key drivers affecting portfolio value and
    financial results. Such risk factors are equity
    prices, interest rates, exchange rates, commodity
    prices, freight rates, etc.
  • Types of Risks
  • Business The risk of loss due to unforeseen
    changes in demand, technology, competition,
    etc., affecting the fundamentals of a business
    activity.
  • Market The risk of loss arising from unexpected
    changes in market prices or market rates.
  • Credit The risk of loss arising from the
    failure of a counterparty to make a promised
    payment.
  • Operational The risk of loss arising from the
    failures of internal systems or the people who
    operate in them.
  • Other types Legal, Liquidity, etc.

7
Risk Management in Shipping About Risk
Management The Risk Management Process
  • The Risk Management process
  • There is a wide misconception amongst
    practitioners, especially within the shipping
    industry, who consider risk management as
    synonymous to hedging. This is an
    oversimplification and does not reflect the true
    dimension of risk management.
  • In fact, risk management is a process that
    involves three separate steps
  • Risk Modeling Before any attempt to take
    decisions on risk considerations, we must
    identify the underlying risk factors, understand
    their behavior, and try to model their dynamics.
    This is the basic foundation on which the other
    phases of the risk management cycle are built.
  • Risk Measurement After identifying and modeling
    the underlying risk factors, we must determine
    their significance and quantify their influence
    on portfolio value and financial results.
  • Risk Management Having identified and measured
    our risks, we are then able to take informed
    decisions on whether to reduce our exposure or
    alter our risk profile based on our risk
    preferences hedging is one such alternative
    course of action.

8
Risk Management in Shipping About Risk
Management Scope of Risk Management
  • Risk Management ? Hedging
  • As already mentioned, risk management is not
    synonymous to hedging. Hedging is just one
    alternative for the active management of risk.
  • Moreover, risk management does not necessarily
    imply risk reduction. In fact, the objective of
    risk management is NOT to reduce risk, but more
    importantly to quantify and control risk.
  • Most of the times, the objective is not to
    eliminate risk, but rather to alter our risk
    profile according to the prevailing market
    conditions, our risk preferences, and potential
    regulatory or contractual requirements.
  • Risks are embedded in any business activity. For
    a shipowner, the decision to invest in a vessel
    signifies his belief that freight rates will go
    up, earning him a return on his investment that
    is higher than the risk-free interest rate.
    However, there is no free lunch in the economy
    his decision to invest creates at the same time a
    natural exposure to freight rates, accepting the
    risk that freight rates may in fact go down.
    Risks are simply unavoidable in any profit-taking
    activity.

9
Risk Management in Shipping About Risk
Management Scope of Risk Management
  • Uncertainty vs. Variability 1
  • Variability is a phenomenon in the physical
    world to be measured, analysed and where
    appropriate explained. By contrast, uncertainty
    is an aspect of knowledge.
  • Sir David Cox
  • Risk management is only useful for the mere fact
    that we cannot predict the future. There are two
    components of our inability to be able to
    precisely predict what the future holds these
    are variability and uncertainty.
  • Variability is the effect of chance and is a
    function of the system. It is not reducible
    through either study or further measurement, but
    may be reduced through changing the physical
    system.
  • Uncertainty is the assessors lack of knowledge
    (level of ignorance) about the parameters that
    characterize the physical system that is being
    modeled. It is sometimes reducible through
    further study, or through consulting more
    experts.
  • Risk management can do very little to reduce
    variability (markets will continue to fluctuate
    no matter how advanced risk management gets), but
    can be very effective in reducing uncertainty for
    those involved in risk-taking decisions.
  • 1 Adapted from the book Risk Analysis by David
    Vose, Chapter 2 Quantitative Risk Analysis,
    Uncertainty and Variability

10
Risk Management in Shipping About Risk
Management Modern Applications of Risk
Management
  • Modern applications of Risk Management
  • Exposure measurement and reporting
  • Market risk (since early 90s)
  • Credit risk (since late 90s)
  • Operational risk (new area)
  • Economic capital estimation
  • Allocation of capital
  • Risk-based pricing
  • Risk limits
  • Risk-adjusted performance evaluation

11
Risk Management in Shipping About Risk
Management Modern Applications of Risk
Management
  • Example Risk-Adjusted Performance Evaluation
  • Consider two traders who are evaluated on the
    basis of their realized profits at some future
    date. Trader B ended up with higher profits
    compared to Trader A. Does this mean he is more
    skilled than Trader A? Does he deserve a higher
    bonus? What about the risk incurred by each
    trader through their trading strategy?

12
Risk Management in Shipping Measuring Market
Risk The Traditional Approach to Risk
Measurement
  • The Mean-Variance framework
  • Under the Mean-Variance framework, we model
    financial risk in terms of the mean and variance
    (or standard deviation, the square root of
    variance) of the Profit/Loss (PL) or the returns
    of our portfolio.
  • The Mean-Variance framework often makes the
    assumption that returns obey a normal
    distribution (strictly speaking, the
    mean-variance framework does not require
    normality, but it is easier to understand its
    statistics).
  • Portfolio Theory
  • The origin of portfolio theory can be traced back
    to the work of Markowitz (1952) which earned him
    the Nobel prize.
  • Portfolio theory starts with the premise that
    investors choose between portfolios on the basis
    of maximizing expected return for any given
    portfolio standard deviation or minimizing
    standard deviation for any given expected return.
  • One of the key insights of portfolio theory is
    that the risk of any individual asset is measured
    by the extent to which that asset contributes to
    overall portfolio risk which depends on the
    correlation of its return with the returns to the
    other assets in the portfolio (a result known as
    diversification effect).
  • Portfolio theory typically makes the assumption
    of normally distributed returns.

13
Risk Management in Shipping Measuring Market
Risk The Value-at-Risk (VaR) Approach
  • The origin and development of VaR
  • In the late 70s and 80s, a number of major
    financial institutions started working on
    internal models to measure and aggregate risks
    across the institution as a whole.
  • The best known of these models is the RiskMetrics
    model developed by JP Morgan. According to
    industry legend, this model is said to have
    originated when the chairman of JP Morgan, Dennis
    Weatherstone, asked his staff to give him a daily
    one-page report the famous 415 report
    indicating risk and potential losses over the
    next 24 hours, across the banks entire trading
    portfolio.
  • The report was ready by around 1990 and the
    measure used was Value-at-Risk (VaR), or the
    maximum likely loss over the next trading day.
    VaR was estimated from a system based on standard
    portfolio theory, using estimates of the standard
    deviations and correlations between the returns
    of different traded instruments.
  • In early 1994, JP Morgan set up the RiskMetrics
    unit to make its data and basic methodology
    available to outside parties. This bold move
    attracted a lot of attention and raised awareness
    of VaR techniques and risk management systems.
  • The subsequent adoption of VaR systems was very
    rapid, first among securities houses and
    investment banks, and then among commercial
    banks, other financial institutions and
    non-financial corporates.
  • Today, VaR is widely used in almost every
    market-sensitive industry (with the exception
    perhaps of shipping!) and has even gained
    recognition from regulatory authorities.

14
Risk Management in Shipping Measuring Market
Risk The Value-at-Risk (VaR) Approach
  • VaR in practice
  • VaR Basics
  • VaR on a portfolio is the maximum loss we might
    expect over a given holding or horizon period, at
    a given level of confidence (probability).
  • VaR is less restrictive on the choice of the
    distribution of returns and the focus is on the
    tail of that distribution the worst p percent
    of outcomes.

15
Risk Management in Shipping Measuring Market
Risk The Value-at-Risk (VaR) Approach
  • VaR in practice
  • Estimating VaR The various methodologies for
    estimating VaR actually differ on their
    particular technique for constructing the
    distribution of possible portfolio values from
    which VaR is inferred. The most common
    methodologies are
  • Analytical methods (Variance/Covariance)
  • Historical simulation
  • Monte-Carlo simulation
  • Attractions of VaR
  • VaR is a single, summary, statistical measure of
    possible portfolio losses, providing a common and
    consistent measure of risk across different
    positions and risk factors.
  • It takes account of the correlations between
    different risk factors.
  • It is fairly straightforward to understand, even
    for non-technical people.
  • VaR variants Following the same logic, other at
    risk measures have been proposed to quantify
    risk in various settings Cash Flow at Risk
    (CaR), Earnings at Risk (EaR), etc.

16
Risk Management in Shipping Measuring Market
Risk in Shipping Justification for Risk
Management in Shipping
  • Business and market risk in shipping the two
    faces of the same coin
  • Most industries can distinguish between business
    risks and market risks. These industries have to
    worry about business risks and try to hedge away
    market risks which may have an adverse
    side-effect on financial results. For example, an
    auto manufacturer has to worry about business
    risks such as technology, competition,
    production, RD, but may also have an exposure to
    FX risk, which may hamper exports, or interest
    rate exposure which may increase debt service on
    floating rate obligations.
  • Other industries cannot distinguish between
    business risks and market risks. The most
    pronounced example is maybe that of financial
    institutions. A significant part of the business
    of financial institutions is to take direct
    exposure in the worlds equity, interest rate,
    currency, and commodity markets.
  • Shipping can be said to belong to the industries
    that cannot distinguish between business risks
    and market risks. Financial results in shipping
    are directly affected by movements in the worlds
    freight rate markets.
  • Shipowners are in effect in the business of
    managing shipping risk affecting a portfolio of
    physical assets, rather than simply managing a
    fleet of vessels.

17
Risk Management in Shipping Measuring Market
Risk in Shipping Justification for Risk
Management in Shipping
  • High market volatility
  • Freight rates have historically been very
    volatile. The impact of unforeseen geo-political
    events and the slow speed of adjusting supply to
    demand have often resulted in dramatic
    fluctuations in the level of freight rates.

18
Risk Management in Shipping Measuring Market
Risk in Shipping Justification for Risk
Management in Shipping
  • Industry inefficiencies
  • Capital needs vs. sources of funds
  • Shipping is a capital intensive industry with
    significant funding needs for fleet expansion and
    replacement purposes. Yet, it has very limited
    opportunities to diversify its sources of
    funding, as most of its financing comes in the
    form of bank debt.
  • Asset Liability (mis)matching
  • Asset economic life gtgt term of debt financing
  • Variable (uncertain) revenues to meet fixed debt
    claims
  • Pro-cyclical lending practices
  • Many banks tend to be influenced by the general
    sentiment of the market and ignore the cyclical
    nature of the business. Thus, they appear more
    willing to lend when the market (and vessel
    prices) is high, despite the fact that the market
    will eventually revert back to lower levels. In
    contrast, they appear rather hesitant to extend
    credit at a period of low freight rates, although
    they are likely to rise to more sustainable
    levels.

19
Risk Management in Shipping Measuring Market
Risk in Shipping Justification for Risk
Management in Shipping
  • Lessons from the High Yield disaster of the late
    90s
  • In the late 90s, many shipping companies decided
    to tap the public debt markets with high-yield
    bonds. Historically, it was the first massive
    attempt of the industry to diversify away from
    bank debt, using an alternative source of
    external funding.
  • Unfortunately, nearly all of these high yield
    issues subsequently defaulted, mainly due to
    insufficient cash flow generation.
  • This indicated poor risk assessment and a lack of
    appropriate tools to evaluate shipping market
    risks.
  • Example

Source Moodys (2002)
20
Risk Management in Shipping Measuring Market
Risk in Shipping Justification for Risk
Management in Shipping
  • Lessons from the High Yield disaster of the late
    90s
  • Examples

Source Moodys (2002)
21
Risk Management in Shipping Measuring Market
Risk in Shipping Market Risk Measurement vs.
Market Forecasting
  • Types of maritime forecasting
  • Structural econometric models Model freight
    rates as a dependent variable, driven by a number
    of independent variables, usually representing
    macro-economic factors that influence shipping
    demand, e.g. GDP growth, oil prices, industrial
    output, etc.
  • Time series models Model freight rates using the
    structure (serial correlation) in the past
    history of the data itself. Future freight rates
    are determined based on lagged values of their
    own history and do not exploit or infer causality
    with other economic variables.
  • Difficulties in maritime forecasting
  • Demand for shipping is characterized as derived
    demand, meaning that it depends on the demand of
    the commodities that are shipped by sea.
  • Econometric models are prone to specification
    problems. Model fit can always improve by
    including more explanatory variables, which may
    introduce multicollinearity problems.
  • It is possible that a small error in demand
    estimation may lead to a gross mis-estimation of
    freight rates (compare D1?D2 vs. D2?D3).

22
Risk Management in Shipping Measuring Market
Risk in Shipping Market Risk Measurement vs.
Market Forecasting
  • Differences in scope
  • Scope Prepare for future vs. Predict the
    future
  • Motivation Prevent unexpected losses vs. Make a
    profit (beat the market)
  • Horizon Long-term vs. Short-term
  • Emphasis Tail of the distribution vs. Mean of
    the distribution
  • Differences in methodology
  • Measurement does not presuppose causality
    relations between economic variables.
  • Forecasting models have potentially infinite
    specifications (depending on choice of
    explanatory variables).
  • Measurement focuses on producing the complete
    picture of potential outcomes (entire
    distribution) rather than producing a point
    estimate (the mean of the distribution).
  • So, do we discard forecasting? NO, it can serve
    a useful complementary role, especially in
    revealing causality relations between economic
    variables. Forecasting may also assist in certain
    chartering or trading decisions in the short-run,
    where it is most effective.

23
Risk Management in Shipping Measuring Market
Risk in Shipping Impact of Freight Rate
Volatility on Cash Flow
  • Identifying the impact of freight rate volatility
    on fleet cash flow
  • Fluctuations in freight rates directly affect
    fleet cash flow.
  • Cash flow performance is the topmost concern in
    shipping.
  • Ship financing belongs to the family of Project
    Financing (other forms of project financing
    include airlines, infrastructure, real estate,
    etc.). There is a principle in project financing
    repayment MUST come from the operating cash flows
    of the financed asset.
  • So, what really matters in measuring freight
    market risk is the impact of freight rate
    variability on cash flow performance.
  • Case study
  • We performed a simple exercise (historical
    simulation) of the cash flow generation of a
    handysize dry bulker over two separate time
    periods (period A Jan-1980 to Dec-1986, and
    period B Jan-1987 to Dec-1993).
  • We used the same set of assumptions in both cases
    (see the following slide), except for using the
    actual freight rates for each period and the
    actual second-hand value of the financed vessel
    at the start of the each period.
  • This exercise not only illustrates the impact of
    freight rate volatility on cash flow, but also
    emphasizes the impact of shipping cycles and the
    importance of proper timing in maritime
    decision-making.

24
Risk Management in Shipping Measuring Market
Risk in Shipping Impact of Freight Rate
Volatility on Cash Flow
  • Case study impact of freight rate volatility on
    fleet cash flow
  • Historical simulation assumptions and actual
    freight rate data (source Clarksons)

25
Risk Management in Shipping Measuring Market
Risk in Shipping Impact of Freight Rate
Volatility on Cash Flow
  • Case study impact of freight rate volatility on
    fleet cash flow
  • Chart of monthly net cash flow (after debt
    service, but excluding balloon payment)

26
Risk Management in Shipping Measuring Market
Risk in Shipping Impact of Freight Rate
Volatility on Cash Flow
  • Case study impact of freight rate volatility on
    fleet cash flow
  • Chart of accumulated liquidity (excluding balloon
    payment)

27
Risk Management in Shipping Measuring Market
Risk in Shipping A Framework for Measuring
Freight Market Risk
  • Basic Assumptions and Objectives
  • It is possible to develop a framework for
    measuring freight market risk using the VaR
    paradigm.
  • The risk factors in this framework consist of
    freight rates.
  • Freight rates are assumed to follow a random walk
    and are modeled using appropriate stochastic
    processes.
  • The stochastic processes that describe the
    evolution of freight rates are able to replicate
    certain known characteristics of freight rate
    dynamics (cyclicality, seasonality, random
    shocks).
  • Monte-Carlo simulation is used to generate future
    freight rate scenarios, in accordance with the
    underlying stochastic process for each risk
    factor.
  • The key measure of risk is fleet cash flow.
  • For each freight rate scenario, we re-compute
    future cash flow, using an appropriate cash flow
    model which takes into account debt repayment and
    other cost items (e.g. drydocking costs, special
    surveys, etc.).
  • Thus we construct the entire distribution of
    future cash flow, from which we can make VaR-type
    inferences based on a specified confidence level.

28
Risk Management in Shipping Measuring Market
Risk in Shipping A Framework for Measuring
Freight Market Risk
  • Modeling the stochastic behavior of freight rates
  • Any time series data can be thought of as being
    generated by a particular stochastic or random
    process, the true data-generating process
    (DGP). A concrete set of data, such as a
    historical data-series on a freight rate, can be
    regarded as a particular realization (i.e. a
    sample) of the underlying true DGP. The
    distinction between the stochastic process and
    its realization is akin to the distinction
    between population and sample in cross-sectional
    data. Just as we use sample data to draw
    inferences about a population, in time series we
    use the realization to draw inferences about the
    underlying stochastic process.

29
Risk Management in Shipping Measuring Market
Risk in Shipping A Framework for Measuring
Freight Market Risk
  • Selecting a stochastic process
  • The modeling of any price variable begins with
    the choice of a particular stochastic process
    which captures the characteristics of asset price
    dynamics. In order to make this selection, we are
    guided by theoretical considerations, such as the
    theory concerning the operation of freight
    equilibrating mechanisms, as well as by empirical
    analysis of historical data (e.g. mean reversion,
    fat tails, autocorrelation, volatility
    clustering, etc.)
  • There is a large number of alternative stochastic
    processes that can be tested to capture the
    dynamics of freight rates. Below we provide a few
    indicative (simplistic) models
  • Geometric Brownian Motion (GBM)
  • Ornstein-Uhlenbeck (O-U) process
  • Jump-Diffusion (O-U with Jumps)

30
Risk Management in Shipping Measuring Market
Risk in Shipping A Framework for Measuring
Freight Market Risk
  • Estimating model parameters
  • As discussed previously, a concrete set of
    historical data can be regarded as a particular
    realization (i.e. a sample) of the underlying
    true DGP. The objective is to find a
    theoretical DGP that provides the best fit for
    the actual data. This is accomplished by
    estimating the parameters of each theoretical DGP
    and comparing the various models in terms of some
    measure of goodness-of-fit.
  • Many stochastic processes admit exact
    discretization or numerical approximations (using
    the Euler or higher-order methods) which allow
    the testing of the underlying processes using
    standard econometric techniques. Both the GBM and
    O-U processes admit such discretizations which
    lead to time-series specifications of a linear
    autoregressive form.
  • For example, the GBM model can be estimated by
    running the following regression
  • The parameters of an O-U process can be estimated
    using discrete-time data by running the
    regression
  • and then calculating
  • More advanced models require other techniques,
    e.g. Maximum Likelihood Estimation

31
Risk Management in Shipping Measuring Market
Risk in Shipping A Framework for Measuring
Freight Market Risk
  • Simulating the stochastic evolution of freight
    rates
  • Having discretized the stochastic process and
    estimated its parameters, we proceed with
    iterative sampling from the probability
    distribution(s) used in our model, in order to
    generate future freight rate scenarios.
  • This technique is known as Monte-Carlo simulation
    and includes several steps
  • Random number generation (pseudo-random or
    low-discrepancy numbers)
  • Transformation of independent random number into
    correlated random numbers
  • Variance reduction methods (to improve the
    accuracy for a given number of runs)
  • Example As discussed in the previous slide, the
    O-U process can be interpreted as the
    continuous-time version of a first-order
    autoregressive process in discrete time.
    Specifically, the O-U process is the limiting
    case as ?t ? 0 of the following AR(1) process
  • where e(t) is normally distributed with mean
    zero and standard deviation se
  • Thus, we can simulate an O-U process, by drawing
    random numbers from a normal distribution with
    mean zero and standard deviation se and
    generating r(t) as follows

32
Risk Management in Shipping Measuring Market
Risk in Shipping A Framework for Measuring
Freight Market Risk
  • Building the distribution of future fleet cash
    flows
  • For each freight rate scenario produced by our
    simulation, we re-compute the fleet cash flow
    based on some cash flow model and plot the
    results in a histogram. This represents the
    distribution of future fleet cash flows.
  • Making risk inferences from the distribution of
    future fleet cash flows
  • The distribution of cash flow results reveals the
    risk profile of the fleet, in terms of the range
    of possible cash flows that the fleet is able to
    generate in the future.
  • We can read this distribution in order to make
    probabilistic inferences about the risk of our
    fleet. For example
  • What is the probability that the fleet will
    breakeven?
  • What is the maximum possible cash deficit at the
    95 probability level?

33
Risk Management in Shipping Measuring Market
Risk in Shipping A Framework for Measuring
Freight Market Risk
  • A practical example
  • Assuming a simple O-U process, we modeled the
    1-year Time-Charter rate for dry bulk handysize
    vessels and simulated 1000 different scenarios.
    Below we compare the distribution of actual
    monthly returns (282 observations from Feb-76 to
    Jul-99) with the distribution of simulated
    (random) monthly returns. From this we can
    compute the cash flow of the vessel and produce
    the distribution of possible cash flows for next
    month.

34
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM Main Methodological
Features
  • Main methodological features
  • Fr8Metrics is a framework for quantifying
    freight market risk in shipping portfolios.
    Fr8Metrics is generally based on the
    Value-at-Risk (VaR) concept, but differs in the
    use of proprietary stochastic models developed to
    simulate the evolution of freight rates. These
    models are designed to replicate the unique
    seasonal and cyclical characteristics of shipping
    markets.
  • The Fr8Metrics methodology is simulation-based
    rather than forecast-based. It draws on advanced
    Monte-Carlo simulation techniques to generate
    future freight market scenarios from which we
    estimate the likely distribution of various
    financial measures, such as cash flow,
    accumulated liquidity, hull cover, NPV, etc.
  • Fr8Metrics is able to incorporate the influence
    of correlations, not only across different market
    segments within the shipping industry, but also
    between shipping and financial markets. Thus, it
    is possible to capture potential diversification
    effects within a portfolio that combines both
    shipping and financial assets.
  • Fr8Metrics is able to support portfolios that
    combine both physical assets (vessels) and
    paper assets (derivatives).

35
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM How Does Fr8MetricsTM
Work?
  • Step 1 Portfolio Definition
  • Input details for the fleet (charter agreements,
    cost data, etc.), loans (repayment schedules,
    interest cost, collateral vessels, etc.), and
    derivatives.
  • Step 2 Risk Mapping
  • Assign risk factors to vessels and derivatives.
  • Step 3 Project Definition
  • Select the portfolio(s) which will be simulated.
  • Specify cash flow model.
  • Specify risk metric.
  • Specify simulation parameters (number of
    scenarios, horizon, confidence level, etc.)

36
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM How Does Fr8MetricsTM
Work?
  • Step 4 Scenario Generation
  • Generate n (number of scenarios specified in
    Step 3) future realizations for each risk factor
    for the time horizon specified in Step 3, in
    accordance with the underlying stochastic process
    of each risk factor

37
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM How Does Fr8MetricsTM
Work?
  • Step 5 Metric Computation
  • Iteratively substitute values from each of the n
    scenarios from Step 4 into the cash flow model
    specified in Step 3, calculate the n future cash
    flow results, and plot them in a histogram

38
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM How Does Fr8MetricsTM
Work?
  • Step 6 Risk Inference
  • Using the distribution of cash flow results from
    Step 5, find the cash flow estimate corresponding
    to the desired confidence level specified in Step
    3.
  • Having exposed the complete risk profile of the
    portfolio(s) specified in Step 3, the user
    (banker, shipowner, etc.) is able to take
    calculated, risk-informed decisions in accordance
    with his risk preferences

39
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM Benefits of the
Fr8MetricsTM Methodology
  • General benefits of Monte-Carlo based methods
  • Flexibility to support a wide range of stochastic
    processes.
  • Not restrictive in terms of distributional
    assumptions.
  • Ability to incorporate correlations among risk
    factors.
  • Ability to incorporate decision rules along the
    simulated paths (e.g. exercise of charter
    options).
  • Particular benefits of the Fr8MetricsTM
    methodology
  • Utilizes stochastic models specifically developed
    to capture freight rate dynamics.
  • Reveals diversification effects across shipping
    assets, as well as between shipping and financial
    exposures.
  • Provides a framework for monitoring derivatives,
    developing hedging strategies and assessing hedge
    effectiveness.

40
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM Potential Users and
Managerial Applications
  • Shipping Banks
  • Determining credit terms maximum advance ratio,
    liquidity covenant, loan spread
  • Risk assessment repayment risk, probability of
    covenant breach
  • Estimating default probabilities, verifying
    internal risk ratings
  • Promoting cross-selling, derivatives sales, hedge
    proposals
  • Shipowners
  • Investment decisions, e.g. dry bulk vs. tanker
    segments
  • Chartering decisions, e.g. time charter vs. spot
    employment
  • Financing decisions, e.g. high-yield bond vs.
    bank debt
  • Hedging decisions, e.g. derivatives vs. long-term
    charter
  • Freight Traders
  • Risk assessment and monitoring
  • Risk-adjusted performance evaluation

41
Risk Management in Shipping Measuring Market
Risk with Fr8MetricsTM Software Implementation
  • Product features
  • Hierarchical portfolios
  • Multi-currency environment
  • Periodic updates of parameter estimates for
    underlying stochastic models
  • 3 cash flow model formats (Fleet, GAAP, Sources
    and Uses)
  • User-defined cash flow items
  • Generation of pro-forma cash flow statements
  • Technology
  • Windows-based
  • Developed in .NET environment
  • Extensive use of XML
  • Databases SQL / Access

42
Risk Management in Shipping Managing Freight
Market Risk Altering the Risk Profile with
Managerial Decisions
  • Risk-informed decision-making
  • As mentioned previously, the objective of risk
    management is not necessarily to eliminate risk,
    but rather to alter our risk profile according to
    the prevailing market conditions, our risk
    preferences, and potential regulatory or
    contractual requirements.
  • Having exposed the complete risk profile of a
    shipping portfolio within a VaR framework, we are
    able to decide whether it suits our risk
    preferences or to make comparisons among
    alternative business strategies.

Choice of strategy is subject to risk
preference (Strategy B higher expected return,
but higher risk)
Strategy A is dominant (Strategy A higher
expected return AND lower risk)
43
Risk Management in Shipping Managing Freight
Market Risk Altering the Risk Profile with
Managerial Decisions
  • Asset Allocation decisions
  • Expand the fleet in the dry cargo or the tanker
    segment?
  • Buy one VLCC or two Aframaxes?
  • Buy one 5-year old vessel or two 15-year old
    vessels?
  • Order a newbuilding in Korea (cheaper, but FX
    risk) or in the US?
  • Chartering decisions
  • Trade in the spot market or lock in a 3-year time
    charter at a rate that is currently- lower than
    the spot rate?
  • Accept a high time charter rate or a lower time
    charter rate with an option to renew?
  • Charter-in or charter-out for the next one year?
  • Funding decisions
  • Finance new acquisitions through bank debt or
    high yield issue?
  • Go for a 5-year loan with low spread or a 7-year
    loan with higher spread?

44
Risk Management in Shipping Managing Freight
Market Risk Altering the Risk Profile with
Freight Derivatives
  • Definition of derivatives
  • In chemistry, a derivative is a substance
    related structurally to another substance and
    theoretically derivable from it (...) a substance
    that can be made from another substance.1
    Derivatives in finance work on the same
    principle. They are financial instruments whose
    promised payoffs are derived from the value of
    something else, generally called the underlying.
  • 1 This definition comes from the online version
    of the Merriam-Webster Collegiate Dictionary. See
    http//www.britannica.com/cgi-bin/dict?vaderivati
    ve
  • Types of freight derivatives
  • Forward Freight Agreements (FFAs) An agreement
    between two counterparties to settle a freight
    rate for a specified quantity of cargo or type of
    vessel, for a certain route, and at a certain
    date in the future.
  • The underlying asset of the FFA contracts can be
    any of the routes that constitute the indices
    produced by the Baltic Exchange.
  • FFAs are settled in cash on the difference
    between the contract price and an appropriate
    settlement price at expiration.
  • To establish an FFA, we need to specify route,
    price, duration/quantity, settlement
  • Other types of derivatives Options, Swaps,
    Swaptions, etc.

45
Risk Management in Shipping Managing Freight
Market Risk Altering the Risk Profile with
Freight Derivatives
  • The market of freight derivatives
  • Historical development of freight derivatives
  • Freight derivatives existed since 1985 with the
    creation of the BFI (Baltic Freight Index), a
    basket of individual dry cargo routes. This index
    served as a settlement mechanism for freight
    futures listed on BIFFEX (subsequently merged
    with LIFFE, contracts de-listed in April 2002).
  • Since 1992, the individual shipping routes could
    be traded over the counter (i.e. not through an
    exchange) in the form of FFAs.
  • Current market size and players
  • Estimated annual turnover 4.0 billion in
    notional value of freight.
  • Types of players Shipowners, Charterers, Trading
    Houses, Shipbrokers
  • The role of the Baltic Exchange
  • Sets the rules and oversees the process of
    collecting and processing the brokers
    assessments of freight rates in more than 30
    cargo routes. These prices are used for the
    settlement of FFA transactions.

46
Risk Management in Shipping Managing Freight
Market Risk Altering the Risk Profile with
Freight Derivatives
  • Fundamentals of freight derivatives trading
  • Trading process
  • Price discovery through brokers
  • FFA negotiation
  • Counterparty clearance
  • Documentation
  • Basis risk (sources correlation, time lag)
  • Marking-to-Market
  • Designing a hedging program
  • Understand the distribution / dynamics of freight
    rates.
  • Estimate the impact of adverse freight rate
    movements on fleet cash flow.
  • Decide whether to hedge, depending on external
    and internal considerations.
  • Choose the appropriate financial instruments.
  • Determine how much to hedge.

47
Risk Management in Shipping References,
Links, and Further Reading
  • References
  • Adland, Roar (June 2000), Theoretical Vessel
    Valuation and Asset Play in Bulk Shipping, Thesis
    submitted for the MS in Ocean Systems Management,
    MIT
  • Attikouris, Kyriakos (April 2000), Modeling
    Freight Rates, Thesis submitted for the Diploma
    in Mathematical Finance, University of Oxford
  • Attikouris, Kyriakos (March 1996), Time Series
    Applications in the Ocean Shipping Business,
    Project submitted for the course Applied Time
    Series Analysis (MBA program), University of
    Rochester
  • Concalves, Franklin de Oliveira (September 1992),
    Optimal Chartering and Investment Policies for
    Bulk Shipping, Thesis submitted for the PhD in
    Ocean Systems Management, MIT
  • Dowd, Kevin (2002), Measuring Market Risk, Wiley
  • Drewry Shipping Consultants (1997), Shipping
    Futures and Derivatives, Briefing Report
  • Moodys Investor Services (2002), Default
    Recovery Rates of European Corporate Bond
    Issuers, 1985-2001
  • Stopford, Martin (1997), Maritime Economics,
    Routledge
  • Vose, David (2000), Risk Analysis, Wiley
  • Wilmott, Paul (1998), Derivatives, Wiley
  • Magazines Risk, Marine Money, Lloyds Shipping
    Economist
  • Seminar notes Freight Derivatives seminar,
    organized by the Cambridge Academy of Transport
    and the Baltic Exchange (25 November 2002)
  • Links
  • www.riskmetrics.com RiskMetrics Group
  • www.gloriamundi.org GloriaMundi (the best
    internet source on VaR material)
  • www.balticexchange.com The Baltic Exchange
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